Find Academic Supervisors

Mingchen GaoView profile

Associate Professor

• Medical Imaging Informatics
• Computer Vision
• Machine Learning
• +3 more

Mingchen Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He serves as Program Director for the Engineering Sciences (Artificial Intelligence) MS Program and is affiliated with the Institute for Artificial Intelligence and Data Science. Previously, he was a Postdoctoral Fellow at the NIH Clinical Center's Radiology and Imaging Science Department (2014–2017). His research focuses on medical imaging informatics, computer vision, and machine learning applications in healthcare. Notable projects include NSF-funded work on continual learning and federated domain adaptation. He teaches advanced courses like CSE674 (Advanced Machine Learning) and CSE703 (Deep Learning for Medical Imaging). Dr. Gao earned his Ph.D. in Computer Science from Rutgers University (2014), advised by Dimitris N. Metaxas, and a B.S. from Southeast University, China (2007). His lab develops AI systems for medical diagnosis, with recent work on robust neural networks and federated learning frameworks. His team has produced impactful algorithms for segmentation, classification, and domain adaptation in imaging tasks. Current research includes NSF CAREER Award (2023–2028) for deployable medical diagnosis systems and collaborations on drug discovery and toxicity prediction. He advises four PhD students and has authored over 60 peer-reviewed publications in top venues like NeurIPS, CVPR, and MICCAI.

Emmanuel J. CandèsView profile

Professor

• Compressive Sensing
• Mathematical Signal Processing
• Computational Harmonic Analysis
• +7 more

Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).

Bradley NelsonView profile

Professor

• Microrobotics
• Nanorobotics
• Medical Robotics
• +3 more

Bradley Nelson is a Full Professor of Robotics and Intelligent Systems at ETH Zürich since 2002, leading the Institute for Robotics and Intelligent Systems. He holds a Ph.D. in Robotics from Carnegie Mellon University (1995), with prior roles as Assistant Professor at the University of Illinois at Chicago (1995-1998) and Associate Professor at the University of Minnesota (1998-2002). His research focuses on microrobotics and nanorobotics for biomedical applications, including targeted drug delivery and medical robotics. He leads the Microrobotics Lab (MSRL) and chairs international workshops/conferences. Awards include IEEE and ASME Fellowships, and Best Paper accolades at major robotics venues. Research interests emphasize magnetic microrobot navigation, smart materials, and clinical translation of robotic systems. His work bridges engineering and medicine, addressing challenges like cerebral vasculature navigation for drug delivery. He has developed electromagnetically controllable catheters and telesurgery frameworks. Leadership roles include Head of the ETH Department of Mechanical and Process Engineering and Chairman of the ETH Electron Microscopy Center (EMEZ). Notable contributions include magnetically guided microcatheters, variable-stiffness catheters, and clinical-ready navigation systems. Ongoing projects explore magnetoelectric effects and biodegradable micromotors for environmental and biomedical uses. His lab collaborates with industry and academic partners globally to advance robotic solutions for healthcare challenges.

Bryan A. PlummerView profile

Assistant Professor

• Artificial Intelligence
• Multimodal Machine Learning
• Efficient Neural Networks
• +5 more

Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.

• Revenue Management and Pricing
• Stochastic Dynamic Programming
• Sustainable Transport Planning
• +1 more

Dr Nursen Aydin is an Associate Professor of Operational Research at Warwick Business School (WBS), affiliated with the ISM-Analytics (ISMA) Group. She holds a PhD in Industrial Engineering from Sabanci University, Turkey, with a prior visit to Cornell University for airline revenue management research. Her career includes roles as a Marie Curie Research Fellow at Brunel Business School and as an assistant revenue management analyst at Pegasus Airlines. Research Interests: Revenue Management and Pricing Stochastic Dynamic Programming Sustainable Transport Planning Large-Scale Applications Awards and Fellowships: Turing Fellow, Alan Turing Institute (2021–2023) Marie Curie Research Fellowship, Brunel Business School (2015–2016) Teaching: She teaches Business Analytics (IB1220) across multiple BSc programs and Analytics in Practice (IB9BW0) at the MSc level. Research Group: Active member of the ISM-Analytics (ISMA) Group, focusing on advanced analytics and operational research methodologies.

Mark LeeView profile

Adjunct Professor

• Human Factors Engineering
• Healthcare Safety
• Data Analytics in Manufacturing
• +3 more

Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.

