27 Dec 2019
by Mark

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 firsts 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. It’s agreat space! 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?

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.

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) 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!