Find Academic Supervisors

• Computer Vision
• Robotics
• 3D Data Understanding
• +3 more

Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .

Christopher PottsView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Semantics
• +5 more

Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.

Jennifer S. ThalerView profile

Professor

• Population and community ecology
• Plant-insect interactions
• Tri-trophic interactions
• +3 more

Jennifer S. Thaler is a Professor in the Department of Ecology and Evolutionary Biology at Cornell University, affiliated with the College of Arts and Sciences. She holds a Ph.D. from the University of California, Davis (1999) and a B.A. from Wellesley College (1993). Her research focuses on tri-trophic interactions between plants, herbivores, and predators, with an emphasis on chemical ecology, plant defense mechanisms, and pest control strategies. She teaches courses such as Chemical Ecology and Insect Ecology, and her work bridges fundamental ecological theory with applied agricultural challenges. Dr. Thaler’s research investigates how plant traits, predator presence, and nutritional states influence herbivore behavior and population dynamics. Notable projects include studying non-consumptive predator effects, plant genotypic diversity’s impact on herbivores, and the evolutionary potential of antipredator plasticity. Her publications span journals like Ecology , Oecologia , and Proceedings of the Royal Society B . Her scientific contributions include winning the 2nd place Elton Prize (2015) for research on predator effects on aphids. She actively engages in departmental initiatives, including the Graduate Program and the Plant-Insect Interactions Seminar. Affiliated with the Cornell Stable Isotope Laboratory, she contributes to interdisciplinary efforts in ecological research and education.

Nagi GebraeelView profile

Professor

• Predictive Analytics
• Machine Learning
• Prognostics and Health Management
• +5 more

Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.

• Ethical Implications of Artificial Intelligence
• Societal Implications of Artificial Intelligence
• Policy Implications of Artificial Intelligence
• +4 more

Hoda Heidari is the K&L Gates Career Development Assistant Professor in Ethics and Computational Technologies at Carnegie Mellon University (CMU), with joint appointments in the Machine Learning Department and the Institute for Software, Systems, and Society. She is affiliated with the Human-Computer Interaction Institute and the Heinz College of Information Systems and Public Policy, and co-leads the university-wide Responsible AI Initiative and K&L Gates Initiative for Ethics and Computational Technologies. Education: PhD in Computer and Information Science (University of Pennsylvania), MSc in Statistics (Wharton School) Her research focuses on the Ethical, Societal, and Policy Implications of AI , particularly fairness and accountability in high-stakes domains. Her work includes evaluating risks/benefits of general-purpose AI, human-AI decision-making systems, and AI governance frameworks. She has received multiple awards, including best paper honors at AIES, FAccT, and SAT-ML. Her research is supported by the NSF Program on Fairness in AI, PwC, CyLab, Meta, and J. P. Morgan. Recent Publications examine generative AI safety, fairness measurement, AI incident documentation, and ethical governance. Her teaching includes courses on Responsible AI, ML Ethics, and Societal Decision-Making, with a focus on preparing students to critically analyze AI's societal impact. Scientific Awards: Best Paper (AIES 2024, FAccT 2021, SAT-ML 2023), Exemplary Track Award (EC 2021) Grants: NSF, PwC, CyLab, Meta, J. P. Morgan She advises doctoral students and postdocs across CMU departments and collaborates with interdisciplinary teams. Her service includes organizing AI safety workshops and advising on NIST guidelines for AI red-teaming.

Yongjia SongView profile

Associate Professor

• Optimization
• Stochastic Programming
• Integer Programming
• +5 more

Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.

Tim Q. DuongView profile

Professor

• Medical Imaging
• MRI
• Image Analysis
• +9 more

Tim Q. Duong, Ph.D., is a Professor at Albert Einstein College of Medicine, affiliated with the Departments of Radiology, Biochemistry, Ophthalmology & Visual Sciences, and Neuroscience. His research focuses on medical imaging, MRI, image analysis, machine learning, and predictive modeling for studying diseases like COVID-19 , neurodegeneration (Alzheimer's, multiple sclerosis), brain injuries , and breast cancer . Develops AI-driven MRI techniques for early disease detection Investigates neuroplasticity in glaucoma and diabetic retinopathy Leads grants from NIH and National Eye Institute Research Trends : Recent publications emphasize AI integration in medical imaging, long-term effects of SARS-CoV-2, and advanced MRI applications for ocular and neurological disorders. Grants include multiple R01 awards for diabetic retinopathy and glaucoma studies. Training Opportunities : Actively recruits postdocs, research coordinators, and faculty. Offers research positions for graduate, medical, and high school students, including Regeneron Scholar programs. Labs & Teams : Leads the Duong Lab at Montefiore Medical Center, focusing on translational research for clinical imaging solutions.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +8 more

