Find Academic Supervisors

Kostas PapakonstantinouView profile

Associate Professor

• Artificial Intelligence
• Civil Engineering
• Computational Mechanics
• +9 more

Kostas Papakonstantinou is an Associate Professor in the Department of Civil Engineering at Penn State University, affiliated with the College of Engineering. His research bridges Artificial Intelligence (AI) with Civil Engineering, focusing on uncertainty quantification and decision-making under uncertainty. Research Areas: Uncertainty Quantification Stochastic Control Deep Reinforcement Learning Bayesian Analysis Nonlinear Filtering Computational Mechanics Infrastructure Management Rare Events Quantification His work emphasizes AI-driven solutions for structural life-cycle management, infrastructure systems, and autonomous operations. Funded by the NSF and USDOT, his projects include AI-enabled fiscally constrained life-cycle asset management and Deep reinforcement learning for multi-asset infrastructure management . Scientific Awards: NSF CAREER Award: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle He teaches graduate courses on Uncertainty and Reliability in Civil Engineering (CE 566) and Computational Analysis of Randomness in Engineering (CE 597) .

Wayne A FullerView profile

Research Professor

• Survey Methodology
• Sampling Statistics
• Measurement Error Models
• +4 more

Wayne A Fuller is a Research Professor at Iowa State University , specializing in survey methodology and sampling statistics. His work focuses on advanced techniques for handling missing data, small area estimation, and measurement error models, with applications to agricultural surveys and public health research. Education: PhD in Statistics from Iowa State University Research Interests: Fuller's work bridges theoretical and applied statistics through: Development of fractional hot deck imputation methods Bootstrap techniques for variance estimation Small area prediction under constrained models Measurement error correction in health and agricultural data Time series analysis with autoregressive components Integration of administrative data with survey samples Publication Trends: His recent work emphasizes computational approaches to small area estimation (2016-2025), including bootstrap prediction intervals and benchmarking techniques, alongside methodological advancements in imputation and measurement error correction (2004-2015). Contact: Email: waf@iastate.edu Phone: 515-294-5830 Location: Ames, Iowa

Hao ShenView profile

Professor

• Stochastic Partial Differential Equations
• Quantum Field Theory
• Statistical Mechanics
• +5 more

Hao Shen is a Professor in the Department of Mathematics at the University of Wisconsin-Madison. His academic work focuses on stochastic partial differential equations and their connections with quantum field theory, statistical mechanics, and geometric flows. Dr. Shen's educational background includes a PhD from Princeton University in 2013 under the supervision of Weinan E. He completed postdoctoral training at the University of Warwick with Martin Hairer (2014-2015) and served as a Ritt Assistant Professor at Columbia University with Ivan Corwin (2015-2018) before joining UW-Madison. Hao Shen's research interests span multiple areas of mathematical physics and probability theory: Stochastic partial differential equations (SPDEs) Quantum field theory, particularly Yang-Mills theory and gauge theories Statistical mechanics and interacting particle systems Geometric flows, including Ricci flow Stochastic quantization methods Renormalization theory for singular SPDEs His recent publications demonstrate a strong focus on the mathematical foundations of quantum field theories through stochastic methods, with particular attention to Yang-Mills theory in various dimensions and the O(N) sigma model. Shen's work often bridges rigorous mathematical analysis with physical applications, employing advanced techniques from regularity structures, paracontrolled distributions, and stochastic analysis. Simons Fellow in Mathematics (2024-2025) NSF CAREER DMS-2044415 (2021-2026) NSF DMS-1954091 (2020-2023) NSF DMS-1712684 / DMS-1909525 (2017-2020) As an academic leader, Dr. Shen serves on the editorial boards of prestigious journals including Annals of Probability, Stochastics and Partial Differential Equations: Analysis and Computations, and Annales de l'Institut Henri Poincaré. He actively contributes to the mathematical community through organizing conferences and summer schools, such as the 2024 MSRI/SLMath Summer Graduate School on "Stochastic quantization" and the upcoming 2025 SLMath semester program "Recent Trends in Stochastic Partial Differential Equations." Dr. Shen's teaching portfolio includes advanced courses in probability theory, stochastic analysis, and specialized topics in stochastic partial differential equations. His commitment to education is evident in his development of graduate-level courses that bridge theoretical mathematics with applications in physics.

