Find Academic Supervisors

• Statistical Machine Learning
• Bayesian Experimental Design
• Probabilistic Programming
• +3 more

Tom Rainforth is an Associate Professor of Statistical Machine Learning at the University of Oxford's Department of Statistics, leading the RainML Research Lab (rainml.uk). He holds a Tutorial Fellowship at Mansfield College and is Principal Investigator of the ERC Starting Grant 'Data-Driven Algorithms for Data Acquisition' (2024–2029). Previously, he held roles including a postdoc under Yee Whye Teh (2017–2019), Junior Research Fellow at Christ Church College (2019–2019), and Florence Nightingale Bicentennial Fellow (2020–2024). He earned his MEng in Mechanical Engineering from the University of Cambridge and his D.Phil from Oxford under Frank Wood and Michael Osborne, focusing on probabilistic programming and Monte Carlo methods. He briefly worked in Ferrari's Formula 1 team. Research Interests : Bayesian experimental design, probabilistic and data-efficient machine learning, active learning, deep learning (with a focus on probabilistic approaches), probabilistic programming, and Monte Carlo methods. His work emphasizes statistical efficiency and adaptive algorithms. Publications : Recent contributions span modern Bayesian experimental design, adaptive importance sampling (Daisee), and applications of probabilistic methods in LLMs and generative models. His research bridges theory and practice, addressing challenges in scalability and robustness. Awards : ERC Starting Grant (2024–2029), highlighting his leadership in foundational AI research. Advising & Grants : Supervises 15 graduate students, including work on Bayesian neural networks, generative flows, and experimental design. His ERC grant supports cutting-edge data-driven algorithm development. Labs & Teams : Directs the RainML Lab, which develops scalable Bayesian methods and probabilistic AI systems.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +7 more

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

• Privacy-Preserving Machine Learning
• Differential Privacy
• Machine Learning
• +3 more

Dr. Mi Jung Park is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. She is also a Canada CIFAR AI Chair at the Amii. Her research focuses on privacy-preserving machine learning, particularly differential privacy, synthetic data generation, and their applications in healthcare. She holds a PhD in Electrical and Computer Engineering from the University of Texas at Austin, supervised by Dr. Jonathan Pillow, and has held postdoctoral positions at the University of Amsterdam and University College London. Education : PhD, Electrical and Computer Engineering, University of Texas at Austin (2016) Master's, Electrical and Computer Engineering, University of Texas at Austin (2012) Bachelor's, Electrical and Computer Engineering, Hanyang University, Seoul, South Korea (2009) Research Interests : Her lab develops methods to balance privacy and accuracy in data analysis, emphasizing differential privacy's role in healthcare. Key areas include: Generating synthetic data with privacy guarantees Integrating fairness, interpretability, and causality into privacy-preserving models Bayesian techniques for model compression and uncertainty estimation Recent Work Trends : Her publications explore differential privacy in generative models (e.g., diffusion models, kernel methods) and neural network pruning. Recent work highlights privacy-preserving techniques for image classification, latent diffusion, and perceptual feature integration. Awards : Canada CIFAR AI Chair (2021). Advising & Grants : Supervises postdocs (e.g., Mingyu Kim), master's students (e.g., Amman Yusuf), and PhD candidates (e.g., Margarita Vinaroz). Her research is supported by the CIFAR AI Chair program and collaborations with institutions like the Max Planck Institute for Intelligent Systems. Labs & Teams : Leads the Privacy-Preserving Machine Learning Lab at UBC, advancing technologies to protect sensitive healthcare data while enabling clinical and research use.

Shipra AgrawalView profile

Associate Professor

• Optimization
• Machine Learning
• Multi-Armed Bandits
• +12 more

Shipra Agrawal is an Associate Professor at the Department of Industrial Engineering and Operations Research, Columbia University, with affiliations to the Data Science Institute and the Department of Computer Science. Her research bridges optimization and machine learning, focusing on decision-making in uncertain environments. PhD in Computer Science from Stanford University (2011) Researcher at Microsoft Research India (2011–2015) Her work addresses online optimization , reinforcement learning , and game theory , aiming to develop algorithms that balance exploration and exploitation for long-term goals. Applications include internet advertising , revenue management , and resource allocation . Recent publications examine dynamic pricing models, regret bounds in reinforcement learning, and convex knapsack optimization. Her research has been supported by NSF CAREER , Google Faculty Research , and Amazon Research Awards . NSF CAREER Award CMMI-1846792 (2019) Google Faculty Research Award (2017) Amazon Research Award (2017) She has advised PhD students who now hold positions at institutions like Google DeepMind, Amazon, and Facebook. Agrawal serves as an associate editor for Management Science , INFORMS Journal on Optimization , and Journal of Machine Learning Research , and co-chaired major conferences such as COLT 2024 and AISTATS 2025.

Peter K. KitanidisView profile

Professor

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +3 more

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

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.

