Find Academic Supervisors

Elisabeth Paté-CornellView profile

Professor

• Engineering Risk Analysis
• Bayesian Probability
• Probabilistic Risk Assessment
• +3 more

Elisabeth Paté-Cornell is the Burt and Deedee McMurtry Professor of Engineering and Professor of Management Science and Engineering at Stanford University. She chairs the Stanford School of Engineering and is an Emerita Chair of the Department of Management Science and Engineering. Additionally, she holds courtesy appointments as a Senior Fellow at the Freeman Spogli Institute for International Studies (FSI) and as a CISAC Affiliated Faculty Member. BS in Mathematics and Physics (University of Marseilles, France, 1968) MS and Engineer Degree in Applied Mathematics and Computer Science (Institut Polytechnique de Grenoble, France, 1970-1971) MS in Operations Research (Stanford University, 1972) PhD in Engineering-Economic Systems (Stanford University, 1978) Her research focuses on engineering risk analysis, combining Bayesian probability and game theory to model technical and management factors in probabilistic risk frameworks. Applications span NASA shuttle tiles, offshore oil platforms, medical systems, and cybersecurity threats. She has over 100 publications and integrates intelligence analysis into risk assessment for terrorism. World leader in engineering risk analysis Pioneered probabilistic risk models with management factors Research trends: Cybersecurity, healthcare systems, energy infrastructure Scientific awards include election to the National Academy of Engineering (1995) and the French Académie des Technologies (2003). She is a Fellow of the Society for Risk Analysis and INFORMS, with multiple best-paper accolades. She has advised numerous industrial and government organizations, including roles on the President’s Intelligence Advisory Board (2001-2004, 2006-2008) and board memberships at Aerospace Corp., InQtel, and Draper Corporation. Her work is conducted through the Stanford Engineering Risk Research Group.

Stephen JesseeView profile

Professor

• American Politics
• Statistical Methodology
• Ideology
• +4 more

Stephen Jessee is a Professor in the Department of Government at the University of Texas at Austin, affiliated with the College of Liberal Arts. He earned his Ph.D. from Stanford University and holds undergraduate degrees from UT Austin. Education: Ph.D. (Stanford), B.A./B.S. (UT Austin) Research Focus: American politics, statistical methodology, ideology measurement, and judicial-political behavior Key Topics: Congressional voting patterns, Supreme Court dynamics, public opinion, causal inference His recent publications emphasize causal inference methods, public opinion analysis, and quantitative political methodology. Collaborative work spans topics like abortion policy impacts, educational effects on partisanship, and ideological alignment between citizens and elites. Stephen Jessee teaches graduate and undergraduate courses in statistical methodology, causal inference, and American politics. Contact: Office (BAT 4.128), Phone (512-232-7282), Email (sjessee@mail.utexas.edu).

Jesse CrossonView profile

Associate Professor

• American Political Institutions
• Legislative Politics
• Political Economy
• +3 more

Jesse Crosson is an Associate Professor at Purdue University's Department of Political Science and Co-Director of the Program on American Institutional Renewal (PAIR) . His work investigates hyperpartisan institutional transformation , focusing on how insecure majorities , interest groups , and legislative centralization affect policy change and decentralized expertise . He also contributes to Cornerstone Integrated Liberal Arts and Cornerstone for Business teaching sequences. Ph.D. , Political Science, University of Michigan (2019) B.A. (summa cum laude), Political Science, Hofstra University (2013) His research examines: Institutional gridlock mechanisms Legislative polarization dynamics Interest group depolarization roles Electoral reform effectiveness Legislative capacity erosion Artificial intelligence policy development PAIR activities highlight constitutional legacy through Sen. Birch Bayh's work and executive power analysis . Recent publications analyze legislative strategy , AI regulation , and polarized pluralism . The program has expanded with postdoctoral and graduate fellows , including Ayoung Chun (money in politics) and Rylee Crum (economics).

• Mechanism Design
• Auction Theory
• Blockchain Economics
• +4 more

Mallesh M Pai is the Lay Family Associate Professor in the Department of Economics at Rice University and a CEPR Research Fellow. His research bridges mechanism design, auction theory, and blockchain economics, with applications to decentralized finance (DeFi) and privacy economics. Prior to Rice, he was a Janice and Julian Bers Assistant Professor at the University of Pennsylvania. PhD in Managerial Economics and Strategy, Kellogg School of Management, Northwestern University Bachelor's in Computer Science and Engineering, Indian Institute of Technology, Delhi His work focuses on designing robust mechanisms in environments with information asymmetry, privacy constraints, and decentralized systems. Recent publications address dynamic transaction fees, blockchain centralization, and algorithmic collusion. He serves on program committees for major conferences in computer science and economics. Indirect Persuasion (Journal of Political Economy) Dynamic Transaction Fee Mechanism Design (with Max Resnick) Collusive Outcomes via Pricing Algorithms (Marketing Science) Mallesh has received significant recognition for his work, including the Best Paper award at the American Economic Journal: Microeconomics (2021) and Best Pricing Paper from the American Marketing Association RAPSIG (2023). His research is supported by NSF grants on fair data analysis and digital privacy foundations. He teaches graduate and undergraduate courses in microeconomic theory and market design at Rice, emphasizing practical applications of theoretical models. His Erdős number is 3, reflecting interdisciplinary collaborations.