John M. HendersonView profile

Professor

• Cognitive Psychology
• Visual Cognition
• Scene Perception
• +3 more

John M. Henderson is a Distinguished Professor at the University of California, Davis , affiliated with the Visual Cognition Lab . He holds additional roles at the Center for Mind and Brain , Center for Vision Science , Center for Neuroscience , and Plasticity and Memory Program . As an editor for Collabra: Psychology and associate editor for Journal of Experimental Psychology: General , he contributes to open science and cognitive research dissemination. Ph.D. in Cognitive Psychology, University of Massachusetts, Amherst (1988) M.S. in Cognitive Psychology, University of Massachusetts, Amherst (1986) B.S. in Psychology, University of Massachusetts, Amherst (1983) Professor Henderson’s research investigates how visual information is acquired, recognized, and integrated into cognitive systems to guide behavior. His work combines scene perception , reading processes , and visual memory using eye tracking , fMRI , brain stimulation , and computational modeling . Recent studies explore semantic guidance of attention in natural scenes, neural correlates of fixation duration, and developmental attentional patterns. His 15 most recent publications (2023-2025) reflect a focus on semantic processing in visual cognition , scene perception , and computational modeling of attention . Topics include meaning-based attentional guidance, deep learning applications in scene analysis, and neural mechanisms of memory-guided eye movements. Collaborations span cognitive neuroscience, developmental psychology, and AI-driven scene understanding. Scientific honors include: Google Scholar Classics recognition (2017) for groundbreaking 2006 paper Fellow of the Association for Psychological Science, American Psychological Association, and Psychonomic Society Grants from the National Eye Institute and National Institute on Aging support his work on visual cognition and aging. His lab trains students in cognitive methods and interdisciplinary research, bridging psychology, neuroscience, and computational modeling.

Adam ElgaView profile

Professor

• Philosophy of Probability
• Decision Theory
• Self-locating Belief
• +4 more

Adam Elga serves as Professor of Philosophy and Director of the Program in Linguistics at Princeton University, based in the Department of Philosophy with his office in Laura Wooten Hall (204). His contact information includes email adame@princeton.edu and phone 609 258-1477, confirming active institutional engagement. Elga's research spans philosophy of probability, decision theory, self-locating belief, and existential risk, with notable intersections in linguistics through his program directorship. His work addresses fragmentation in belief systems, cognitive instability, and rational decision-making under uncertainty, contributing significantly to epistemological frameworks and philosophical probability models. Recent publications (2020-2025) demonstrate sustained focus on existential risk analysis, decision theory paradoxes, and belief fragmentation. His scholarship bridges theoretical philosophy with practical applications in risk management and cognitive science, particularly through investigations of suboptimal risk decisions and cognitive limitations in high-stakes scenarios. Scientific awards: None mentioned in provided text. Advising and grants: The scraped text contains no information about doctoral students, mentees, or specific research funding. As a full professor and program director, he likely oversees graduate research and secures institutional support, though concrete details are absent from the source material. Labs and research teams: No dedicated laboratories, research groups, or collaborative teams are referenced in the available documentation.

Yang SongView profile

Assistant Professor

• Generative Models
• Diffusion Models
• Score-Based Modeling
• +5 more

Yang Song is an incoming Assistant Professor in Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Prior to joining Caltech, he leads the Strategic Explorations team at OpenAI. He received his Ph.D. in Computer Science from Stanford University under the supervision of Stefano Ermon and completed his Bachelor's degree in Mathematics and Physics from Tsinghua University. His educational background includes: Ph.D. in Computer Science, Stanford University Bachelor's in Mathematics and Physics, Tsinghua University Dr. Song's research focuses on building powerful AI models capable of understanding, generating, and reasoning with high-dimensional data across diverse modalities. He is particularly known for inventing foundational concepts and techniques in score-based diffusion models, which have revolutionized the field of generative AI. His work bridges theoretical advances with practical applications, particularly in image generation, medical imaging, and solving inverse problems. His research has demonstrated how score-based models can achieve state-of-the-art results in image generation while maintaining flexibility for various applications including medical image reconstruction. Analysis of his publication record reveals a strong trajectory in generative modeling, with a particular emphasis on score-based approaches and diffusion models. His work consistently addresses fundamental challenges in generative modeling including sample quality, training stability, computational efficiency, and application to real-world problems. His most recent work on consistency models represents a significant advancement toward making generative models practical for real-time applications. His notable achievements include: ICLR 2021 Outstanding Paper Award for Score-Based Generative Modeling through Stochastic Differential Equations NeurIPS 2021 Spotlight Presentation for Maximum Likelihood Training of Score-Based Diffusion Models Multiple ICLR Oral presentations for his work on consistency models Developing foundational techniques that power many modern AI image generation systems His GitHub repository for score-based generative modeling has gained significant traction in the research community, with over 1,700 stars, reflecting the impact of his work. His research bridges theoretical machine learning with practical applications, particularly in medical imaging where his techniques have shown promise for improving image reconstruction in CT and MRI.