Greg KeoleianView profile

Professor

• Sustainable Systems
• Renewable Energy
• Life Cycle Assessment
• +4 more

Greg Keoleian is the Peter M. Wege Endowed Professor of Sustainable Systems at the University of Michigan. He co-founded and directs the Center for Sustainable Systems and co-leads the MI Hydrogen initiative. His research focuses on advancing life cycle assessment (LCA) methodologies to evaluate the sustainability of technologies, products, and systems. Key areas include renewable energy systems (e.g., wind, solar, bioenergy), transportation decarbonization, circular economy frameworks, and food/agricultural systems. He teaches interdisciplinary graduate courses on Sustainable Energy Systems and Industrial Ecology, and co-directs the Engineering Sustainable Systems Dual Degree Program and the Rackham Graduate Certificate Program in Industry Ecology. Research interests emphasize integrating environmental, economic, and social metrics into sustainability assessments. Notable contributions include pioneering LCA models for hydrogen energy systems, vehicle lifecycle optimization, and circular economy pathways for automotive materials. His work spans global and regional scales, addressing challenges such as reducing greenhouse gas emissions, optimizing resource efficiency, and promoting sustainable urban infrastructure. Keoleian’s recent work highlights synergies between emerging technologies (e.g., wireless charging, autonomous vehicles) and sustainability goals. He collaborates with policymakers and industry leaders to translate research into actionable strategies for deep decarbonization and circular economy adoption. Initiatives like the State of Michigan Hydrogen Roadmap exemplify his focus on bridging academic research with practical implementation. His interdisciplinary approach ensures comprehensive analysis of energy systems, industrial processes, and consumer behaviors. Keoleian’s teaching and mentorship emphasize systems thinking and cross-sector collaboration. He has shaped curricula that prepare students for leadership roles in sustainable systems design, policy development, and corporate sustainability management. Ongoing projects include optimizing reusable packaging systems, evaluating food waste impacts, and modeling urban energy justice in electric vehicle deployment.

John KingstonView profile

Professor

• Phonetics
• Phonology
• Speech Perception
• +4 more

John Kingston is a Professor of Linguistics and Director of the Phonetics Lab at the University of Massachusetts Amherst, where he has been since 1990. He holds a BA and MA from the University of Chicago (1976–1977) and a PhD from UC Berkeley (1985). His research focuses on the interplay between phonetics and phonology, particularly speech perception and its influence on phonological representations. He co-founded the Laboratory Phonology Conference series in 1987 and has conducted fieldwork on Otomanguean languages. His work emphasizes experimental methods to study phonological questions, including studies on vowel perception, tone systems, and cross-linguistic phonetic patterns. Kingston’s academic journey includes roles at the University of Texas, Austin (1984–1986) and Cornell University (1986–1990). His research explores how auditory processing and linguistic knowledge shape speech perception, with notable contributions to understanding tonogenesis, perceptual contrast effects, and vowel category learning in second languages. He collaborates on grants examining Ganong effects and phonological inventories, advocating for theories that bridge perceptual and structural aspects of language. His lab, the Phonetics Lab, supports experimental work on speech perception and production. Kingston is also the Honors Program Coordinator, mentoring students in linguistics and related fields. Despite no explicit awards listed, his extensive publications and conference leadership reflect his scholarly impact.

Kristofer S.J. PisterView profile

Professor

• Micro/Nano Electro Mechanical Systems (MEMS)
• Control Systems
• Robotics
• +6 more

Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.

Alexei KitaevView profile

Professor

• Quantum Computation
• Topological Quantum Phases
• Anyons
• +4 more

Alexei Kitaev is the Ronald and Maxine Linde Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech). His research focuses on quantum computation, topological quantum phases, anyons, topological insulators and superconductors, and the black hole information paradox. He has pioneered the concept of topological quantum computation, where quantum information is protected through topological properties of many-body systems. His recent publications explore quantum error correction, scrambling dynamics, and holographic principles in SYK-like models, reflecting his interdisciplinary impact on quantum physics, computer science, and condensed matter. His work on the Sachdev-Ye-Kitaev model has advanced understanding of quantum chaos and gravitational phenomena. Kitaev has received numerous accolades, including the MacArthur Award (2008), Breakthrough Prize in Fundamental Physics (2012), Dirac Medal (2015), and Oliver Buckley Condensed Matter Prize (2017). He has taught advanced courses such as 'Quantum Computation' and 'Advanced Condensed-Matter Physics' at Caltech. Scientific Awards: MacArthur Award (2008) Breakthrough Prize in Fundamental Physics (2012) Dirac Medal (2015) Oliver Buckley Condensed Matter Prize (2017)

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• Sea Ice Dynamics
• +3 more

Leif Eriksson is a Professor at Chalmers University of Technology , specializing in Radar Remote Sensing within the Department of Space, Earth and Environment . His career at Chalmers began in 2004, and he was promoted to Professor in 2022 after serving as Group Leader (2012–2017) and Head of Faculty Assembly (2017–2020). His research focuses on developing advanced methods for environmental monitoring using radar data, particularly synthetic aperture radar (SAR) from satellites and aircraft. Leadership Roles: Group Leader (Radar Remote Sensing), Faculty Assembly Head Key Collaborations: Rymdstyrelsen, EU Horizon, VINNOVA, European Space Agency Research Interests : Dr. Eriksson’s work spans forest biomass estimation , sea ice dynamics , and ocean surface current/wind retrieval . He integrates SAR data with in situ observations and climate models to study: Forest degradation (clear cuts, storm damage) via multi-temporal SAR Sea ice concentration, drift patterns, and thickness in Arctic regions Wind vectors and surface currents using interferometric SAR techniques Applications for maritime navigation safety and polar shipping optimization Article Trends : His recent publications emphasize SAR’s role in transport infrastructure monitoring (e.g., Iron Ore Line degradation), pan-Arctic landfast ice stability , and multi-frequency SAR fusion for enhanced sea ice observations. Collaborative work with teams across Europe and the U.S. highlights interdisciplinary approaches to climate and marine research. Projects & Grants : Dr. Eriksson leads or contributes to projects such as: CAISA (2022–2024): Air-ice-sea data assimilation EONav (2016–2019): Copernicus data for maritime navigation SEDNA (2017–2020): Safe Arctic shipping Forest Biomass Monitoring (2017–2018): Spaceborne SAR applications His work is supported by Rymdstyrelsen, EU Horizon, and industry partners like Trafikverket. Labs & Teams : He is central to the Radar Remote Sensing Group at Chalmers, collaborating with institutions like Lund University and international bodies such as ESA. His research often involves satellite campaigns (e.g., TanDEM-X, Sentinel) and field studies in polar regions.

Fumiya IidaView profile

Researcher

• bio-inspired robotics
• evolutionary robotics
• soft robotics
• +3 more

Dr. Fumiya Iida is a researcher affiliated with the University of Cambridge , contributing to interdisciplinary research through Cambridge Reproduction and the Department of Engineering . His work spans bio-inspired robotics , soft robotics , and embodied intelligence , with a focus on biomechanics and human-robot interaction. His research integrates evolutionary robotics , reservoir computing , and tactile sensing , aiming to bridge engineering, physiology, and synthetic biology. Recent publications highlight innovations in Soft robotic actuation Robust control systems Multimodal sensor integration Human-robot collaborative tasks Dr. Iida's 15 most recent 2025 articles emphasize reservoir computing , soft sensor design , and adaptive motor coordination , reflecting his commitment to advancing embodied intelligence in robotics. No formal awards or student advisement details were found in the provided texts.

• Data Science
• Numerical Optimisation
• Medical Imaging
• +3 more

Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.

Ranjay KrishnaView profile

Assistant Professor

• Computer Vision
• Natural Language Processing
• Robotics
• +3 more

Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he co-directs the RAIVN lab and leads the computer vision team at the Allen Institute for AI (Ai2). His research intersects computer vision , natural language processing , robotics , and human-computer interaction . PhD in Computer Science from Stanford University (2021) Bachelor's and Master's degrees from Stanford and Cornell His work has received best paper , outstanding paper , and orals at top conferences like CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV. Media outlets including Science , Forbes , and PBS NOVA have covered his research. He has been supported by grants from Google , Apple , NFS , and others. Ranjay advises a diverse group of 15 PhD and postdoctoral researchers , including Jieyu Zhang, Benlin Liu, and Cheng-Yu Hsieh. His teams have developed benchmarks like MemoryBench and The Colosseum , and his PathFinder framework achieved 74% accuracy in skin melanoma diagnosis—surpassing human experts by 9%. Notable contributions include: Perception Tokens for visual reasoning in MLMs SAM2Act for robotic manipulation with memory Synthetic Visual Genome dataset with 5.6M relationships

Michal KolesárView profile

Professor

• Econometrics
• Causal Inference
• Instrumental Variables
• +4 more

Michal Kolesár is a Professor in the Department of Economics at Princeton University , holding this position since July 2020. Previously, he served as Assistant Professor (2014-2020) with dual appointments in Economics and the Woodrow Wilson School (2018-2020), and as Visiting Assistant Professor at MIT (2016-2017). His research focuses on econometrics , particularly causal inference , instrumental variables , nonparametric regression , and robust statistical methods . His work addresses fundamental challenges in high-dimensional data analysis, treatment effect heterogeneity, and finite-sample inference validity. Current projects include developing bias-aware methods for regularized regression and analyzing dynamic causal effects in nonlinear systems. Kolesár's recent publications reveal a strong emphasis on methodological rigor with practical applications. His work spans instrumental variable techniques (addressing contamination bias, weak identification), regression discontinuity designs (discrete running variables, measurement error), and high-dimensional inference (sparsity fragility, honest confidence intervals). Key recurring themes include finite-sample optimality, coverage probability guarantees, and robustness to model misspecification. Fellow of the International Association for Applied Econometrics (2023) Journal of Econometrics best associate editor award (2023) Sloan Research Fellowship (2019) NSF grants for high-dimensional data inference (2021-2025) and nonparametric regression (2016-2019) Graduate Economics Club teaching awards (2017, 2018) As an educator, Kolesár teaches advanced econometrics courses at both undergraduate (ECO 312, ECO 313) and graduate levels (ECO 517, ECO 519, ECO 539b). He serves as Co-editor of the Journal of Business & Economic Statistics (2024-2027) and sits on editorial boards of Econometrica , American Economic Journal: Applied Economics , and others. His professional activities include extensive peer review for top journals and organization of major econometrics conferences including the 2026 Econometric Society Winter Meeting.