31 Dec 2020 by Mark

Year in Review: 2020

Summary: A copy of my end of year message to my lab, where I review the key events from 2020.

As 2020 draws to a close, I think we can all say that this year took on quite a unique flavor compared to last year in every sort of way. That said, there are many things that remained the same, including the drive and commitment that all of you have shown under such exceptional circumstances, given both my own paternity leave at the beginning of the year and then later when dealing with the pandemic and its remote work consequences. I am grateful to have had such a wonderful and gifted group of collaborators to help weather the collective storm that was 2020. Despite these difficulties, we have had some genuinely bright spots throughout this year to celebrate, which I will try to highlight below for you, as well as some key things to look forward to in 2021.

The People

As I have mentioned before, the best part of the lab for me is all the wonderful people I get to collaborate with and call our collective lab family. This year marked an important year of renewal for the lab, as we had several departures and additions that formed part of the natural ebb and flow of academic research labs. I must admit that I am still getting used to that process, as I find it difficult to see good friends and colleagues move onto new things, but it is also exciting to bring in new people with fresh approaches and ideas. I missed doing many of our typical retreats and lab activities this year, on account of pandemic restrictions, but am looking forward to starting those up again safely in 2021.

First, over the summer Rachel Hess graduated with her M.S. thesis entitled “Automatic Optimization Methods for Patient-Specific Tissue-Engineered Vascular Grafts.” Rachel worked with Axel Krieger and I (along with our collaborators at UChicago and Children’s National Hospital) for our Design + ML related NIH project on designing 3D electrospun grafts for children born with congenital heart disease. She has since joined the FAA nearby working in their trajectory optimization group. Second, both Dr. Charlie Manion and Dr. David Anderson finished their contracts with UMD over the summer and are on to new endeavors (including a large round of angel investor funding for David’s design automation startup). I will miss all of our great conversations and explorations that we had as part of the DARPA Fundamental Design program, which has given me at least 5-10 years of new proposal and paper ideas from all the places we got to explore together. Third, Dr. Xiaolong Liu has transitioned to a Research Scientist role at Johns Hopkins, though we are still collaborating on our active NIH grants and Xiaolong was pivotal in securing a follow-on grant coming in 2021 that I cannot publically announce yet, but I am looking forward to working on it with him.

In terms of additions, this year we were joined by three excellent Ph.D. students, Eesh Kamrah, Qiuyi Chen, and Xuliang Dong, who will be supporting several NSF and ARPA-E projects, as well as an M.S. student, Richard Moglen, who will be looking at joint work in Soft Robotics with post-doc Fatemeh Ghoreishi, and PIs Ryan Sochol and Axel Krieger. I have already benefited from many of the great skills, perspectives, and energy that they have brought with them into the lab; especially given that their “first-year graduate experience” during a pandemic is unlike anything others have had to go through. Truly impressed!

The Science

This year we made a bunch of progress across many scientific areas, with almost too many applications to exhaustively mention here. Some of these were closing the loop on existing projects from 2019, though many also represented new endeavors that we’ll see bear more fruit in 2021 and beyond. I’ll highlight a few common threads below that provide exemplars of this trajectory, though I think you can get a broader sense of our entire research portfolio by checking out our papers page or Google Scholar. For example:

  • Wei spearheaded the publication of a culmination of his work on Design Manifolds via an AIAA Journal paper finally published this year called “Airfoil Design Parameterization and Optimization Using Bézier Generative Adversarial Networks” as well as the ArXiV preprint for “Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization.” These two papers brought together years of work that he has done in his thesis on the usefulness of low dimensional “Design Manifolds” for exploration and optimization. In his most recent work, we show how design manifolds can accelerate airfoil optimization methods 2-3x that of State of the Art, and up to 10x that of popular methods. It laid the foundation for work in two of our new ARPA-E projects, so I hope to share more about how we built upon it in the coming year. As of writing, the code for that paper and the underlying BezierGAN architecture code are currently our most popular repositories on our lab’s GitHub. So I’m really excited to see where we and others can take some of these ideas, as it represents the real culmination of the work we did under the DARPA Young Faculty Award program mentored by Jan Vandebrande.
  • Faez finalized several publications that came out of our joint work with collaborators Scarlett Miller and Sam Hunter at Penn State on learning and optimizing metrics for design variety and creativity measurement, such as “Design Variety Measurement Using Sharma–Mittal Entropy” and “How should we measure creativity in engineering design? A Comparison of social science and engineering approaches”. The types of problems we explored in those papers, as well as his earlier work, had been gnawing at my brain ever since I was a graduate student and so it felt nice to finally get our hands around generalizing some of those concepts in a more unified way. This led to ongoing work that I hope to see come to fruition in 2021 or 2022, so stay tuned.
  • Jun and Nicholas continued putting out work that came out of the DARPA Fundamental Design program, such as on the Learning to Abstract and Compose problem. We explored so many interesting directions in that program and have more papers in the pipeline to release in 2021.
  • Dan’s 2019 review paper on “Deep learning for molecular design” in Molecular Systems Design & Engineering by the Royal Society of Chemistry continues to gain traction in the growing ML + Materials Design community, so I am glad that it is remaining relevant in such as rapidly evolving area.
  • Faez’s continued collaborations with Saba Ahmadi, John Dickerson, Samir Khuller and I on various applications of optimal diverse matching has produced multiple new avenues such as diverse team selection and multi-attribute matching. This formed some of the foundations for one of our new ARPA-E collaborations that John and I now have underway.
  • Mark, along with Co-PI’s Ken Kiger, Miao Yu, David Bigio, and Steve Michell received a Provost Teaching Innovation Grant over the summer to help re-tool our department and college’s teaching tools by integrating and deploying a system called PrarieLearn (developed out of UIUC) for mastery-based teaching. It turns out that this grant was actually the largest one that the MechE department received out of the Provost’s initiative, to my surprise. A number of you in the lab helped get this off the ground over the summer, including David Anderson, Charlie Manion, and Nicholas Chiu, so thank you all again for your help on this. It ended up being widely used for over 200+ students in the Fall semester with more coming in the spring.

While this was just a brief snapshot of some of the outcomes this year – and is by no means exhaustive – I wanted to use it as an opportunity to show you all how the work you are doing today really does lay the groundwork for yourselves and future students to build upon. Much of what we do and celebrate today would not be possible without seeds that students planted many years ago and dutifully tended until today; so you can see your own trajectory as forming the life-blood of the lab itself.

I also wanted to remind everyone that all of the above would not have been possible were it not for the support of our various research sponsors that, at various parts of 2020, included seven projects across DARPA (via the FUN DESIGN and YFA programs), NSF (via the EDSE program), the NIH (via the NHLBI), and ARPA-E (via the DIFFERENTIATE program). So I do thank you all for your many efforts – big and small – to help deliver on the promises we make to these sponsors and the missions they support.

The Field

There continues to be broad interest in the intersection of Machine Learning and Mechanical Design/Optimization. For example, while 2019 saw a Journal of Mechanical Design Special Issue on “Machine Learning in Engineering Design”, JMD also recently announced another SI in “Artificial Intelligence and Engineering Design” which includes ML as a subset. I have also noticed beyond our own lab both a growth in the number of ML + Design related publications in our major conferences, and, perhaps more importantly, more nuanced application, critique, and contributions of those articles beyond what I had seen even five years ago. I think this speaks to a growing set of researchers who’ll be putting forward great work in these areas in years to come, and I am excited by this growth and interest. As always, if you hear of any up-and-coming researchers that you think I should add to my reading lists, let me know since I’d like to support and keep abreast of the best work in this growing area.

On the downside, the COVID-19 pandemic pushed back our plans for an in-person 2020 NSF Workshop/Summer School on Emerging Mathematical Foundations for Engineering Design, however, we’re gearing up to offer this in 2021 and investigating methods to do it in a safe and engaging way, so I look forward to sharing updates with folks on that as the world and our plans evolve out of this pandemic.

Funding wise, this year saw the launch of ARPA-E’s DIFFERENTIATE Program, which is targeted at using ML to improve the design of energy products. Our lab is involved in 3 of the 23 funded efforts, which spanned a dizzying array of application areas from what I could tell at the kickoff meeting. I was really impressed by the range of ML + Design applications that various industrial, academic, and national lab partners were involved in, and this gives me great hope that there is a real commercial and societal impact to be made by research investments in this area. I think what this means to you as lab members are (1) that, if done rigorously and on important problems, your research can have a substantial real-world impact; and (2) that I expect the employment opportunities for folks with our types of skills at this intersection will remain strong at least over the next decade or so. I’ve always enjoyed the academic research angle of ML+Design type questions (thus why I still run an academic lab), but it has become abundantly clear to me now that industrial partners, national labs, and startups are also active and growing in this space. What a great time to be working in this type of research! Look out next year since we have some new grants/projects coming in from sponsors like ARL, ONR, and two others that are yet to be publicly announced, so I am excited about what those possibilities will enable for us as well.

Alumni News

Occasionally, I’ll get some updates from past lab alumni that I like to share with the lab family each year. Specifically, Faez officially kicked off his lab at MIT this fall, so we all wish him well at a strong start there. Wei has been leveraging some of his research skills at deploying new internal projects and tools at Siemens, so it is nice to hear that our work remains industrially relevant. Zois is settling into American University and is helping spearhead the initiation of a new Ph.D. program there, so that has been an exciting opportunity for him to shape the future of his department and university. Thanks to all the alumni who occasionally report back on their changes over the year so that I can share many of your updates with those who follow in your footsteps.

Thanks everyone again for what you have been able to accomplish in spite of a difficult 2020, and I look forward to what 2021 has in store for us!

31 Dec 2019 by Mark

Year in Review: 2019

Summary: A copy of my end of year message to my lab, where I review the key events from 2019.

As I detail further below, this past year was both incredibly satisfying as well as bittersweet in a number of ways. The work you have all done this year has made this year one of my most memorable and restorative. I truly count myself so fortunate to work every day with such a hard-working, creative group of people who continually push me and each other to perform at our best. This year was different than last year in the sense that we were able to harvest many of the great efforts you all have spent years building up to. Likewise, this has led to many new seed ideas and endeavours that I am excited to grow and explore in the years ahead!

The People

Working with all of you is the best part of my job, hands down, and this year we had several important departures from the lab that make this year surprisingly bittersweet to me. Specifically, in August of this year, three of our lab members moved onwards to new stages in their careers.

First, Dr. Zois Boukovalas, a post-doc in the lab who many of you interacted with, accepted an offer as an Assistant Professor in the Department of Mathematics and Statistics at American University (AU). Zois was one of the first post-docs I hired, alongside Dr. Dan Elton, and so to see Zois secure his top choice institution while remaining nearby enough to continue our collaborations, is a win-win for both of us! Second, in early August (now Dr.) Wei Chen defended his dissertation entitled “Data-Driven Geometric Design Space Exploration and Design Synthesis” and walked in the December 2019 graduation ceremony. After defending in August, Wei started as a Research Scientist at Seimens Corporate Technology within the Generative Design group. I am looking forward to continuing to collaborate with Wei and his colleagues at Seimens as the years move forward. Third, a few weeks after Wei in August, (now Dr.) Faez Ahmed defended his dissertation entitled “Diversity and Novelty: Measurement, Learning and Optimization” and walked in the December 2019 graduation ceremony. Faez has accepted a job as an Assistant Professor in the Mechanical Engineering department at the Massachusetts Institute of Technology (MIT) starting in Fall 2020, and will be taking a year to broaden his research horizons by taking a post-doc with Dr. Wei Chen at Northwestern University.