• Biostatistics
• Cancer Genomics
• Statistical Methods for High-Dimensional Data
• +4 more

Yuan Huang is an Assistant Professor in the Department of Biostatistics at the Yale School of Public Health . Her research focuses on statistical methods for high-dimensional data, motivated by challenges in cancer genomics and neurodegenerative diseases. She develops approaches for biomarker identification, network structure estimation, and gene-environment interaction analysis, with applications in Alzheimer’s, Huntington’s, and Parkinson’s diseases. Key Affiliations: Yale Cancer Center, Center for Brain & Mind Health, Yale Center for Analytical Sciences (YCAS) Her methodological work emphasizes integrative analysis across multiple datasets to improve reproducibility and discovery. Recent collaborations span clinical trials, genetics, and epidemiology, with a focus on addressing heterogeneity and nonlinearity in complex biomedical data. Her publications include Bayesian finite mixture models, precision matrix estimation, and advanced techniques for high-dimensional causal mediation analysis. She actively engages in translational research, linking statistical innovation to clinical and public health challenges.

• Organic Functional Materials
• Materials Informatics
• Structural Organic Chemistry
• +4 more

Takuya Taniguchi is an Associate Professor at the Center for Data Science, Waseda University, where he has been employed since 2019, first as an Assistant Professor (2019-2021) before promotion to his current non-tenure track position. His interdisciplinary research bridges organic chemistry, materials science, and data science, with a focus on developing and analyzing functional organic materials, particularly photo-responsive molecular crystals. He maintains active collaborations with research groups including Hideko Koshima and Toru Asahi, and serves on professional committees such as the Manufacturing Industry AI Promotion Association. Education Doctor of Engineering, Waseda University (2019) Graduate studies at Waseda University's Graduate School of Advanced Science and Engineering Undergraduate studies at Waseda University's School of Advanced Science and Engineering, Department of Life Science and Medical Bioscience Research Focus Professor Taniguchi's work centers on structural organic chemistry and physical organic chemistry , with particular emphasis on organic functional materials and the application of materials informatics to crystal engineering. His research investigates how molecular structure influences macroscopic mechanical properties, especially photo-mechanical behaviors in organic crystals. He has pioneered the use of machine learning techniques to predict crystal properties, optimize materials performance, and design novel functional materials with applications in soft robotics and energy conversion. His recent work demonstrates a clear progression from fundamental studies of photo-mechanical crystals toward increasingly sophisticated applications of machine learning in materials science. The 15 most recent publications reveal a strong focus on neural network potentials for crystal property prediction, optimization of photo-actuated materials, and exploration of phase transitions in molecular crystals. This research trajectory shows consistent innovation in combining experimental chemistry with advanced computational methods. Awards and Recognition Inoue Research Encouragement Prize (2019) Ono Azusa Memorial Award from Waseda University (2019) Student Presentation Award at Japan Chemical Society 99th Spring Annual Meeting (2019) Poster Presentation Award at Chirality 2017 Multiple poster and presentation awards at international crystallography conferences Research Leadership Dr. Taniguchi leads multiple research projects funded by JSPS, JST, and industry partners including ENEOS Corporation and Sumitomo Foundation. His current projects include 'Efficient crystal structure prediction for organic porous materials,' 'Machine learning simulation of HOF material stability,' and 'Machine learning application for organic solid-phase transition.' He has successfully secured competitive funding including JST ACT-X and JSPS Early-Career Scientist grants. His work has received significant media attention, with coverage in Nikkan Kogyo Shimbun and Kagaku Kogyo Nippo highlighting breakthroughs in light-driven organic crystals with 3.7x improved output force. Academic Contributions Beyond research, Professor Taniguchi teaches extensively in Waseda's Global Education Center, offering courses in data science, statistical analysis with R, and statistics literacy across multiple academic quarters. He has contributed chapters to technical publications on process informatics, Bayesian optimization, and crystallization process design. His scholarly impact is evidenced by an h-index of 11 (Google Scholar) with 581 total citations, demonstrating significant influence in the emerging field of materials informatics for organic crystals.