Zukui LiView profile

Professor

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +4 more

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

• Random Matrix Theory
• Probability Theory
• Mathematical Physics
• +3 more

Dr Nicholas Simm is a Principal Research Fellow in the Department of Mathematics at the University of Sussex, affiliated with the School of Mathematical and Physical Sciences. He has been funded by the Royal Society since October 2018 as a University Research Fellow. His research focuses on random matrix theory, probability, and mathematical physics, with applications to areas such as quantum physics and statistical mechanics. Key research interests include orthogonal polynomials, eigenvalue statistics, multiplicative chaos, and Painlevé transcendents. His work bridges pure mathematics and applied problems, leveraging tools from probability theory and integrable systems. Notable publications include studies on asymptotics of orthogonal polynomials, fluctuations in eigenvalues of random matrices, and connections to the Riemann zeta function. His recent work explores high-frequency limits in holomorphic multiplicative chaos and large deviations in elliptic random matrices. Dr Simm has secured grants from the Royal Society and Leverhulme Trust for projects on random matrices, log-correlated fields, and mesoscopic statistics. He collaborates widely, with co-authors including leading researchers in probability and mathematical physics.

Martin WiddicksView profile

Academic

• Mathematical Finance
• Derivatives Pricing
• Financial Economics
• +3 more

Martin Widdicks is a Teaching Associate Professor of Finance and Director of the MSF Program at the Gies College of Business, University of Illinois at Urbana-Champaign. He holds the Josef and Margot Lakonishok Faculty Fellow distinction and has been recognized as an outstanding teacher repeatedly since 2011. His academic background includes a PhD in Mathematical Finance (University of Manchester, 2002) and a BSc in Mathematics (Manchester, 1999). Research interests focus on mathematical finance, particularly derivative pricing methodologies, singular perturbation theory applications, and executive compensation models. He has developed frameworks for multi-asset option valuation and analyzed incentive structures in equity-based compensation. Recent work includes extracting market crash and bankruptcy signals from options data. Teaching responsibilities span core quantitative finance courses (FIN 502, FIN 503), derivatives (FIN 512), and advanced electives like Complex Derivatives (FIN 514) and Term Structure Models (FIN 516/517). His awards include the Shebik Faculty Fellowship (2021–2022) and Towey Faculty Fellowship (2019–2021). Notable publications include Journal of Futures Markets , Journal of Corporate Finance , and Mathematical Finance . His work bridges theoretical finance with practical applications in risk management and corporate decision-making.

Stefan KlusView profile

Lecturer

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +8 more

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

Mahmoud ZarepourView profile

Professor

• Time Series Analysis
• Nonparametric Bayesian Inference
• Analysis of Random Variables with Infinite Variance
• +3 more

Mahmoud Zarepour is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds an MSc from Shiraz University (Iran) and a PhD from the University of Toronto. His research focuses on advanced statistical methodologies including Time Series Analysis, Nonparametric Bayesian Inference, and Analysis of Random Variables with Infinite Variance. He has supervised numerous graduate students and postdoctoral researchers, including Nada Habli, Reyhaneh Hosseini, and Sicheng Huang, among others. Education: MSc (Shiraz University, Iran) PhD (University of Toronto) Dr. Zarepour's research interests emphasize Bayesian nonparametric techniques, stochastic processes, and robust statistical methods. His work bridges theoretical developments with practical applications in areas such as change point detection, multivariate analysis, and resampling schemes. Notable contributions include advancements in Dirichlet process-based methods, bootstrap techniques for complex distributions, and asymptotic theory for unstable time series. His publications span over two decades, with recent works addressing Bayesian bootstrapping, quasi-Bayesian change point detection, and nonparametric inference for spherically symmetric distributions. His research group is part of the Statistics and Probability cluster at the University of Ottawa. Dr. Zarepour has advised multiple students and contributed to collaborative projects in statistical theory and methodology. His work often explores the intersection of probability theory and applied statistics, with an emphasis on rigorous mathematical foundations.