Faez Ahmed, Wei Chen, and Mark Fuge at UMD December 2019 Graduation.

Having both Wei and Faez leave this fall has been particularly bittersweet for me because they were the first PhD students who I recruited and hired into the department. They effectively helped me launch the lab into the state it is in today, and many of you reading this have interacted with them in some capacity. Many, if not all, of the grants we have received in the past few years have been directly built upon the foundation of papers and research Wei and Faez did during their time here. I will always be grateful for what their hard work has done for my own career and the rest of the lab. I look foward to being their friend and colleague in the years ahead!

In terms of additions, this year our lab welcomed two excellent Post-Docs, Dr. Xiaolong Liu and Dr. Fatemeh Ghoreishi, both of whom are exploring different aspects of medical robotics design and optimization for Aortic Grafts and Soft Catheters, respectively. I am excited to explore with them all the interesting ways design and ML can help advance the medical device field! Glancing into 2020 a little bit, we’ll be welcoming Sangeeth Balakrishnan as a new Ph.D. student in the Spring who I am co-advising with Peter Chung on the intersection of Geometric Deep Learning and Materials Design.

The Science

Once again this year, your scientific efforts have led to our lab’s most productive year to-date, by almost every measure I can think of. Beyond simple metrics, however, what really impressed me about your work this year was the breadth of contributions across a number of scientific areas that I think speaks to how well each of you is able to connect our common expertise to a set of diverse applications. Just to highlight a few (non-exhaustive) examples of what I mean by this, we saw:

And there were many other exciting papers you all worked on that I could have mentioned here, though my point is just to highlight how enriching it is to my own personal satisfication and intellectual curiousity that we can apply our work in design, machine learning, and applied mathematics to such a range of venues and problems. You are the reason my job continues to be so interesting!

The Field

This year continued to be a banner year in terms of academic, government, and industry interest in the intersection of Machine Learning and Mechanical Design/Optimization. As just one example, this year the Journal of Mechanical Design, ASME’s flagship design journal, had its Special Issue on “Machine Learning in Engineering Design.” I co-guest edited this SI with the wonderful Jitesh Panchal, Ying Liu, Sammy Missoum, and Conrad Tucker and all of them did a simply tremendous job given the enormous number of high quality submissions there were on this topic. If I recall correctly, the SI even spanned two volumes in the journal! It was really exciting to see such interesting work and a large investment by the academic community in this area. For a summary of the SI, you can check out the short editorial that we put together.

Also this year, ARPA-E’s DIFFERENTIATE Program launched, which is a $30M funding program focusing on new AI/ML tools that can improve energy product design to enable things like all-electric aircraft, new photovoltaics, efficient power grid convertors, and so forth to help address growing energy and climate concerns. You can read more about it from ARPA-E’s press release. I remember in 2018 presenting at the program workshop many of the results that Wei, Faez, Dan, Zois, Charlie, and Nicholas had worked throughout 2017 and 2018, and much of that work ended up supporting UMD’s grant applications to the program. In total, UMD secured around 3 of the 23 awards (13%) and I am excited to see how these new investments can propel our work forward in the years to come. Such a thing would not have been possible were it not for the great effort you all put in!

Alumni News

Occasionally, I’ll get some updates from past lab alumni, and I was fortunate to see Dr. Kailyn Cage on campus several months ago during a trip over from Amazon Lab 126. She’s enjoying her time and work at Amazon and spoke of how well regarded UMD is at places like Amazon and Apple, particularly among those who focus on product relability. She runs into fellow Terps often! Thanks for visiting Kailyn :-)

Thanks everyone again for a great 2019, and I’m looking forward to what we can accomplish together in 2020 and the decade to come!

Also, I’m including some fun pictures below from the Lab Picnic and Kayaking trip over the summer on the Anacostia River:

Lab eating some Ethopian food for lunch.

Charlie, Zois, Maria, and Fatemeh kayaking on the water.

Group photo at the lab picnic.

Frequently Asked Questions by Prospective Ph.D. Students

Summary: I answer several frequently asked questions that prospective Ph.D. students have about what it is like to work in my lab or at UMD. Hopefully this helps Ph.D. students better decide if UMD is a good fit for them and their professional goals.

When recruiting and interviewing graduate students for admission into UMD, I often end the preliminary interview by asking students if they have any questions for me, since I know how difficult deciding on graduate schools can be. Over the years, I have seen that many students have a subset of similar questions that often come up when they are considering whether UMD is a good fit for them. I have written up some answers to the most common questions I get to help address them ahead of time for prospective students and also help shed some light on these things when students are considering applying here.

About My Lab Group

Where do your students end up? What kind of jobs can I get after working with you? Are your students successful?

This is largely driven by the interests of the students, since some students are interested in more industry-type research jobs, while others seek an academic position or something in a national government lab. As of last time I updated this, I’ll list below where my past students or post-docs ended up, organized by whether or not they wanted/preferred an industrial, academic, or government job. You can decide for yourself whether or not the types of jobs they secured align with the type of jobs you are interested in for yourself, with the caveat that a student’s personal preference and motivation is the key determinant in where they end up. Note that this list includes only the job that they interviewed for and got right after working with me, which I figure is the most relevant to you as a prospective graduate student, and thus their current positions may differ from what is below depending on how much time has elapsed since they left the lab.

Student/Post-Doc wanted Academic Position

  • Dr. Faez Ahmed (PhD ‘19) - Asst. Professor, Mechanical Engineering, Massachusetts Institute of Technology
  • Dr. Zois Boukouvalas (Post-Doc, ‘19) - Asst. Professor, Mathematics and Statistics, American University

Student/Post-Doc wanted Industrial Position

  • Dr. Wei Chen (PhD ‘19) - Research Scientist, Generative Design Group, Seimens Corporate Technology
  • Dr. Kailyn Cage (PhD ‘18) - Technical Program Manager, Amazon Lab126
  • Mr. Ceena Modarres (MS ‘16) - Data Scientist, Capital One Financial Corporation

Student/Post-Doc wanted Government Position

  • Dr. Dan Elton (Post-Doc, ‘18) - Staff Scientist, National Institutes of Health

How many students do you have? What kind of backgrounds do they have?

This changes year-to-year given the natural ebb and flow of how many students join or graduate each year, but, in general, we have had between 6-12 FTE (full time employees) including post-docs, depending on the year and whether you count remote or part-time employees/students. I typically aim to hire 1-2 on-campus graduate students per year, on average. This would be considered “medium-sized” compared to other labs at comparable instituions. For what it is worth, I think this size is a nice size as a new graduate student, since you will have other students to work with and call your friends and colleagues, but there aren’t so many students that I can’t meet with you one-on-one regularly.

In terms of academic background, many students have a B.S. in Engineering (typically Mechanical) but we have also had folks in the lab (or who closely collaborate with us) who possess B.S. degrees in Computer Science, Applied Math, and Physics. Students in my lab also come from a diverse array of nations, cultures, ages, gender identities, learning styles, beliefs, and cognitive backgrounds. I view this as a strength that enables us to do new and innovative things. You should know that wherever you come from and whatever you believe, you will always be welcome in the lab provided your respect your colleagues, approach your own work with integrity, and like bringing computing and math to bear on hard engineering and design problems!

How many years do your PhD students take to graduate?

This depends heavily on the student’s willpower, determination, and publication record. In the case of the two PhD students I have graduated start-to-finish thus far, both took almost exactly four years. I think the department average is slightly under 5 years, last I recall.

Will I work alone on my project or as part of a team?

This depends on the student’s preferences, but generally speaking you will interact with other students on various papers and projects. Even when your project is somewhat different that what other folks are working on, there are generally many opportunities to collaborate on joint papers or proposals if you think it would benefit you. For example, a common collaboration story in the lab often sounds like “Hey, you developed cool technique X in your last paper, and I think if we combine it with cool technique Y that I just worked on we would have something awesome!” Likewise, we often teach each other useful skills, such as how to use our NVIDIA DGX or the HPC cluster or different statistical/math techniques. So, even if you work on your own project, I don’t think anyone ever really feels like they are doing things alone here. This differs if one is a remote or part-time student who works far off-campus, but even in those cases we often connect folks remotely with people in the lab who have diverse or helpful expertise.

Can I talk with your students?

Absolutely! Typically, after I have decided to extend an admission offer to you, I will connect you with some current graduate students and alumni who can speak to their experience at UMD. I do this because they can give you unfiltered/unbiased responses to any questions that you have and also address things I don’t know well as a faculty member. Often there are questions you don’t feel comfortable asking a faculty member directly, and in those cases talking to actual students can be helpful.

I have another offer from famous person X; what are some reasons why I should work for you instead of him or her?

This is always a hard one since I probably personally know famous person X and collaborate with or respect him or her immensely. He or she would probably be an excellent choice of advisor! That said, here are some “highlight points” that you can jot down on your pros/cons list when making your decision. You can read through some of the other questions in this FAQ to get a fuller picture beyond these brief points.

  • In general, my students do really well (academically and professionally) at securing whatever employment opportunities they want, whether academic, government, or industry positions. So in that sense, we have a good track record of getting people where they want to go.
  • We collaborate a lot on interesting, hard problems that matter to society and industry. From healthcare to energy efficiency to national security, generally speaking, people care about the problems we work on. Here it is easy to feel like your efforts will make a difference, even if your research is highly theoretical in nature. In some labs it can feel like you are making only incremental progress on niche problems, but that is generally not the case here.
  • Living here is both awesome and cost-effective. You get access to big-city-level (Washington DC) amenities/attractions and nearby national parks (Shenandoah), but living in College Park is fairly inexpensive and safe. Our graduate student salaries are at the top range of what schools offer. You can have a good life here both inside and outside of school.
  • Unlike famous person X, who already has A# citations, B# awards, and C# graduated students, I am early enough in my career where you will make a serious impact on my own trajectory and success. So I have every possible reason to want to invest in you and your success! You are not just a bean to be counted but rather a valued partner who I must cultivate if I am to succeed myself. Your success literally defines my own success in ways that are simply not often the case for PIs who are already well entrenched in their careers.

What kind of advanced technological or computing equipment do you have available in your lab? How is its use allocated?

We have a variety of desktop workstations equipped with high performance CPU and GPU hardware. We also have an NVIDIA DGX Station, which is a powerful GPU system capable of running medium-sized experiments. In addition to this, there is the campus High Performance Computing system to which we have access. Our campus HPC, like similar HPCs at other universities, is useful for highly parallelizable jobs that require high amounts of compute.

On the advanced manufacturing side, the College of Engineering, within the past decade, has invested over $3M in advanced manufacturing equipment and related services. You can see the various equipment at the TerrapinWorks website, including some of the most advanced additive manufacturing equipment in the world. In addition, we are a member of the Maryland Robotics Center which provides facilities such as the Robotics Realization Lab for manufacturing and testing several robotics platforms.

In terms of access to various equipment, this depends on the specific machine, but generally speaking there is some kind of access and job management infrastructure to ensure that folks have access to the needed compute or manufacturing needed for their projects. If needed, I also have the ability to purchase needed cloud computing from various providers if the needs of the research warrant such expenses.

Would you say a student needs experience in your type of work before starting there, or can it be picked up? For example, do I need to know things like Machine Learning before starting in your lab?

In general, it is my belief that most advanced technical skills can be taught assuming that a student wants to learn them. This means I don’t require a certain set of pre-defined courses before folks start working in my lab; I have seen too many examples of exceptional and motivated students picking up new and different things to believe that anyone’s path is pre-written by their past.

This said, there are certain skills coming out of an undergraduate program that make it easier for students to quickly get up to speed on my research. These are not required by any means (see above), but students who have the below skills make the transition easier than others. I should also say that it depends heavily on the specifics of the project. For example, a robotics project may require different skills than an optimization project, etc.

  • Some exposure to modern programming languages or an interest in learning them. For example, many ME students are familiar with MATLAB. In addition to this, familiarity with languages like Python, Julia, C++, Java, or other programming languages is a plus. Once you have learned more than one programming language, I believe it is fairly easy to pick up others that you might need.
  • Undergraduate courses in topics like Statistics or Linear Algebra give folks the fundamentals. If students have taken courses in Optimization that is also useful background. Often at the undergraduate level in Mechanical Engineering, many students will not have taken a formal course in Machine Learning, and I don’t expect folks to have that background coming in (though certainly if you do have that background or coursework that is a plus)
  • A general scientific interest at the intersection of Mathematics, Computer Science, Optimization, or Machine Learning as it applies to Mechanical Engineering, with a desire to learn how to run computational experiments.

As you can see, I don’t expect folks to be experts in ML or other graduate level mathematics coming into the PhD program (that is what you would come here to learn, after all), but rather that you are open to learning those new areas and have some mathematical or computational background that would give you the right foundation to take graduate level courses in those areas.

What is the overall lab culture? What are hourly expectations for students to be in the lab or working, if any?

This is a difficult question to answer given the “culture” is a broad category. We can attempt to break this down into sub-pieces that might contribute to a definition of culture (certainly let me know if I missed any important pieces).

  • Hours: I do not have specific “lab hours” where I require people to be there. Like in the “real world” we would set expectations for things like when/how many papers you want to submit and then work towards that. I do encourage people to set up a schedule for themselves to practice key time management skills like time-blocking and deep work, etc. If that happens to be typical work week hours I have no problem with that. But it does not personally matter to me when those are for you, since different people have different optimal hours for themselves, so long as our work gets done.
  • Collaborations: See my answers to “Will I work alone on my project or as part of a team?” and “What possibilities are there for co-advising” elsewhere in this document.
  • Lab Activities: Occasionally we will do various lab-wide activities. These can range from short one-hour “Tea Talks” by lab members on relevant topics with tea and cookies, all the way to full-day lab retreats where we might go kayaking or hiking or some other activities with our families. I expect everyone in the lab to attend these to help you connect with your colleagues and partake in important conversations we need to have as a lab in general.
  • Work-Life Balance: In general, I expect people to try their best to balance their research and personal lives. There is a (I think misplaced) mythos about PhD life (and academic research more generally) that we work all of the time. This is a dangerous misconception that, I believe, detracts from your productivity more than one gains. We need to stop perpetuating that falsehood. While certainly I do know people who have pushed themselves for certain stretches before a deadline (and I was guilty of this myself as a graduate student), I neither expect nor encourage such behavior. Rather, I encourage (and guide you on how to achieve) a disciplined and aggressive approach to your time management that will make sure you can be as successful or more so than your colleagues within a reasonable number of work hours per week. This will permit you the needed space to enjoy other aspects of your life and recharge as needed for our demanding line of work.

What is the basic timeline for new students, in terms of what they need to do in the first month, semester, year, etc.?

This depends a lot of the specific project as well as whether or not a student comes in with an M.S. or just a B.S. In general, the first year or so will be focused on taking foundational coursework, while also getting you started on a paper (e.g., perhaps a conference paper at first) so that you can get a sense for the research and build up key skills. Working on a research paper during the first year also helps prep you for the qualifying exam which typically occurs at the end of your first year. The next year or so will be taking more advanced courses as well as setting up collaborations with other professors or industry partners that can help inform you thesis directions. By around your third year, you should be finishing up coursework and working on a handful of papers that can contribute to your thesis. Once courses are done, the focus will be almost entirely on research and paper output.

In general, I encourage students to use their first few years to try out some experimental ideas/direction (what I call Tier 1 projects) as well as building on more mature ideas that contribute directly to grant deliverables funding your time (I call these Tier 2 projects). By the third year, you might also spend some of your time working on concrete applied projects that can transition your earlier work into impact with industry or clinical collaborators (I call these Tier 3 projects), while still maintaining a sustainable pipeline of new or maturing projects that can lead to a stready stream of publications for you.

How many of your students pass things like the Department Qualifying Exam, Dissertation Proposal, Dissertation Defence, etc.?

All of my students have passed the Qualifying Exam, Proposal, and Defense with no issues. Generally, the students I bring onboard, for the type of research that we do, have never encountered difficulties here.

What is the physical lab space like?

We have space for around 10-12 full time employees, split across three interconnected rooms. There is lots of natural sunlight owing to one entire wall of windows. Graduate students have their own desks, plus there is a group table with expandable chairs and a door that closes in case you need to hold a meeting or teleconference. There is also a couch and small reading library. It’s a beautiful space!

What kind of things do your students (or the lab in general) do for fun?

All sorts of things! You can ask the students if you would like. I have heard of things ranging from board game nights to ski trips to road trips to breaks in Key West, among others. There is also a Graduate Student Association at UMD that arranges trips for graduate students to places like ski resorts or New York City or many other places.

For our lab retreats, we have previous held board game nights, cookouts, and kayaking trips, and I plan for similar types of events in the future.

About Myself, as an advisor

How often do you meet with your students?

I have different kinds of meeting, depending on the purpose, as I describe below. In general, for graduate students, typically once a week for between 30-60 minutes, depending on the student career stage and how much we have to cover. If there is a particularly gnarly technical concept or paper we need to discuss, it would not be unusual for us to schedule a separate 2-3 hour discussion at a local coffee shop where we dig into the details of some specific paper or derivation. There is no technical problem that cannot be solved with sufficient amounts of Pour-Over coffee mixed with brain power. Overall, I conduct the following types of meetings with students:

  • One-on-One Meetings: These may be regular or irregular depending on the needs and preferences of the student. Often these will discuss specific technical issues or plans for the upcoming weeks. We may schedule these as-you-need or we might set them up regularly recurring. I want to support the way you work best.
  • Weekly Research Office Hours: This is a drop-in (no appointment needed) time where I make myself available to any student who comes by. Sometimes you just have a quick question and stopping by OH is the right pace for that.
  • Quarterly Individual Development Plan (IDP) Meetings: Every three months I will sit down with you, one-on-one, to discuss your broader professional development and ways in which I can help you grow. These are forward looking and help me guide you as a mentor. They also carve out specific times in the year to support your growth holistically, and not just around individual research results or meetings.
  • Project Meetings: If we have a larger collaborative project with multiple groups, we will typically meet regularly for those as well, to sync up with collaborators.
  • Lab Events: These are typically whole-lab events like retreats or social activities, but can also be things like invited talks or other research things of interest to the lab more broadly.

How do you evaluate success with your students?

I evaluate student progress primarily on what I call growth. That is, is the student growing sustainably in a way that reflects the goals they say they want to accomplish? Unlike certain labs which have fixed minimum “quotas” for things like publications, presentations, hours in the lab, etc. I don’t believe such a “quota” mindset is helpful in cultivating a researcher’s long-term success.

Instead, typically we would start by discussing what your plans are with your degree. Do you want to graduate in four years and immediately apply for a faculty position at an R1 Univerity? Do you want to be a research intern at several industrial companies to find the right fit for your interests? Do you want to explore a topic deeply while balancing and supporting your growing family part-time on with your current job? Obviously the path that each of those goals would take requires a different set of evaluation criteria and expectations for yourself. I view my goal as helping you set expectations and plans for yourself that can help you achieve the end point you want in a reasonable and sustainable way. So by growth I mean that, with respect to your chosen path, you are accelerating your progress along that path at a regular pace; that is, getting better over time.

Procedurally, this means typically an initial meeting when you start your PhD program to assess how things should go, then having you craft up a plan (most typically a plan of what you want to publish where and when) that we both agree is reasonable at achieving the goal you want with the funding sources we have. Then re-evaluating that plan every semester to see how things are progressing and what support you need to keep on track or adjust as needed.

How will I be funded? What happens if funding runs out?

We will discuss the particulars of this during the interview in terms of which possible projects carry what types and duration of funding. But, generally speaking and as of writing, every single one of my PhD students has been funding using Full-Time Research Assistantships (RAs), unless they have specifically asked me to be a Teaching Assistant to gain experience needed for faculty jobs or because they want to. Put another way, funding in my lab has yet to be a problem for my students. I view it as my job to make sure everyone never has to worry about where their funding is coming from.

However, in the case that funding for a specific project does end, the department does have TA slots available for students or fellowship opportunities that are available. Though, as mentioned before, I have not had to use these yet.

Will I be expected to teach?

Not unless you want to or we mutually agree it is in your interest or if funding necessitates so. Unlike some other departments, UMD does not have any required teaching assignments for students. For example, some schools require their students to TA for 1-2 semesters as part of the program; our department does not do that. If you would like to TA because you get personal satisfaction from it, just want the experience to see if you enjoy teaching, or you want to bolster your record for faculty application purposes then that is a different story and I can help you achieve that if you want to.

Outside of ME, are there specific departments you collaborate with more often than others?

I collaborate with a wide number of departments both within and outside of the university. This is often determined via a student’s needs and interests, or whether we have a grant with another department. In order of frequency, my most frequent collaborators come from:

  • Computer Science
  • Other Engineering Departments
  • Mathematics, Applied Math, or Statistics
  • The Information School (ISchool)
  • Psychology
  • Physics
  • Education

How did you get into this type of research? Have your research interests shifted during your career? How so?

In high school, I wanted to design airplanes. Then I got to college, took my first fluid mechanics class, had an absolutely terrible instructor for that class. I noted that he was the same person who would teach aerodynamics. Dreams dashed! Or so I thought. I then talked with the wonderfully talented Dr. Susan Finger who introduced me to advanced Computer-Aided Design tools like Topology Optimization. It was the first time I had seen a computer do something that I, as an engineer, would have considered “creative” beyond just mere rote calculation. This got me interested in studying how humans and computers could work together as a team to be greater than the sum of their parts. I then had the pleasure of working with Dr. Burak Kara who introduced me to Machine Learning and showed me how those (understudied) approaches could transform engineering. I was hooked from that point forward.