Alan StockerView profile

Professor

• Behavioral Neuroscience
• Cognitive Neuroscience
• Sensation and Perception
• +4 more

Alan Stocker is a Professor in the Department of Psychology at the University of Pennsylvania, affiliated with the Neuroscience Graduate Group, Bioengineering Graduate Group, and Computational Neuroscience Initiative. His research focuses on understanding how prior beliefs and expectations shape visual perception, combining theoretical models with psychophysical and physiological experiments. Key areas include optimal inference frameworks, neural coding efficiency, and decision-making under uncertainty. Education: PhD in Physics (ETH Zurich, 2002); MSc in Biomedical Engineering and Materials Science (ETH Zurich, 1995). Prior appointments include postdoctoral work at New York University and ETH Zurich. Research Interests: Behavioral Neuroscience, Cognitive Neuroscience, Sensation and Perception. Specific projects explore statistical properties of visual environments, subjective expectations from past decisions, and neural computations underlying perception. Advising: Current advisees include Cheng Qiu (Postdoc), Ansh Soni, and Mengting Fang (Graduate Students). Courses taught: Perception, Visual Neuroscience, and Probabilistic Models in Perception and Cognition. Labs/Teams: Leads the Computational Perception and Cognition (CPC) Laboratory, emphasizing theory-driven experimentation. Collaborates widely across disciplines including bioengineering and electrical systems engineering.

Jiti GaoView profile

Professor

• Econometrics
• Non- and Semi-Parametric Econometrics
• Time-Series Analysis
• +3 more

Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives

• Bayesian methodology
• Variable selection and model averaging
• Nonparametric regression models
• +3 more

Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.

Sonia PetroneView profile

Professor

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +1 more

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

Zhe ZengView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +8 more

Zhe Zeng is an incoming Assistant Professor in the Department of Computer Science at the University of Virginia starting July 2025. Currently, she serves as a Faculty Fellow in the Computer Science Department at New York University. She earned her Ph.D. in Computer Science from UCLA in 2024 under Professor Guy Van den Broeck, and her B.S. in Mathematics from Zhejiang University in 2018. Research Focus: Dr. Zeng specializes in neurosymbolic AI and probabilistic machine learning, developing methods that integrate symbolic knowledge (logical constraints, graph structures) with probabilistic uncertainty. Her work spans three core areas: Reasoning: Probabilistic inference, tractable probabilistic models Learning: Constrained deep learning, graph ML, weakly supervised learning Trustworthiness: Explainability, uncertainty quantification, domain-knowledge integration Awards & Honors: Rising Star in EECS (2023) Amazon Doctoral Fellowship (2022) NEC Research Fellowship (2021) ICML Travel Award (2018) Outstanding Graduate, Zhejiang University (2018) Advising & Mentoring: Has supervised six students including PhD candidates and undergraduates at UCLA, Tsinghua, and CAS, with placements at Princeton and UT Austin. Academic Service: Regularly reviews for NeurIPS, ICML, ICLR, UAI; served as UAI 2023 discussant; active in WiML mentorship programs.

Samuel W.K. WongView profile

Associate Professor

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +4 more

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

Abderrahim BenslimaneView profile

Professor

• Distributed Computing
• Networking
• Communication Protocols
• +7 more

Abderrahim Benslimane is a Full Professor of Computer Science at the University of Avignon, France, where he serves as Vice Dean of International Relations at the UFR STS (Unité de Formation et de Recherche en Sciences et Technologies). He is also Head of the master degree SICOM (Systèmes Informatiques Communicants: réseaux, services et sécurité) program at the university. His extensive academic career spans several decades with significant contributions to computer science, particularly in networking and security domains. Professor Benslimane holds a HDR (Title to supervise researches) from the University of Cergy-Pontoise, a Ph.D. from the University of Franche-Comté, along with M.S. and B.S. degrees in Computer Science from the same institution and the University of Nancy respectively. His research interests primarily focus on distributed computing, networking and communication protocols, with particular emphasis on modeling, describing and implementing secure communication protocols and multimedia applications in heterogeneous network architectures. He combines engineering and theoretical approaches using graphs, distributed algorithms, transition systems, and performance evaluation models. Benslimane's scholarly work demonstrates a strong trend toward addressing security and privacy challenges in emerging technologies. His recent publications focus on cybersecurity applications for wireless sensor networks, Internet of Things, blockchain implementations, UAV communications, and vehicular networks. He has pioneered research in energy attack mitigation, trust management systems, and secure group communications, often employing game theory and novel cryptographic approaches. His work bridges theoretical foundations with practical implementations in next-generation networking technologies. IEEE VTS Distinguished Lecturer (2020-2022) Best Paper award at IEEE ICC 2019 Multiple Prime d'Encadrement et de Recherche Doctorale awards (1998-2021) Prime d'Excellence Scientifique (2011-2015) IEEE Senior Member As an academic leader, Benslimane has served as Editor in Chief of Multimedia Intelligence and Security Inderscience Journal, Area Editor of IEEE Internet of Things Journal, and Associate Editor for multiple prestigious publications including IEEE Transactions on Multimedia and IEEE Wireless Communication Magazine. He has founded and led research centers including the Informatics Research center (CRI) at the French University in Egypt and the Multimedia and networking team (RAM) at the Laboratoire d'Informatique d'Avignon (LIA). His laboratory research focuses on security, communication protocols, graphs and distributed algorithms, with applications in ad hoc networks, sensor networks, vehicular networks, and IoT.

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +5 more

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

• Computational Statistics
• Causal Inference
• Machine Learning
• +3 more

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects