Mark Fuge

Principal investigator
Google Scholar

Mark Fuge is an Assistant Professor of Mechanical Engineering at the University of Maryland, College Park. His research lies at the intersection of Mechanical Engineering, Machine Learning, and Design; an area often referred to as “Design Informatics” or “Data-Driven Design.” He received his Ph.D. from the University of California at Berkeley and received his M.S. and B.S. at Carnegie Mellon University. He has conducted research in applied machine learning, optimization, network analysis, additive manufacturing, human-computer interfaces, crowdsourcing, and creativity support tools. He has received a DARPA Young Faculty Award and a National Defense Science and Engineering Graduate (NDSEG) Fellowship.


Forming Diverse Teams from Sequentially Arriving People

Machine Learning for Engineering Design

Algorithms for Optimal Diverse Matching

Using Semantic Fluency Models Improves Network Reconstruction Accuracy of Tacit Engineering Knowledge

Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks

Measuring and Optimizing Design Variety using Herfindahl Index

Checking the Automated Construction of Finite Element Simulations from Dirichlet Boundary Conditions

Structuring Online Dyads: Explanations Improve Creativity, Chats Lead to Convergence

Deep learning for molecular design - a review of the state of the art

Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

Design creativity

Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning

Interpreting Idea Maps: Pairwise comparisons reveal what makes ideas novel

Thermal Design and Testing of a Passive Helmet Heat Exchanger With Additively Manufactured Components

MGGD Parameter Estimation on the Space of SPD Matrices

BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

Machine Learning of Energetic Material Properties

Unpacking subjective creativity ratings : Using embeddings to explain and measure idea novelty

Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks

Applying machine learning techniques to predict the properties of energetic materials

Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space

Creative Exploration Using Topic Based Bisociative Networks

Ranking ideas for diversity and quality

Diverse Weighted Bipartite b-Matching

Beyond the Known: Detecting Novel Feasible Domains over an Unbounded Design Space

Design Manifolds Capture the Intrinsic Complexity and Dimension of Design Spaces

Discovering diverse, high quality design ideas from a large corpus

How Designs Differ: Non-Linear Embeddings Illuminate Intrinsic Design Complexity

Capturing Winning Ideas in Online Design Communities

The MechProcessor: Helping Novices Design Printable Mechanisms Across Different Printers

A Scalpel not a Sword: On the Role of Statistical Tests in Design Cognition

Pattern Analysis of IDEO’s Human-Centered Design Methods in Developing Regions

Examining Design for Development Online: A Qualitative Analysis of OpenIDEO using HCD/UCD Metrics

How Online Design Communities Evolve Over Time: the Birth and Growth of OpenIDEO

User Research Methods for Development Engineering: A Study of Method Usage with IDEO's HCD Connect

Machine Learning Algorithms for Recommending Design Methods

Collaborative Design Informatics: Leveraging Data to Make Design Teams Better

Analysis of Collaborative Design Networks: A Case Study of OpenIDEO

Automatically Inferring Metrics for Design Creativity

ImpactMap: Designing Sustainable Supply Chains by Incorporating Data Uncertainty

CreativeIT Tools for Assisting and Evaluating Creativity and Problem Framing in Early-Stage Human-Centered Design

Conceptual Design and Modification of Freeform Surfaces using Dual Shape Representations in Augmented Reality Environments

Engineering Sketch Recognition: Findings from Recent Bio-Inspired and Cognitive Studies at VDEL

A Testing Method and Cognitive Model of Human Diagram Understanding for Automating Design Sketch Recognition


Year in Review: 2019

Prospective Student Frequently Asked Questions

Scientific Writing: A Self-Study Guide

Year in Review: 2018

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