I pursued my PhD in MechE, but took almost exclusively CS and Statistics classes that allowed me to get up to speed on ML as an area. I knew little about ML going in, but after many difficult classes, countless hours of hard work, and applying those tools to real world engineering problems, I found a niche for myself where I could do the kind of research that I had wanted to do ever since Susan showed me my first optimization routine. This did not come without struggles or disappointments, which often left me feeling like an outsider. In classes, I was often asked by CS students “Why are you here taking this class?” In department committees, I was often asked “Why is this Mechanical Engineering? Why aren’t you just building a robot?” In faculty search committees, I was often told “No one will fund this kind of work. Do you really think you belong in this department?” Fast forward to today, however, and every MechE department in the country recognizes the future role that ML and AI will play in engineered systems. Funding agencies are regularly initiating new programs at the intersection of ML and Engineering (just our lab at UMD has secured over $10M in grants to cover such research). Companies large and small, including the world’s largest energy systems manufacturers and defense contractors regularly try to hire our students to keep at the cutting edge of those techniques.

At the time that I finished my PhD, my research focus was fairly narrow (various ML algorithms for assisting in crowd-sourced design tasks), but since arriving at UMD I have broadened my purview of problems significantly. We have projects ranging from jet turbine design to helicopter transmissions to heart grafts for newborn children with congential heart disease to ship design to team-based creativity to optimization theory, and, yes, even to airplane design. (It came full circle!) While the various societal problems we work on are broad, they all leverage a common set of mathematical and computational tools at the intersection of Machine Learning and Engineering Design. I have broadened this toolset from traditional ML to include complementary areas of mathematics and computer science, in part just because I like learning new things and I find those tools relevant. This ability to learn and change throughout one’s career is part of the fun of the job, in my opinion!

Would you say your work more theoretical or more applied?

This depends heavily on the specific student’s interests as well as the nature of the grant funding your time. I try to keep a balance of around 50/50 between theoretical and applied work. I find that the distinction is often not black and white: applications can drive or illuminate new theoretical results, and new theory can lead to applications we hadn’t seen before. I will work with you during your time here to find the right mix for you. If you don’t express a preference, I will generally try to expose you to both throughout your PhD.

About The Department and Coursework

What kind of coursework do your students typically take? What kind of skills might I learn if I came to UMD to work with you?

As Mechanical Engineering departments go, UMD is (thankfully) fairly flexible in the specific courses you need to take (see the enme.umd.edu website for the specifics). In practice, my students have had no trouble satisfying any formal coursework requirements. In terms of specific courses, my students typically take courses in the MechE, Computer Science, Applied Mathematics, Statistics, or Mathematics departments, since those courses tend to be the most closely related to the skills we specialize in (usually Machine Learning or similar advanced mathematics/computation). These courses will typically equip you with skills in optimization, advanced computation/programming, statistical learning, among other related areas. Generally speaking, every semester we will discuss what courses will best support your research interests and goals, and select courses based on that.

In addition to formal coursework learning, you will pick up technical skills in things like software version control, advanced computing hardware (such as using GPUs like our NVIDIA DGX or the HPC center) or advanced additive manufacturing equipment if the project requires it (e.g., some of the equipment in the Maryland Robotics Center’s Robotics Realization Lab). My students also end up getting fairly good exposure and practice with technical or academic writing (see, for example, my scientific writing guide) as well as presentation skills. This is in part due to my own focus and extra advising on how to write and present well, which I have found to be key to academic success. In addition to this, my students tend to gain mentorship skills via the UMD undergraduate research program where we can pitch projects and recruit undergrads to work with them. This helps you develop your skills at mentoring and leading others.

What possibilities are there for co-advising, for example, if I want to collaborate with other professors or researchers while at UMD?

There are many possibilities to work with multiple professors while here, and I encourage my students to learn as much as they can from the other excellent and talented faculty who are here. Part of our ongoing discussions around your Professional Development Plan while a student here will include making sure you are assembling the kind of mentorship network you need to succeed in the future. Possible co-advising or co-mentorship setups fall under the following broad types:

  • Informal mentorship: Perhaps you take a course with a professor or meet him or her through some other capacity. If you think his or her work is relevant to your interests, I have no problem with you trying out some seedling ideas for their course project or arranging a time to chat with them separately. Some of my favorite papers have come from chance interactions that students have initiated with other faculty, and they often lead to new grant proposals for me and that other faculty member. This kind of collaboration is organic and informal, but has a range of other benefits.
  • Dissertation Committee Members: Part of a Dissertation or Thesis Committee’s role is to help guide or advise on a students research. So, it is quite common for students to initiate new discussions and collaborations with other faculty and for those faculty members to be natural choices for members of your committee. This can include even co-authoring publications with your committee members.
  • Formal Co-Advising: If your research interests span multiple professors and you wish to officially have “two advisors” then we can also do an official “Co-Advising” setup wherein one professor would be the “Chair” of your dissertation, and then other the “Co-Chair” and you would officially have two advisors with respect to the university system. I have done this multiple times before and, while not common, it is a good option if you truly believe that having multiple official advisors would benefit your research in ways that more informal advising might not.

If I am coming in with a B.S. versus an M.S., does that make a difference? Can students who come from B.S. straight to Ph.D. do well?

Those entering with an M.S. require fewer formal courses than those entering with a B.S., since some of your Master’s credits may transfer over to UMD (depending on the specific courses). In such cases, the time to degree may be shorter, though a lot depends on the specific student’s motivation and research performance. However, in general, students entering with a B.S. or an M.S. (or M.Tech or M.Eng) can all do equally well here. If your end goal is a Ph.D., I would not be particularly concerned regarding whether or not you had a Master’s degree or not when applying here.

Are there courses to take on things like technical writing or other professional development?

We do not require graduate students to take any specific courses in things like technical writing. That said, there are a variety of centers on campus that can help students develop these skills, such as the Teaching and Learning Transformation Center (TLTC), the Graduate School Writing Center or various writing groups and retreats offered through the Graduate School. In addition, there are various workshops that take place throughout the year on techniques like science communication and other skills that you can sign up for. I am happy to support those kinds of activities for you if you would benefit from them.

About Living in the Area

Is UMD expensive to live near? Can I survive well on a graduate student salary in that area?

I live in College Park and find it particularly affordable, given the amenities that are located here. You can ask current students who rent near here what they typically spend, but I can tell you that the UMD graduate student salaries that we provide are larger than any other school I have heard of when comparing offers that students send me, while our living cost here is not nearly as high as some other comparable schools. So, translating that into total quality of life, I think students here can live quite comfortably, especially given what kinds of amentities we have access to via the metro.

I have a spouse or family that needs specific access to work opportunities or school systems. Does the area around UMD support those constraints?

College Park is located in easy access to the broader DMV (DC-Maryland-Virginia) area and is located on the Green Line for the DC metro and is right on a Beltway exit. So, if you have a partner that works in a specific industry it is generally easy to find employment here since the DMV is a major metropolitan area and has a wide range of employers. Likewise, there are many good and affordable school options nearby if you have school-aged kids, or the University has a daycare system if they are younger than school aged. Unlike other schools where the university might be perhaps the only large employer, UMD’s proxity to DC and Baltimore make us an ideal place to balance your academic and family constraints, compared to other large cities or more rural settings.

Is UMD a fun place to live? I like to do activity X in my free time; is that easy to do at or near UMD?

While the specifics depend on the particular activity you enjoy, in general, many things are easy to do here. For example, College Park is located on the Green Line of DC’s metro system. This makes going into DC easy and fairly fast. For example, the metro gets you to the edge of DC in 20 minutes and pretty much anywhere in the city within 45 minutes. We are a few hours drive from multiple large National Parks (like Shenandoah) and less than an hour from the beach/water. We have a commuter train station near campus that is serviced by Amtrack, and their lines go up and down the east coast regularly. (I have personally taken the train to CT from MD and it was quite pleasant.) Our central location with respect to the East Coast makes visiting nearby cities quite easy and fast to do.

Being a major cultural center, Washington DC has everything you would expect from a major city, with the added bonus that most museums and the zoo are free (they are the Smithsonian Museums). For example, my wife and I have gone to everything from local comedy clubs to dancing to the visiting chinese ballet and musical performances at the Kennedy Center. UMD even has its own performing arts center on campus (the Clarice Center). Likewise, for sports, UMD is part of the Big 10 and has every major sport you can want plus relevant fitness facilities. There is no shortage of interesting and diverse things to do and food to eat here. This is often not the case at some schools. Moreover, because you don’t have to pay big-city cost-of-living prices compared to some schools, doing many of those things is actually quite possible on a grad student salary, which is not the case for some schools near other large cities.

Lastly, though importantly for certain students, we are a short drive from three international airports (BWI, DCA, and IAD). Because we live near the capital, you can generally find non-stop flights to almost anywhere on Earth. This can matter a lot for certain international students who want to visit their families because what might be a 1-flight trip from UMD could easily be a 3- or 4-flight trip at other, more remote or isolated universities. It is also easier and cheaper for us to go to academic conferences for this reason.

Is it safe to live in the area?

I personally live in College Park, walk regularly to campus, and am raising my two small children in the area. I have never personally been concerned for my safety, nor have my students ever voiced such concerns to me. Every city will have some degree of incidents, but I find that the overwhelming majority of such notices, when I hear about them, come from nearby the area around Faternity houses where undergrads tend to live, and not where graduate students, faculty, or researchers live.

Are there any specific things I should know about UMD’s location or living the that I haven’t asked already?

  • In general, I think when people first hear of College Park, they don’t realize how accessible it is to DC and major attractions in the area. Put it into Google Maps, and I think you will be pleasantly surprised, especially compared to some other schools you may be considering. There is a lot going on near and around here, while not being all that expensive!
  • Our proximity to DC provides many useful internship or collaboration opportunities that can aid your research that many graduate students do not realize when applying. For example, multiple National Labs are right nearby here, including places like NIST, NASA, ARL, NRL, FDA, NIH, NSWC, and others. This means that you will have lots of opportunities to network with other scientists who can later be helpful to your career, or attend short workshops held by funding agencies that are less than a $10 metro ride away!

Scientific Writing: A Self-Study Guide

Summary: I summarize some useful readings in Scientific Writing and Communication that helped me when I was learning to write and that will likely help my students and others.

Creating high-impact research requires effective writing. Most researchers, however, will never take a formal course in Scientific Writing. Even if we did, a formal course would hide the fact that researchers must continually practice and refine their writing craft over their careers. Since “good writers read about writing,” this page lists a self-study course in scientific writing for the researcher who wants to become a better writer. It lists good books at the bottom of the page (all of which we have available in our lab’s reading library), and then breaks out sections of those books for you to read and progress through: from understanding what scientific writing is to how to structure your writing to writing a first draft to editing and submitting the final copy and beyond. I hope that the below progression will guide you on your path to becoming a better writer and that you will add to and revise this list as you uncover what readings helped or hindered your progression as a writer.

How to use this guide: I organized this guide as a week-by-week progression of readings for the aspiring scientific writer. You will find some weeks more time-consuming than others. To help alleviate this burden, I have bolded specific titles that provide the best coverage in the least time each week. Allotting about 30 minutes, two to three times a week, should provide you ample time to finish the bolded ones. You can read the non-bolded chapters if you have extra time or are particularly interested in the topic. I suggest having some your own writing handy so that you can practice what you learn each week; working through your own concrete example improves both your learning and your research. While I arranged the topics in a week-by-week progression, that should not stop you from jumping around if your needs require it; skipping ahead to the “Editing” sections might be more useful when a deadline approaches.

Part 1: Understanding Content

Week 1: Understanding Your Role and Purpose

  • The Chicago Guide to Communicating Science: Ch. 1: Communicating Science
  • The Chicago Guide to Communicating Science: Ch. 2: Scientific Communication
  • The Craft of Scientific Writing: Ch. 1: Deciding Where to Begin
  • The Craft of Research: Ch. 1: Thinking in Print
  • The Craft of Research: Ch. 2: Connecting with Your Reader
  • Writing for Scholarly Publication: Ch. 1: Writing as Conversation

Week 2: Setting Yourself Up to Be a Productive Writer

  • How to Write a Lot: Ch. 1: Introduction
  • How to Write a Lot: Ch. 2: Specious Barriers to Writing a Lot
  • The Craft of Scientific Writing: Ch. 17: Actually Sitting Down to Write

Part 2: Understanding Organization and Flow

Week 3: Structuring Scientific Documents

  • The Craft of Scientific Writing: Ch. 2: Organizing Your Documents
  • The Chicago Guide to Communicating Science: Ch. 7: The Scientific Paper
  • The Craft of Research: Ch. 12: Planning
  • The Chicago Guide to Communicating Science: Ch. 10: Technical Reports
  • The Craft of Research: Ch. 16: Introductions and Conclusions
  • Writing for Scholarly Publication: Ch. 8: Introduction and Conclusion
  • Writing for Science: Ch. 9: Scientific Journal Articles

Week 6: Abstracts and Titles

  • Writing for Scholarly Publication: Ch. 6: Title and Abstract
  • The Craft of Scientific Writing: Ch. 2: Beginnings of Documents
  • Writing for Science: Ch. 3: Research Abstracts
  • Scientific Papers and Presentations: Ch. 10: Titles and Abstracts
  • Scientific Papers and Presentations: Appendix 8: Evolution of a Title
  • Scientific Papers and Presentations: Appendix 9: Evolution of an Abstract

Week 7: Effective Literature Reviews

  • They Say, I Say: Ch. 1: They Say
  • They Say, I Say: Ch. 2: Her Point Is
  • They Say, I Say: Ch. 3: As He Himself Puts It
  • The Craft of Research: Ch. 5: From Problems to Sources
  • Scientific Papers and Presentations: Ch. 4: Searching and Reviewing Scientific Literature

Week 8: Constructing Cogent Arguments

  • The Craft of Research: Ch. 7: Making Good Arguments
  • The Craft of Research: Ch. 8: Making Claims
  • The Craft of Research: Ch. 9: Assembling Reasons and Evidence
  • The Craft of Research: Ch. 10: Acknowledgements and Reponses

Week 9: Revising a First Draft

  • The Craft of Scientific Writing: Ch. 17: Revising, Revising, Revising
  • Scientific Papers and Presentations Ch. 9: Reviewing and Revising

Week 10: Revising for Organization and Flow

  • The Craft of Scientific Writing: Ch. 3: Providing Transition, Depth, Emphasis
  • Style: Toward Clarity and Grace: Lesson 7: Motivation
  • The Craft of Research: Ch. 14: Revising Your Organization and Argument
  • Style: Toward Clarity and Grace: Lesson 5: Cohesion and Coherence

Part 3: Understanding Style

Week 11: Revising for Clarity and Straighforwardness

  • Style: Toward Clarity and Grace: Ch. 3: Actions
  • Style: Toward Clarity and Grace: Ch. 4: Characters
  • The Craft of Scientific Writing: Ch. 5: Being Clear
  • The Craft of Scientific Writing: Ch. 6: Being Forthright
  • Revising Prose: Ch. 1: Action
  • The Craft of Research: Ch. 17: Revising Style: Telling Your Story Clearly

Week 12: Revising to Reduce Length and Complexity

  • The Craft of Scientific Writing: Ch. 4: Being Precise
  • The Craft of Scientific Writing: Ch. 7: Being Familiar
  • The Craft of Scientific Writing: Ch. 8: Being Concise
  • Style: Toward Clarity and Grace: Ch. 9: Concision

Week 13: Revising the Layout of Sentences:

  • The Elements of Style: Part II: Elementary Principles of Composition
  • The Elements of Style: Part V: An Approach to Style
  • Style: Toward Clarity and Grace: Lesson 1: Understanding Style
  • The Craft of Scientific Writing: Ch. 9: Being Fluid
  • Line by Line: Ch. 3: Ill-Matched Partners
  • Style: Toward Clarity and Grace: Lesson 10: Shape

Part 4: Understanding Illustrations

Week 14: Why Illustrations

  • The Craft of Scientific Writing: Ch. 10: Illustration: Making the Right Choices
  • The Chicago Guide to Communicating Science: Ch. 9: Graphics and Their Place
  • Writing for Science: Ch. 6: Scientific Visuals

Week 15: Effective Illustrations

  • The Craft of Scientific Writing: Ch. 11: Illustration: Creating the Best Designs
  • The Craft of Research: Ch. 15: Communication Evidence Visually

Part 5: Understanding Editing

Week 16: Basics of Copy-Editing

  • The Copyeditor’s Handbook: Ch. 1: What Copyeditors Do
  • The Copyeditor’s Handbook: Ch. 2: Basic Procedures

Week 17: Correcting Basic Errors

  • The Elements of Style: Part I: Elementary Rules of Usage
  • The Elements of Style: Part IV: Words and Expressions Commonly Misused
  • Style: Toward Clarity and Grace: Lesson 2: Correctness
  • Line by Line: Ch. 1: Loose, Baggy Sentences
  • The Craft of Scientific Writing: Appendix B: A Usage Guide for Scientists and Engineers

Week 18: Improving Sentence Structure

  • Line by Line: Ch. 2: Faulty Connections
  • Revising Prose: Ch. 2: Attention
  • Line by Line: Ch. 3: Ill-Matched Partners
  • The Copyeditor’s Handbook: Ch. 14: Grammar: Principles and Pitfalls

Week 19: Adding Punch to Sentences

  • Revising Prose: Ch. 3: Voice

Week 20: Editing Structure

  • The Copyeditors Handbook: Ch. 15: Beyond Grammar

Week 21: Final Edit Checklist

  • Writing for Scholarly Publication: Appendix C: Review Checklist
  • The Copyeditor’s Handbook: Appendix A: Checklist of Editorial Preferences

Part 6: Life-Long Learning About Writing

Week 22: Reading and Reviewing

  • The Chicago Guide to Communicating Science: Ch. 3: Reading Well
  • The Chicago Guide to Communicating Science: Ch. 6: The Review Process

Week 23: Creating a Culture and Support Structure for Writing

  • How to Write a Lot: Ch. 3: Motivational Tools
  • How to Write a Lot: Ch. 8: Good Things Still to be Written
  • Revising Prose: Ch. 8: Why Bother?
  • How to Write a Lot: Ch. 4: Starting Your Own Agraphia Group
  • The Chicago Guide to Communicating Science: Ch. 5: Writing Very Well
  • Writing for Scholarly Publication: Ch. 5: Using Exemplars

Week 24: Proposals

  • The Craft of Scientific Writing: Ch. 13: Writing Proposals
  • Scientific Papers and Presentations: Ch. 5: The Proposal
  • The Chicago Guide to Communicating Science:: Ch. 11 Proposals
  • Writing for Science: Ch. 10: Scientific Grant Proposals

Week 25: Writing for Non-Native Speakers:

  • The Chicago Guide to Communicating Science: Ch. 12: For Researchers with English as a Foreign Language
  • Scientific Papers and Presentations: Ch. 20: To the International Student
  • Writing for Scholarly Publication: Appendix B

List of Writing Books

The Content of Scientific Writing: What and Why to Write

  • The Chicago Guide to Communicating Science. Chicago, IL: The University of Chicago Press, 2003 by Montgomery, Scott L.
  • The Craft of Scientific Writing, 3rd Edition 3rd Edition by Michael Alley
  • The Craft of Research by Booth, Coloumb, and Williams
  • How to Write a Lot: A Practical Guide to Productive Academic Writing by Paul J. Silvia

Organization and Flow: Structuring Ideas

  • The Craft of Scientific Writing, 3rd Edition 3rd Edition by Michael Alley
  • Writing for Science by Robert Goldbort
  • Writing for Scholarly Publication by Anne Sigismund Huff
  • Scientific Papers and Presentations by Martha Davis
  • The Craft of Research by Booth, Coloumb, and Williams
  • On Writing Well. 6th ed. New York: HarperCollins, 2001 by William Zinsser
  • Trees, Maps, and Theorems Effective Communication for Rational Minds, 2009 by Jean-Luc Doumont

Style: Creating Effective Paragraphs and Sentences

  • The Elements of Style by William Strunk, Jr and E.B. White
  • Style: Toward Clarity and Grace by Joseph M. Williams
  • On Writing Well. 6th ed. New York: HarperCollins, 2001 by William Zinsser
  • They say I say: The Moves That Matter in Persuasive Writing by Gerald Graff and Cathy Birkenstein

Editing: The Mechanics of Copy-Editing and Review

  • The Copyeditors Handbook: A Guide for Book Publishing and Corporate Communications by Amy Einsohn
  • Line by Line: How to Improve your Own Writing by Claire Cook
  • Revising Prose. 4th ed. Boston: Allyn and Bacon, 2000 by Richard Lanham
31 Dec 2018 by Mark

Year in Review: 2018

Summary: A copy of my end of year message to my lab, where I review the key events from the past year.

This past year the lab grew in a number of ways, both because existing projects that started at the end of 2017 have now hit full steam and because of new project funding that came in over the past year. Looking back with virtue of hindsight, I would characterize 2015 and 2016 as the lab’s “seedling period” where most of the groundwork for today’s projects was laid (e.g., Greg/Ryan/Jessica’s work on the MechProcessor, Wei’s work on Design Manifolds, Faez’s work on diverse ranking and matching) and 2017 was perhaps the “germination period” where we saw a number of projects take root and bring on new members. With that analogy, I consider 2018 the beginning of the “blooming period,” where we really got to see our hard work pay off with a series of exciting papers and new projects that have expanded the what our lab is capable of. Honestly, this has been the most intellectually fulfilling year of my career and a ton of fun! I have all of your hard work to thank for that. I am truly humbled to work with such a great group of researchers.