• Energy and Power Systems
• Renewable Energy Integration
• Power System Stability
• +13 more

Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.

Gregory WheelerView profile

Professor

• Formal Epistemology
• Philosophy of Science
• Probability Theory
• +4 more

Gregory Wheeler is a Professor of Philosophy and Computer Science at the Frankfurt School of Finance and Management, where he serves as Head of the Computational Science & Philosophy Department and Academic Director of the Master of Applied Data Science program. He received a joint PhD in Philosophy and Computer Science from the University of Rochester in 2002 and has held faculty positions at LMU Munich, Carnegie Mellon University, and Nova University Lisbon, with visiting roles at TU Dresden AI Institute and Max Planck Institute for Human Cognition in Berlin. His research focuses on Formal Epistemology , Bayesian Statistics , and Machine Learning , particularly Imprecise Probability Theory and Probabilistic Models of Coherence . His recent work explores intersections between logic, probability, and computational methods in philosophical inquiry. From his publications, key trends emerge in Uncertainty Quantification , Probabilistic Reasoning , and AI Applications across philosophy and statistics. Topics include peer disagreement resolution, causal modeling, and foundational issues in imprecise probability. Gregory co-founded Exaloan AG in 2019, serving as CTO and Head of AI & Machine Learning. He has contributed to academic leadership through editorial roles and organizing symposia, including the International Symposium on Imprecise Probabilities.

Linda ValeriView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Causal Mediation Analysis
• +9 more

Linda Valeri is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University, and an Adjunct Assistant Professor in Epidemiology at the Harvard T.H. Chan School of Public Health. Education: M.Sc. in Economics and Social Sciences from Bocconi University Ph.D. in Biostatistics from Harvard University Her research focuses on causal mediation analysis, environmental health, and the development of machine learning approaches under Bayesian frameworks to estimate complex health outcomes. She has contributed to software development for mediation analysis in SPSS, SAS, STATA, and R. Dr. Valeri leads NIH-funded studies on causal inference in mHealth and mechanisms underlying Alzheimer’s disease. She serves as Associate Editor for the International Journal of Biostatistics and as Statistical Editor for JAMA Psychiatry and JAMA Network Open .

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +7 more

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

Gaurav NandaView profile

Assistant Professor

• Applied Machine Learning
• Natural Language Processing
• Decision Support Systems
• +4 more

Gaurav Nanda serves as an Assistant Professor in the School of Engineering Technology at Purdue University, where he leads research at the intersection of artificial intelligence and human-centered systems. His work develops intelligent decision support frameworks applicable across critical domains including occupational safety, smart manufacturing infrastructure, healthcare analytics, and educational technology. Education Background Ph.D. in Industrial Engineering, Purdue University Dual Degree: B.Tech. and M.Tech. in Agricultural and Food Engineering (Major) with Electrical Engineering Minor, Indian Institute of Technology Kharagpur His research program integrates applied machine learning and natural language processing to solve complex problems in safety analytics (injury surveillance systems), Industry 4.0 (IoT-enabled manufacturing), healthcare (breast cancer prediction models), and STEM education (MOOC feedback analysis). Current projects emphasize human-AI collaboration, with growing focus on ethical AI implementation and social justice integration in engineering contexts. The INDESS Research Group he directs develops systems that balance algorithmic precision with human factors considerations. Recent publications (2023-2025) demonstrate accelerating adoption of large language models and vision-language systems across application domains, particularly in safety analytics and educational technology. Key trends include human-in-the-loop validation frameworks, explainable AI interfaces, and multimodal data integration (eye-tracking, text, sensor data). His work increasingly addresses fairness considerations in AI deployment, especially regarding diversity in engineering education and workplace safety systems. Dr. Nanda actively mentors the next generation of engineers through the INDESS Research Group , advising Ph.D. candidates Madhumathi Ponnusamy and Shuning Yin, while previously supervising Master's graduates including Srushti Vichare and Meet Suthar. His research receives support through Purdue-affiliated institutes including ICON (Control/Optimization Networks), RDE (Digital Enterprise), and FWL (Future Work/Learning). He maintains active service roles as Editorial Board Member for the International Journal of Industrial Ergonomics and as reviewer for leading publications including IEEE Transactions on Learning Technologies and Safety Science. The research group maintains strong industry connections through the Purdue School of Engineering Technology, with projects spanning manufacturing automation, healthcare informatics, and educational technology platforms. Current initiatives focus on real-time anomaly detection systems, ethical AI frameworks for safety-critical applications, and inclusive curriculum development for engineering education.