Saqib JavedView profile

Researcher

• Computer Vision
• Deep Learning
• Efficient Neural Networks
• +4 more

Saqib Javed is a doctoral researcher and Researcher at the Computer Vision Laboratory (CVLab) at EPFL, supervised by Prof. Pascal Fua and Dr. Mathieu Salzmann. His research focuses on energy-efficient deep networks, 3D reconstruction, quantization-aware training, and domain generalization. He holds a master’s degree from TU Munich and ETH Zurich. He is affiliated with the School of Computer and Communication Sciences (IC) and the Department of Computer Science at EPFL. His current projects include compressed Gaussian splatting for dynamic scenes, quantized diffusion models, and reducing inference time for vision-language models. He has been awarded the EPFL IC Distinguished Service Award and is a Global Leaders PhD Fellow. His work spans theoretical research and practical applications in low-power device optimization. Teaching roles include serving as a Teaching Assistant for courses like Introduction to Machine Learning (CS-233) and Probability and Statistics (MATH-232). He has supervised multiple students, including Chengkun Li and Ahmad Jarrar Khan, on projects related to quantization and 3D pose estimation. His research also involves collaborations with industry partners like BMW, Siemens, and Intel, focusing on hardware-friendly neural networks. Awards include the EPFL IC Distinguished Service Award (2024) and recognition for his contributions to efficient deep learning. His recent publications address domain generalization, Gaussian splatting, and modular quantization techniques, reflecting his interdisciplinary approach to advancing machine learning efficiency and applicability.

Eoin DelaneyView profile

Lecturer

• Explainable AI
• Trustworthy AI
• Computer Vision
• +6 more

Eoin Delaney is a Lecturer in the School of Computer Science and Statistics at Trinity College Dublin (as of Spring 2025). Previously, he was a Postdoctoral Researcher at the Oxford Internet Institute (OII), University of Oxford (October 2023 – March 2025), focusing on Trustworthiness Auditing in AI. His research spans Explainable AI, algorithmic fairness, computer vision, and sustainable AI applications. He holds a PhD from University College Dublin (UCD), advised by Dr. Derek Greene and Prof. Mark T. Keane, where he developed counterfactual explanations for time series and image data. His work bridges technical AI research with societal impact, including collaborations with Accenture Labs and the VistaMilk SFI Research Centre. Key achievements include developing the OxonFair toolkit (accepted to NeurIPS 2024), winning the 2022 AI Ireland Award for Best Application of AI in a Student Project, and authoring papers in top venues like NeurIPS, AIJ, and IJCAI. He is actively involved in academic service, serving on program committees for FAccT 2025 and AAAI workshops. Eoin has supervised students at the University of Oxford and organizes workshops on AI accountability, such as the Auditing Accountability in Trustworthy Artificial Intelligence in personalized medicine. His teaching and outreach efforts include promoting STEM education through initiatives like an educational website for children, emphasizing cryptography and probability. He continues to explore fairness in AI systems, user-centric explanations, and scalable machine learning frameworks (PyTorch, TensorFlow).

Xiaolei GuoView profile

Professor

• Transportation
• Supply Chain Management
• Congestion Pricing
• +3 more

Xiaolei Guo is a Professor of Management Science at the Odette School of Business, University of Windsor. His expertise spans transportation systems, supply chain optimization, and infrastructure pricing strategies. He holds a Ph.D. in Transportation from the Hong Kong University of Science and Technology (2008) and a B.A. in Hydraulic Engineering from Tsinghua University (2004). His research focuses on congestion pricing mechanisms, transportation network modeling, and toll road economics. Notable areas include optimizing toll designs under uncertainty, analyzing autonomous vehicle impacts on ridesharing, and evaluating strategic distribution channel decisions under environmental regulations. Dr. Guo has contributed to over 30 peer-reviewed articles since 2005, exploring topics such as Braess paradox under bounded rationality, Pareto-improving congestion schemes, and profit maximization for private toll roads. His work bridges theoretical optimization with practical policy applications. He is an active member of the Canadian Transportation Research Forum (CTRF) since 2011 and the Institute for Operations Research and the Management Sciences (INFORMS) since 2009.

Faysal Ahmed ChowdhuryView profile

Assistant Professor

• Statistics
• Applied Mathematics
• Biostatistics
• +3 more

Faysal Ahmed Chowdhury is an Assistant Professor in the Department of Mathematics at Florida Gulf Coast University (FGCU). He holds a Ph.D. in Mathematics with a concentration in Statistics from the University of Louisiana at Lafayette, completed in Spring 2023. His academic journey includes a B.Sc. (2015) and M.Sc. (2017) in Statistics Biostatistics & Informatics from the University of Dhaka, Bangladesh, followed by a second M.Sc. in Statistics (2020) during his Ph.D. program. His research focuses on generalized fiducial inference and lifetime data analysis, with notable publications in journals like the Journal of Statistical Theory and Practice . He teaches courses such as Calculus-I, Statistical Methods, and Probability & Statistics at FGCU. No scientific awards or grants are explicitly listed, but his academic trajectory reflects strong scholarly contributions in statistical methodology.