The People

The best parts of my lab are the people, so I wanted to start my 2018 review by noting some of the important departures and arrivals this past year. In January, Kailyn Cage (now Dr. Cage—the first member of her family to earn an PhD!) successfully defended her dissertation entitled “Optimizing Mass Customization through Consideration of Human Variability and Machine Specification Trade-Offs” and walked in the May, 2019 ceremony. Kailyn has since taken her immense talents to the SF Bay Area where she started in Amazon Lab 126 (Amazon’s hardware R&D lab located in Sunnyvale). We also bid farewell in May to two undergrad researchers (Josh Land, now a Software Eng. at Appian, and Ashwin Jeyaseelan) who help out in two ongoing DARPA projects. We were joined in the summer by high school interns Jaime Yen (Eleanor Roosevelt HS) and Isabel Beariault (Holton Arms HS) who were investigating materials for soft-robot design and locomotion. Jaime continues with us through May 2019 as part of ERHS’s research practicum (looking forward to the Science Fair next spring!). In the fall, we also welcomed Rachel Hess as a M.S. student working on our new NIH project and also Dr. David Anderson who joined us in November after finishing his Ph.D. under Kris Wood at SUTD. Lastly, in the next few weeks, Dr. Dan Elton, who has been with us since Summer 2017 will be taking up an exciting new position nearby as a Staff Scientist at the National Institutes of Health applying deep learning to medical imaging, primarily radiological images (CT Scans) and we will be joined by Dr. Jun Wang who just completed his Ph.D. under Rahul Rai at UBuffalo.

I am continually impressed by the range of backgrounds all of you bring to the table—from mechanical engineering to physics to statistics/applied math to computer science. Many of the ideas we have been exploring together involve the unique intersection of your many fields; for example, connections between differential geometry and generative models, or between type theory and conceptual design, among many others. Such bridges would simply not be practical without you. You really do make the whole greater than the sum of their parts! This expansion outgrew our old lab in the Engineering Lab building, and led to a new lab space in Martin Hall that we are still in the process of renovating. I’m looking forward to building out that space in 2019!

On a related note personal to me, 2018 saw my undergrad Gemstone team graduate from UMD (Team PRINT). While they didn’t spend much time seated in the lab with you all, some of them interacted with you at lab social events and they have been a big part of my life at UMD for the past four years. (Gemstone is a four-year intensive research program where I advised them during their time at UMD.) This culminated with them successfully defending their undergrad thesis and also getting a paper accepted in and presented at IDETC 2018, which I consider impressive considering they were all undergrads not working with a grad student or post-doc. Many of them are off to high-profile jobs or graduate schools, including Microsoft, UC Berkeley, NAVAIR, Textron, UCLA, among others.

The Science

In broad terms, your scientific efforts have led to our most productive year to-date, by almost every measure I can think of. Depending on how exactly you count it, this year we published around 15 papers, more than double that of last year. This includes multiple papers in the flagship journals of my field (including JMD and SAMO), largely thanks to the excellent work of Faez and Wei. They have consistently put forward brilliant work that I think really drives at some mathematical boundaries of my “home” field in unique ways.

Perhaps just as important, however, we also expanded the reach of our work into broader readership journals like Scientific Reports (part of the Nature publishing group) and Machine Learning venues (like ICML and NeurIPS). Here thanks is due to Dan and Zois for their ambition and drive to get our work out there into related fields that lie beyond my traditional publishing community; I have learned a great deal from both of you. We’ve also begun making inroads at bridging design and robotics, particularly via Charlie’s collaborations with Sarah Bergbrieter on soft robotic actuators that he presented at the IROS soft robotics workshop in the fall. Likewise, a collaboration between Wei and Nicholas has resulted in an invited AIAA SciTech submission; I am looking forward to seeing how our work might integrate with the efforts of the aerospace community. I’m enjoying watching all the ways that you are all making design’s contributions more relevant to other fields—it is only through such efforts that we’ll ultimately expand the impact of design to many more areas of society and get out of our academic bubble.

While papers are easy to measure, your efforts have also led to scientific advances in other ways that often more difficult to keep track of. For example, earlier this year, Charlie, Nicholas, and Josh worked immensely hard at the tail end of Phase I of the DARPA Fundamental Design program. Their efforts were rewarded since we were awarded the Phase II funding for the program over some other very deserving and top-notch institutions and companies. Because of their hard work, I have had the pleasure of being invited to give talks at two NASA laboratories (Langley and Goddard), ARPA-E, and United Technologies all of which were interested in building upon our efforts. Charlie, Nicholas, and now David are exploring some honestly foundational work at the core of ML + Design that we are in the process of writing up for 2019. Even if they are only partially successful, they could fundamentally re-envision the basis for computational design in ways that really build on our strengths. If you haven’t had the chance to chat with them in lab about what they are working on, I encourage you to do so.

In terms of new projects starting up, Faez’s work on diversity algorithms last year laid the foundation for a new NSF grant we just received that will explore how idea diversity impacts final solution quality. Wei’s airfoil manifold papers led to the preliminary results that we used to support a new NIH grant on 3D printable grafts for children with congenital heart disease (this is the project Rachel is now on). Charlie and Nicholas’ work via our FUN DESIGN project provided background material that led us to win a Generate Design project with Lockheed Martin over the next year. While I don’t bother many of you with these kinds of details on a day-to-day basis, I think it is important that you see how the hard work and collaborations you set up really do impact the lab’s activities in the long term.

The Field

While there has generally been renewed interest in the combination of ML and Mechanical Design for the past decade or so, it does seem to me like 2018 saw a marked increase the pace and scale of this interest. For example, in the later part of this year, the Journal of Mechanical Design launched a special issue on the topic of Machine Learning and Engineering Design, and then Design Science followed closely thereafter with a special issue on Deep Learning. At ASME IDETC, machine learning tended to be a specialized topic and was even considered an “emerging topic” in DAC as of only 3-4 years ago. Now, by contrast, sessions with significant coverage of Machine Learning occur in all major design conferences, both as full sessions as well as workshops (e.g., I hosted a Deep Learning workshop at Design Computing and Cognition this past summer). Even in venues not specifically targeting design’s intersection with machine learning, we’ve been seeing pickup; for example, in the Special Issue on Design Creativity that Kate Fu, Dave Brown, and I guest edited for Artificial Intelligence for Engineering Design Analysis and Manufacturing (AIEDAM), ML approaches played a part.

This has also been mirrored in a growing willingness by federal and industry funders to explore ML + Design approaches in a variety of domains. As one example, if you take a look at DARPA’s AI Next Campaign (the agency’s current AI portfolio), of the roughly twenty programs listed there two of them are design programs (that’s around 10% of the AI programs). If you had told me five years ago that that would be the case today, I would not have believed you. Interests do tend to go in cycles and I’m not sure how long such trends will last, but for the time being it is certainly refreshing to see academic, industry, and government sectors all giving the intersection of ML and Design a chance. It is now up to us to make sure we deliver the kind of results that demonstrate design’s value, both within and beyond our field!

Alumni News

Occasionally, I’ll get some updates from past lab alumni. Over this past year, we had a visit from Greg Carmean, who is still nearby at NSWC Carderock. We had fun looking at how the campus has changed since he was here and discussed how he is helping modernize how his department at NSWC-CD handles things like code review and version control (yay Git!). I also talked with Ceena Modarres who is still enjoying being up in NYC as a Data Scientist at Capital One after completing the Data Incubator program here in DC. His group there has been doing some cool collaborations with John Paisley’s group at Columbia, and he was actually at NeurIPS a few weeks ago presenting at the AI in Financial Services workshop on some work in explainable deep learning for credit lending. Both Greg and Ceena said to say hi to everyone!

Thanks everyone again for a great 2018, and I’m looking forward to what we can accomplish together in 2019!

The Graduate School Statement of Purpose: A Faculty Perspective

Summary: I talk about writing an effective graduate school Statement of Purpose (SoP) from the perspective of how I (a faculty member in Mechanical Engineering) read it, along with common mistakes I see applicants make. I offer a few tips for students looking to make sure their SoP gets into the right hands and communicates the necessary details so that you’ll have the best shot of getting accepted.

Applying to graduate school, particularly for Ph.D. positions, can be a nerve-racking experience for many students. Part of the stress comes from the all-important Statement of Purpose, where you have the opportunity to represent yourself and your interests beyond what your purely numerical scores (e.g., GPA, GRE, TOFEL, etc.) or recommendation letters might say about you. There are many guides all over the Internet about how to write your Statement of Purpose (SoP) (See Berkeley, Purdue, UCLA, UNI, etc.). I won’t replicate their advice here.

However, to write well you need to know your audience. So rather than talk about how to write a SoP, I want to describe what it is like to read one. By going in the reverse direction and giving you the faculty perspective, I’m hoping you’ll better understand how and why faculty members read a SoP so that you’ll have an easier time writing something that communicates effectively to them. (With the important caveat that this is all my own opinion, and that other faculty may read a SoP slightly differently.)

Why the type of Graduate School program you’re applying to matters

Before we dive into specific details, we need to differentiate between at least three types of “Graduate School” programs (in Science and Engineering):

  1. Ph.D. Program: long-term commitment usually at least four years in length where the primary responsibility of the student is to conduct research with a faculty advisor. This includes M.S./Ph.D. programs where a student receives an M.S. degree during the course of pursuing their Ph.D. degree.
  2. M.S. w/ Thesis: shorter-term commitment, typically two years in length, where the student splits their time between taking graduate courses and conducting a two year research project with a faculty advisor.
  3. M.S. w/ Coursework: like the M.S. w/ Thesis, except without the research aspects; you just take courses of your own choosing and then graduate with the degree. Depending on the student, there might be no primary faculty advisor that you communicate with on a regular basis. This also includes M.Eng. degrees or “Professional Masters” programs.

Why differentiate? Because faculty will expect your SoP to be fundamentally different depending on what your eventual goals are.

Advice for Particular Types of Graduate Programs

For each of the three main programs, I’ll mention: 1. what I’m looking for in the SoP, 2. common mistakes I see applicants make, and 3. suggestions for improving your SoP so it has a better chance of success.

Ph.D. Program

From the faculty perspective, Ph.D. students are a big, but important, commitment. You will develop a long-term professional relationship with your faculty advisor and they will act as a mentor (officially or unofficially) to you for the rest of their life, even after you graduate. Beyond mentoring, faculty provide most of the financial support for their Ph.D. students, for things like tuition, a stipend, any experimental resources they need to complete their research, not to mention hours of one-on-one training. In exchange for these years of training, the Ph.D. student and the advisor will eventually carve out new areas of knowledge that will push forward the cutting edge of science and technology. In short: big commitment and big pay-off, for both the student and advisor, over the course of about 4-6 years.

What runs through my head when I open the SoP

This student is looking primarily for a faculty mentor that will guide their research throughout the course of the Ph.D. I should be looking to see if I’m the right person to guide them. This means I’m paying attention to:

  1. Are they interested in research that is relevant to my area?
  2. Who else in the department could act as good additional mentors to them?
  3. Do their interests align with projects I have going on right now (or wish to start)?
  4. What are their career goals once they get their Ph.D.?
  5. Do they appear to have enough preparation and credentials that it is worth my time to contact them and set up a remote interview?
  6. If they are the right fit, can I find the appropriate financial support for them over the duration of the Ph.D.?