Catherine (Kate) CalderView profile

Professor

• Spatial and Spatio-Temporal Statistics
• Bayesian Statistics
• Network Analysis
• +4 more

Kate Calder is a Professor and Chair of the Department of Statistics and Data Sciences at the University of Texas at Austin's College of Natural Sciences. She earned her B.A. in Mathematics from Northwestern University, followed by an M.S. and Ph.D. in Statistics from Duke University. Prior to joining UT in 2019, she held faculty positions at The Ohio State University for 16 years. Former co-director of NSF-funded Mathematical Biosciences Institute Associate Director of UT’s Population Research Center Chair of NIH Analytics and Statistics for Population Research Panel B Study Section Area Editor for Annals of Applied Statistics Her research develops statistical methods for complex data, including spatial statistics, Bayesian modeling, and latent space models for networks. Current projects examine: Mobile-tracking data to analyze crime patterns and social ties Youth health impacts from non-residential activity spaces Exposure assessment through hierarchical pathways models She has secured funding from NIH, NSF, and federal foundations, and was elected to leadership roles in the American Statistical Association and International Society for Bayesian Analysis. Her work emphasizes practical applications across public health, ecology, and finance. Notable achievements: Tripled department size after joining UT Launched Online Master’s in Data Science (2021), surpassing 1,000 students Introduced undergraduate major in Data Sciences (2022)

George StreftarisView profile

Professor

• Bayesian Statistics
• Epidemiology
• Actuarial Science
• +3 more

Professor George Streftaris is a faculty member at Heriot-Watt University within the Actuarial Mathematics and Statistics department under the School of Mathematical and Computer Sciences . His academic career spans over two decades, including roles as associate professor and lecturer at Heriot-Watt University (2004-2019) and post-doctoral positions at BioSS and Heriot-Watt (2001-2004). He serves on the Board of Examiners for the Institute and Faculty of Actuaries and acts as an external examiner for multiple institutions. Professional memberships include Fellow of the Royal Statistical Society , member of the International Society for Bayesian Analysis , and the Greek Statistical Institute . Education: PhD in Statistics (University of Edinburgh) MSc in Statistics and OR (University of Essex, Distinction) BSc in Statistics and Actuarial Science (University of Piraeus, Greece) Research Interests: Streftaris specializes in Bayesian stochastic modeling , inference, and assessment at the intersection of statistics, epidemiology, and actuarial science. His work addresses critical illness insurance, longevity risk, and health-related insurance through predictive modeling and statistical machine learning. Key themes include disease transmission dynamics, model diagnostics, and uncertainty quantification in epidemic systems. Collaborations extend to life and biomedical sciences. Recent Publications: Recent articles focus on COVID-19 pandemic impacts on breast cancer mortality using semi-Markov models, neural network approaches for admission rate prediction, and Bayesian modeling of epidemic systems. Notable projects involve machine learning for multi-asset strategies, model uncertainty in insurance pricing, and stochastic frameworks for disease spread. Research Projects: Centers of Actuarial Excellence (SOA, 2019-2023): Predictive modeling for medical morbidity risk SCOR Foundation of Science (2022-2024): Breast cancer life insurance impact ARC Project (IFoA, 2016-2022): Longevity and morbidity risk management The Data Lab (2017-2018): Machine learning for multi-asset strategies Advising: Supervises ongoing PhD students in Bayesian and neural network modeling in epidemiology, with completed students working on topics like critical illness insurance, disease transmission, and stochastic mortality. Collaborations include researchers in the UK, USA, and international institutions.

• Granular Mechanics
• Mechanical Engineering
• Neutron Diffraction
• +4 more

Associate Professor Chris Wensrich is a faculty member in the School of Engineering at the University of Newcastle, specializing in Mechanical Engineering. He holds a PhD, Bachelor of Mathematics, and Bachelor of Engineering from the same university. His research focuses on granular mechanics, neutron diffraction, and strain tomography, with pioneering work in Bragg-edge transmission strain tomography and granular dynamics modeling. Wensrich has held visiting appointments at Clare Hall College, Cambridge University, and the Isaac Newton Institute for Mathematical Sciences in the UK. He currently serves as President of the Australian Neutron Beam User Group (ANBUG) and is a member of the ACNS Program Advisory Team at ANSTO. His expertise spans applied mechanics, computational modeling (DEM), and experimental techniques involving neutron diffraction. His research interests include granular material behavior, stress distribution measurement, and validation of computational models using neutron imaging. Notable contributions include studies on silo quaking dynamics, force chain analysis in granular assemblies, and residual stress characterization in additive manufacturing. Wensrich has supervised 11 PhD students and secured over $5.4M in grants, including ARC Discovery Projects and industry-linked initiatives. Publications span granular mechanics, strain tomography, and material characterization, with over 60 journal articles and 38 conference papers. His work bridges theoretical, computational, and experimental methods to advance understanding of particulate systems and engineering materials.

Alfred SteinView profile

Professor

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +7 more

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.