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

• Sustainability Assessment
• Life Cycle Assessment
• Circular Economy
• +5 more

Dr. Li Shen is an Associate Professor at the Copernicus Institute of Sustainable Development , Utrecht University since 2015. She holds the Energy & Resources group and has been active in sustainability assessment of materials and energy systems since completing her PhD in 2011. Education: BEng (Mechanical Engineering, cum laude 1999, Shanghai) MSc (Sustainable Development, track Energy and Resources 2006, Utrecht University) PhD (2011, Utrecht University, thesis: Bio-based and Recycled Polymers for Cleaner Production – an Assessment of Plastics and Fibres ) Research Interests: Dr. Shen specializes in Life Cycle Assessment and Technological Sustainability Evaluation of Bio-based Plastics , Plastics Recycling , Waste Management , and Nanomaterials . Her work bridges Environmental Science , Chemical Engineering , and Sustainable Development through rigorous system analysis. Scientific Publications: Leading expert in Circular Economy applications for plastics Developed Ex-ante LCA frameworks for emerging materials Key contributor to PEF Bio-based Bottle sustainability analysis Active in Telescope Energy Systems sustainability Published in Nature Communications , Science of the Total Environment , and Journal of Cleaner Production Teaching: Responsible instructor for Advanced Energy Analysis in the Master Energy Science program. Also teaches Life Cycle Assessment and Sustainable Resources Use in the Global Sustainability Science bachelor program. Supervises numerous master thesis projects from Energy Science (ES), Sustainable Development (SD-EM), and Science for Energy Innovation (SBI) programs.

Stephen GravesView profile

Professor

• Operations Research
• Supply Chain Management
• Manufacturing Systems
• +3 more

Stephen Graves is the Abraham J. Siegel Professor of Management and a Professor of Operations Management at MIT Sloan School of Management, with a joint appointment in Mechanical Engineering. He holds a PhD in Operations Research from the University of Rochester. His research focuses on operations research models applied to manufacturing systems, supply chains, and service operations, including strategic inventory positioning and order fulfillment optimization. Graves has served as MIT Sloan’s Deputy Dean and Chair of MIT’s Faculty Council (2001-2003). He is a Fellow of INFORMS, recipient of the Kimball Medal, and member of the National Academy of Engineering. Education: PhD in Operations Research, University of Rochester MBA and AB in Mathematics/Social Sciences, Dartmouth College Research Interests: Graves develops analytical frameworks for supply chain optimization, inventory management, and production planning. His recent work addresses post-pandemic supply chain resilience, digital twins for production systems, and closed-loop supply chains. Current projects explore operational challenges in e-commerce fulfillment and global supply networks. Awards: INFORMS Kimball Medal (2023) Elected to National Academy of Engineering INFORMS Prize for Operations Research President of INFORMS (2020) Advising & Grants: Supervisor to 25+ PhD students across manufacturing, supply chain analytics, and healthcare operations. Active in industry collaborations with companies like Intel, Polaroid, and Monsanto. Leads MIT initiatives including the Leaders for Global Operations program. Labs/Teams: Core member of MIT’s Institute for Data, Systems, and Society (IDSS). Co-director of the MIT Operations Research Center. Involved in interdisciplinary projects combining data science with operations management.

Ruth EtzioniView profile

Adjunct Professor

• Cancer Screening
• Prostate Cancer Research
• Biomarkers
• +5 more

Ruth Etzioni is an Affiliate Professor in the Biostatistics Program and Public Health Sciences Division at the Fred Hutchinson Cancer Center. She leads the Etzioni Lab, focusing on cancer screening, early detection, and overdiagnosis analysis. Her work integrates statistical modeling, epidemiology, and clinical research to address critical questions in prostate and breast cancer control. Dr. Etzioni holds the Rosalie & Harold Rea Brown Endowed Chair and has received a $7.4M NIH Outstanding Investigator Award. Education: PhD in Statistics (Carnegie Mellon University, 1990), MS in Statistics (Carnegie Mellon, 1987), BS in Mathematics (University of Cape Town, South Africa). Research interests emphasize biomarkers, clinical trials, and epidemiological methods. She leads the Biostatistics Core for the Pacific Northwest Prostate Cancer SPORE and participates in the Cancer Intervention and Surveillance Modeling Network (CISNET). Her lab develops models to evaluate screening policies, quantify overdiagnosis, and inform healthcare disparities reduction strategies. Key achievements include groundbreaking work on prostate cancer screening's harm-benefit tradeoffs and contributions to multi-cancer early detection (MCED) frameworks. Recent studies address racial disparities in prostate cancer outcomes, metastasis trends, and the clinical utility of novel diagnostics like PSMA PET imaging. Awards include the Brown Endowed Chair (2020), NCI OIA Award (2023), and recognition for advancing cancer data science. Her lab collaborates with institutions globally and mentors students in biostatistics and translational data science.