Ideally, the SoP would help me answer the above questions as easily as possible.

Common Mistakes and Suggestions for Improvement

In line with my above points, here are common mistakes applicants make:

  1. The applicant doesn’t say what their research interests are.

    If a student is fantastic (good grades, research experience, great letters of recommendations, etc.), but doesn’t tell me what kind of research they want to do, there is no way for me to determine if I’d be the right advisor for them.

    Suggestion: Be upfront about the kind of research you want to do, preferably in the first paragraph. Say something to the effect of “My research interests include insert broad MechE topic area here, specifically in insert specific sub-fields here.” This way, in the first paragraph of your statement I know whether you are appropriate for my lab or possibly another faculty member’s lab.

    It’s important to strike a balance here. If you say “I’m interested in Mechanical Engineering”, I would say “this student doesn’t yet know what kind of research they want to do, so how do I know if I’ll be a good advisor for them?” On the other hand, if you are super-specific and say something like “I want to work on agent-based architectures for swarm-based, unmanned underwater vehicles” then I might say “hmm, I don’t really have any funded projects specifically on that topic right now, so maybe the student wouldn’t ultimately be happy with my available projects; maybe another faculty member might have something closer to that.” Look over faculty web pages and try to find a happy medium that is specific enough to pique some faculty interests, but broad enough appeal to the projects they have going on.

  2. The applicant doesn’t make it clear which faculty might be appropriate mentors for them.

    If you want to work with particular people but don’t mention them, you are missing a golden opportunity.

    Suggestion: name dropping particular faculty in your SoP is one of the best ways to get those particular people to look over your application. Look over faculty webpages and specifically highlight one or more faculty that you might possibly want to work with. For example, if you really like the work of Dr. X, but could also see yourself working with Dr. Y or Z, then say something like “I am particularly interested in Dr. X’s work on super cool research topic by Dr. X, but would also be interested in related work by Dr. Y and Dr. Z in the areas of research topics of Drs. Y and Z that you like.”

    That strategy is powerful for multiple reasons. First, it shows you did your homework on what people are working on. Second, it demonstrates that you have specific research interests, but also are flexible regarding projects in related areas. Third, it is eye-catching: if I see my name explicitly listed in a SoP, I spend much more time reading it through, since I already know that the student is possibly interested in my specific line of research.

  3. The applicant doesn’t mention what they want to do after they complete their Ph.D.

    If you don’t mention what you want to do once you have your Ph.D., then I can’t determine if I’ll be able to provide the appropriate contacts or support when you graduate.

    Suggestion: mention why you want to get a Ph.D. and what your goals are once you graduate. Do you want to do research at a research University? Teach at a teaching university? Work in an Industry lab? Start-up company? Open your own bakery/circus/boutique coffee shop? Let us know.

    This is important since this helps us determine two things: 1) why do you want to go through the long and arduous Ph.D. process, and 2) are we the best people to provide you with that kind of path once you graduate? If you’re interested in working as a research scientist for Fancy Company or National Lab, and I have many connections or joint-projects with those or similar labs, then I’ll likely be able to give you what you need to succeed.

  4. Not listing skills or experience that match the research field you are trying to go into.

    Your experience and skills should match the job you want. If you’ve spent years doing experimental work, but list heavy computational or theoretical research interests, we may think “This person is really interested in my area, but do they really know what they are getting themselves into? How much extra training will they need to get up-to-speed on the work in my area?”

    Suggestion: make it crystal clear how your past experience translates directly into applicable skills that will be useful when you start. For example, what if you want to join a lab that does computational work? Did you do a project where you had to learn and master C++ programming? Go ahead and mention it! What about your time doing biological research in a wet-lab? Think about how that experience translates to the new lab you want to join and tailor it to them: maybe your exceptional pipetting ability is not worth mentioning, but your data-analysis abilities would be perfect!

  5. It is unclear what options exist to financially support the student.

    Typically students are funded by the advisor out of an active research grant they have at that time. If you express interest in a project related to that grant, and we have money available, it’s your lucky day! However, sometimes things aren’t that lucky: maybe we’re waiting to hear back about a pending grant, or there is a student graduating in one year who is already on that grant, so money won’t be available for a new student on that project until he or she graduates. This could mean that I can’t admit a fantastic student that I normally would because the right funding didn’t line up.

    Suggestion: if you’re open to receiving other forms of funding, say so. For example, Teaching Assistantships might be possible for several semesters while waiting for dedicated research grant funding. Or if your country has some kind of fellowship program (NSF GRFP or NDSEG are examples in the U.S.) that you have already applied for (or anticipate applying for), then you should mention this. If you’re open to different funding options, then that increases the possibility that we can provide continuous financial support throughout your entire time as a Ph.D. student.

M.S. w/ Thesis

For a research-focused M.S. degree, where you are expected to work with a faculty advisor, the same advice from Ph.D. applications above applies. In addition to that advice, you should be specific about your goals for the M.S. degree.

Students apply to a research-focused M.S. program for a variety of reasons: 1) they like research, but are unsure about whether they want to go all the way with a Ph.D., so they test the water with the M.S. + research first and then maybe apply for the Ph.D. later; 2) They just want the M.S. degree, and intend to go into industry upon completing it, but like research and are hoping to cover some of the M.S. costs through a research assistantship; or 3) they want to get into a Ph.D. program, but believe that having an M.S. first before applying for Ph.D. programs will benefit them more than the direct Ph.D. program (this is less useful if you intend to stay at one institution for both degrees).

Whatever your goals, be specific about them, since that will help faculty determine the appropriate level of support, expectations for your application, and how you might fit into the research group.

M.S. w/ Coursework

This type of degree program doesn’t directly typically involve a faculty advisor, and so faculty have less say in these applications and the advice above is less relevant to you. Since these are often reviewed by a department’s graduate office, I don’t have much input here other than to be specific in your degree goals and state concrete ways in which the programs at that particular university will benefit you.

General Tips for Improving Readability

Given the above considerations, there are some general ways that you can make your SoP easier to read:

  1. Organization and Formatting are your Friends.

    SoPs that are well organized, either by using topic paragraphs/sentences or section headings make it really easy to scan through the SoP and make a judgement. For example, bolding the names of research interests or particular professors make it less likely that person will miss that detail in a quick read. You can even use special headings to organize the SoP, such as “Faculty who are closely related to my research interest:” or “Prior Research Experience:” or “Degree Goals:”

  2. Quality Over Quantity

    The longer the SoP is, the more likely the reader is to skip around looking for the information they want, rather than reading the whole thing. Just like a resume, assume that a first-pass read of your SoP will only be ~10 seconds, so you want to get your point across quickly. This means 1) highlight important points in the first paragraph, 2) keep it shorter, if possible, and 3) use organization to make things easy to scan. Feel free to use all the space provided to tell your story, but make sure that if they only read the first paragraph you’d be able to pique the interest of the appropriate faculty member. I’ve seen a SoP with only a couple of to-the-point paragraphs that led me to interview someone, as well as a multi-page, well organized SoP, labeled with clear section headings that allowed me to identify whether the candidate was appropriate within seconds. Length doesn’t matter as much as quality and clarity.

  3. Print It Out and Give it to Someone to Quickly Read:

    Get a friend of yours to look at your SoP quickly and give you their gut reaction. You have been working so hard on it that you’ll know it inside and out, but a fresh set of eyes can be really useful. Is the page too crammed with text that it looks cluttered, busy, and unapproachable? Is it easy for them to find the above mentioned information? Are there spelling or other errors you didn’t catch? Spending one minute with a third party will drastically help you improve your chances on the real deal.

Best of luck!

Contact Information

Email: fuge@umd.edu

Tel: +1 (301) 405-2558

Lab: 3105 Glenn L. Martin Hall

Office Hours: By Appointment, 2172 Glenn L. Martin Hall, University of Maryland, College Park, MD 20742

Can we meet?

During the Fall and Spring semesters, anyone can stop by my office hours (posted above). If I’m not in during my posted hours, I’m likely traveling, and will try to note such times outside my door in advance. When you stop by my office, check the door position:

Door wide open: Come on in and sit down, no need to knock. This applies even if I’m talking to other people.

Door open, but only slightly: Knock first, and then wait for a response.

Door closed: I’m not in, or cannot be disturbed at the moment. Send me an email, leave a note on my door, or come back during office hours.

If you just need me to sign a form and don’t have to discuss the form with me, just go ahead and say so. Don’t stand around for 20 minutes just to get my signature. If we talked about something during my hours that requires me to do something for you, follow it up with an email summarizing what you need me to do, so that it doesn’t fall off my radar. Put “Note from Office Hours:” in the first part of the subject header to jog my memory.

I don’t want to take up your office hours. Can we meet another time?

Generally, group office hours are more effective than one-on-one meetings for several reasons: For classes, students often ask relevant and related questions, and can provide valuable insight on top of what I can offer; by asking your questions around other students, you’ll get to benefit from their experience. For course and requirements advising, this is even more the case, since students often know details about the curriculum that I may not. This also holds for job advice.

For research, there might be some project-specific details that only the two of us would know, but you’d be surprised at how your peers can offer a lense that complements my own. (After all, they are your future colleagues, and these kind of discussions will be pro forma). Also, the research advice I give one student can benefit other students as well.

If you need to discuss something confidential, then come by towards the end of my hours and let me know that you have something to discuss in private. At the end of my office hours, I’ll close my door and we can discuss things in confidence.

Did you get my email, and how long should I wait until I resend you something?

Professors frequently receive hundreds of emails a day. Here are some tips for improving the response time to your messages:

  1. Include specific phrases in your subject line: certain phrases get automatically flagged so that I respond to them faster.
  2. Include the action you want me to do in the subject line: for example, I can process “Project IDETC Sketching Study: Approve IRB Form 7634 in attached link by Tuesday, 5pm” much faster than “Re: Protocol IV-7634 adjusted”.
  3. Make sure I’m listed in the “To:” field if there is something that you need me to respond to or take action on: I check “CC:” or “BCC:” messages less frequently.
  4. If it has been a week since the response, don’t be afraid to follow up again. Sometimes, when there are lots of important things going on, messages can get lost or buried in the shuffle. Professors have at least seven bosses, constantly requesting things from them, so make sure to be pro-active and follow up.
  5. Get on my critical path. Response rates will increase dramatically.

If you need a response in under 24 hours, you should call my office phone.

Can I do research with your group?

Current Undergraduate Students

We always welcome motivated and talented undergrad students who are interested in conducting research in design. If you are looking for something during the semester, you can come by office hours to chat about possible options. Please check out some of our publications to get an idea of what we do. You can get involved by either volunteering for fun or signing up as an official undergraduate researcher. If you are interested in doing research during the summer, check out the Maryland Summer Scholars program.

Generally, you will work on a specific project with one or more of the lab members so that you can learn what research is like and whether that career choice is right for you. We expect an undergraduate researcher to commit to one semester of 6-8 hours/week or two semesters of 3-4 hours/week in order to get to see all aspects of the research process. If interested, stop by office hours and then send me an email with your resume and transcript (prepend “Undergrad Reseach:” to the subject line).

Current Graduate Students

We often have a variety of M.S.-level or Ph.D.-level projects available for motivated and talented students. Take a look at some of our publications and then stop by office hours so that we can discuss how you can contribute.

Can you write me a Recommendation Letter?

I want all of my students and colleagues to succeed, and I view writing recommendation/reference letters as a critical part of that. I put a lot of thought and time into each of my letters, and often get more requests than I have time to devote to them. Surprisingly, most of the time spent writing a letter is not the valuable “writing” portion, but actually keeping track of destinations and all the background things I need to write a good letter. So, if you want to maximize your chances of me writing you a recommendation letter, follow these steps:

  1. Stop by my office hours or email me to get my permission first (prepend “Recommendation Letter:” to the subject header). The sooner you ask me, the more likely I am to say yes. More than two months before the deadline is great, especially in high-demand times, such as the Fall semester. If the first letter deadline is less than a month away, your chances of receiving a positive response drop exponentially as the deadline approaches. (A rushed letter is a poorly done letter, and I don’t want to do you more harm than good.)
  2. If applicable, ask any graduate student TAs or any Ph.D. students you worked with to email me a summary of the work you did for them and their opinions of that work. Have them put “Reference Letter supplement for your name” in the subject line so I can search for it easily. When you ask them, be sure to make their job easy by summarizing your work for them.
  3. Fill out one of the following online forms. These help me keep track of your letter destinations/deadlines and provides me some more detail about how you want me to position the letter. This will really help your chances of getting the highest quality letter possible.
  4. After filling out the online form, send me one email with all of the following documents attached or linked to (if applicable to the application):
    • Transcript.
    • CV/resume.
    • A draft of your Statement of purpose/Research Statement/Teaching statement (whatever statements or essays are applicable to the letter).
    • Highlight the date that the earliest letter is due. I will submit all of them by that date.
  5. Sit back and wait for my confirmation. I will send you one short email letting you know that I have everything, and confirming the number of letters you listed in the online form.
  6. From this point forward, you can assume with 100% certainty that I will submit your letters on time. You do not need to send me email reminders (if you do, it will only slow down progress on my end). I recognize how important these letters are to you and your career and I consider them one of the most critical documents I write. I will not drop the ball on this.
  7. Once I have submitted all the letters you requested, I might send you one confirmation email letting you know that everything is done. This is a good time to check various online systems and make sure that they received everything correctly. If something is amiss, you can let me know.
  8. Unfortunately, I cannot accept thank-you gifts for writing letters under any circumstances, so please don’t get me anything. The preferred way to thank me is to continue to do fantastic and high-integrity work wherever you end up, and to keep in touch so that I can follow your future success. I didn’t take this job to get chocolate; I took it so that I could help you grow and excel. Your success is my success.

Following the above steps will ensure that I can write you a good letter and will put you ahead of someone else in my queue if he or she did not follow the above steps. If I send you a quick email just directing you back to this page, that means you likely didn’t do one of the steps completely and that I can’t move forward until you do.

Can you participate in my Ph.D. dissertation/proposal/defense committee?

I love learning about new intersecting fields of research, and am generally happy to serve on various committees. I prefer to be a helpful and involved committee member, but also have limited time, which means that I can only handle a few of these commitments at once. This is on a first-come, first-served basis, so it is in your best interest to chat with me as soon as you think you might want me as a member. If I’m already committed to too many students, I’ll have to decline, regardless of how well I know you.

I am fairly open-minded to any research that intersects design. That said, if your topic is so far away from my expertise level that I cannot provide useful feedback, I may have to decline your request. The best way to determine this is to either a) send me an brief paragraph describing your research and how my expertise would benefit your research, or b) stop by office hours to chat about it.

Once I agree to be on your committee you should follow these steps to ensure efficient and high-quality feedback:

  1. Make sure to use appropriate subject headers at the beginning of any emails you send me. This will flag your message as important and will allow me to respond to you faster. For example:
  2. Around 2-4 weeks before any important oral talk (e.g., your defense, etc.), you should email one of my students and have them book you as a speaker during one of our group meetings. Use this opportunity to practice your talk. This benefits my students, since they get to see the format and learn how to ask good questions. It also benefits you, since you can get some feedback that will make you much better prepared for the “real deal” a few weeks later.
  3. Twenty four hours prior to any formal talk that you want me to attend, please email me a draft of your proposal/dissertation/paper etc., so that I can come prepared.
  4. Immediately prior to the above talk, give me a printout of your visual aids or supporting documents. This helps me give you better and more constructive notes and feedback during the talk.
  5. If possible, have someone there (other than yourself) who can take notes for you on any questions that we ask you. I suggest purchasing a high quality, portable audio recorder that you can use to record the feedback if you are unable to locate a designated note taker (always ask permission of everyone if you plan on recording any audio).
  6. Make sure to send me directions to the talk location and a contact number 24 hours ahead of time. I may not be familiar with a particular building and I want to respect both your and my time by not wandering around lost.

What department should I apply to?

I directly supervise students in the Mechanical Engineering Department, so if you wish to work in the lab, you should apply to that department. I frequently collaborate on projects with PIs and students from other departments, particularly in Computer Science, so if you wish to get a degree from a different department you will need to find a primary advisor in that department and then contact me once you get here.

Will you be recruiting new graduate students to your lab this year? For what projects?

Generally, yes, provided there is an appropriate match of skills, interests, and funding. Specifics regarding projects would be determined once your application is processed. For a flavor of the kind of projects we do, check our publications. Also, you can check out my list of Prospective Student Frequently Asked Questions for some common questions PhD students typically ask when evaluating offers or considering applying to UMD.

Can I be your student?

I don’t see applications until after they pass through general admissions. To maximize the chances that I see your application, follow these steps:

  1. Make sure you apply to the right department: Mechanical Engineering.
  2. Select the appropriate final degree goal you are seeking: If you intend to eventually graduate from UMD with a Ph.D. degree, apply to the Ph.D. program, rather than the M.S. program. If you intend to graduate from UMD with just an M.S. degree, but want to do some research while here, then apply to the M.S. program.
  3. Specify in your application that you are interested in “Design” or “Design and Reliability” (the official sub-division in UMD’s ME department). For the 2015 admission cycle, you can choose one or two “Area of Interest” from about 21 choices in the online form; the ones on that list that I readily check are: “Product Design”, “Computer Aided Design”, “Optimization”, “Software”, and “Design Decision Support Systems”. Make sure that you select one of those categories so that I find your application faster.
  4. Where possible, list my name as someone you’d be interested in working with.
  5. In your essay, explain how your research interests and skills complement the ones we list use in our publications.
  6. Use your essay to demonstrate research effectiveness, and highlight any publications that you have. If possible, link to any portfolio or code samples, such as a GitHub account that demonstrates concrete examples of your skills.

What do you look for in choosing graduate students for your lab?/What should I put in my Statement of Purpose?

In addition to strong research skills related to our publications, we also value the following:

  • Strong ability to communicate effectively in written and oral English.
  • Creativity, humor, and the ability to think “outside the box”.
  • Diversity, both cognitive and otherwise.
  • Ability to quickly pick up new skills, particularly software and programming languages.

For some tips and tricks on writing good Statements of Purpose, check out my suggestions.

I am very interested in your research area. What other schools besides UMD have M.E. professors working on similar research?

We work at the intersection of Design, Machine Learning, and Open Innovation. To locate other professors working at this intersection, check out research published at some of the following conferences: ASME IDETC (particularly DTM, DAC, and CIE), the Design Computing and Cognition conference, CHI, Creativity and Cognition, KDD, and ICED. Also check out the following journals: JMD, JCISE, AIEDAM, Design Studies, Research in Engineering Design, and Computer-Aided Design.

Can I be a visiting scholar in the group

Assuming certain circumstances and alignment of interests, yes, this is possible. Send me an email prepended with “Visiting Scholar:” and note: when and how long you plan to visit, how your stay would be funded, and a brief paragraph describing the proposed research your trip would address and why our lab is appropriate. This way, I can see what kind of space and resources we would have available.

Can I do a Post-Doc with your group?

Because of the level of commitment involved, I will typically only consider someone for a postdoc if I am familiar with his or her research (or if he or she is recommended to me by someone whose research I’m familiar with). I normally must know by Fall of the preceding year to consider someone for a postdoc in the next year. If you fit both of those criteria, send me an email (prepend “Post-Doc Position:” to your subject) and include answers to the following question:

  1. How has your Ph.D. training prepared you for the type of work we conduct in our lab?
  2. What joint research opportunities do you see?
  3. How long of a post-doctoral position were you planning for?
  4. Would the post-doctoral experience be externally funded (e.g., through a government or university fellowship), or do you require funding from one of my grants?

If the right combination of interests and funding exists, we can move forward from there.

How can we begin a collaboration?

There are many ways that industry partners can collaborate with us.

  • Financial support through industry-sponsored research grants: Contact me and I can provide more details (prepend “Industry Support:” to the subject for a faster response).
  • You have some of your own data that you would like to analyze using some of our prior techniques or tools: Let us know and we can collaborate together on the analysis once appropriate agreements regarding publication and patenting are set forth.
  • You have research technologies that complement our skills or technologies and you are interested in pursuing a joint external funding opportunity: we are always interested in collaboration possibilities that combine our respective strengths. Contact me and we can explore proposal possibilities (prepend “Industry Proposal:” to the subject for a faster response).

We would like to hire your best student. Can you put us in touch?

Full-time Employment

Prepare a statement that I can forward to relevant students and send it to me in an email, (prepend “Industry Employment:” to the subject line). Make sure it is specific, concrete, and actionable, so that students know how to interpret it. I should be able to just hit “Forward” without needing to offer any additional explanation myself. No attachments, please. You should also send a representative to Design Day once a semester, as you will have the opportunity to talk our best students and view their work.

Internships for Ph.D. Students

We wholeheartedly support internship possibilities for our Ph.D. students, as it typically affords advantages for both the student and the employer, and promotes better collaboration between our lab and our industry partners. The best opportunities possess the following properties:

  • The internship allows the student to contribute to his or her dissertation research. This includes the option to publish resulting internship research, after we ameliorate appropriate IP concerns.
  • The employer is ok with the student continuing to work in research areas related to the internship. This is a great way to establish a continuing research collaboration and gain visibility for both the company and the student. Obviously, the student would not continue to use proprietary technologies developed while employed at the company, unless specified apriori.

If your internship opportunity possesses these properties, then send me an email with “Industry Employment:” prepended to the subject line, and a self-contained summary of the position that I can forward to my students.

Can your students help me with my design project?

In addition to the advice above, we also teach several, project-based courses where students frequently complete engineering projects. If you have an exploratory project that you would like to propose to student teams in one of our affiliated courses, send me an email with “Industry Course Project:” prepended to the subject line. In that email, give me a paragraph description of the potential project and the course you were hoping to pitch it to. I will then make a determination about whether it looks like a good educational experience for the students and how we might move forward. Also, be aware of the University’s Intellectual Property policy.