Christian PoellabauerView profile

Professor

• Mobile Sensing
• Data Analytics
• Healthcare Technology
• +7 more

Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.

William V. GiannobileView profile

Professor

• Periodontology
• Regenerative Medicine
• Tissue Engineering
• +3 more

Dr. William V. Giannobile serves as the Dean and A. Lee Loomis, Jr. Professor of Oral Medicine, Infection, and Immunity at Harvard School of Dental Medicine (HSDM). He leads the institution as its chief academic and administrative officer while maintaining an active research program and clinical practice in periodontics and implant dentistry at the Harvard Dental Center in Cambridge, MA. Dr. Giannobile received his DDS and MS in oral biology from the University of Missouri, followed by a certificate in periodontology and Doctor of Medical Sciences (DMSc) in oral biology from Harvard School of Dental Medicine. He completed postdoctoral training in molecular biology at the Dana-Farber Cancer Institute and Harvard Medical School. With over 30 years of continuously NIH-funded research, Dr. Giannobile's work focuses on oral and periodontal regenerative medicine, tissue engineering, and precision medicine. He serves as principal investigator of the NIDCR U-Award Harvard Clinical Practice-based Research Program for Dental Students (H-CREDENT), which evaluates the genomics of oral pain in a multi-institutional academic setting. His research has produced over 300 manuscripts, textbook chapters, and patents focused on periodontology, regenerative medicine, and oral health research. He has edited or co-edited over 10 books in clinical and translational research, periodontology, and regenerative medicine. His publications demonstrate consistent focus on periodontal regeneration, biomaterials, tissue engineering, and the molecular mechanisms underlying oral diseases. ADA Gold Medal Award for Excellence in Dental Research (2024) International Association for Dental Research Distinguished Scientist Research in Periodontal Disease Award (2022) Norton M. Ross Clinical Research Award from the American Dental Association (2018) William J. Gies Award (2014) Jan Lindhe International Periodontology Research Award (2011) As an educator and mentor, Dr. Giannobile completed a 10-year term as editor-in-chief for the Journal of Dental Research, the official journal of the International Association for Dental Research. He has served as a consultant to the U.S. Food and Drug Administration for Dental Devices and as an associate editor for the NIDCR Report on Oral Health of the U.S. in 2021. His laboratory continues to advance research in periodontal regeneration and precision medicine approaches to oral health, with ongoing NIH funding supporting innovative translational research.

J. Cole SmithView profile

Professor

• Integer programming
• Combinatorial optimization
• Network flows
• +9 more

J. Cole Smith is the Dean of the College of Engineering and Computer Science at Syracuse University and holds the academic rank of Professor. He joined Syracuse in 2019 with a focus on advancing undergraduate and graduate education, research initiatives, and diversity, equity, and inclusion efforts. Previously, he earned a PhD in Industrial and Systems Engineering from Virginia Tech (2000) and a BS in Mathematical Sciences from Clemson University (1996). His research focuses on mathematical optimization, particularly mixed-integer programming and combinatorial optimization, with applications in network interdiction, logistics, national security, healthcare, and sports. His work has been supported by agencies such as the National Science Foundation, Office of Naval Research, and Defense Advanced Research Projects Agency. Recent research trends emphasize network interdiction under uncertainty, bilevel optimization, and large-scale optimization challenges. His publications in top journals like Operations Research and Mathematical Programming reflect these themes. Awards include INFORMS Fellow (2023) and recognition from Virginia Tech's Grado Department of Industrial and Systems Engineering. While his dean duties reduce direct research involvement, he remains active through collaboration with PhD students. His team's work addresses real-world problems in security, logistics, and healthcare, leveraging advanced algorithmic techniques.

• Geomechanics
• Geophysics
• Numerical Modeling
• +5 more

Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .