Find Academic Supervisors

• Statistical Learning
• Multivariate Analysis
• Classification
• +6 more

Yoonkyung Lee is a Professor of Statistics and Computer Science Engineering at The Ohio State University, affiliated with the Department of Statistics in the College of Arts and Sciences. She holds a PhD from the University of Wisconsin-Madison (2002). Her primary research focuses on statistical learning and multivariate analysis, with specializations in classification, kernel methods, and model stability. She has a courtesy appointment in Computer Science and Engineering since 2016 and served as a faculty co-director of the Translational Data Analytics Institute (2020–2022). Her work has been funded by the National Science Foundation (NSF), and she was elected a Fellow of the American Statistical Association in 2015. Her educational background includes a PhD in Statistics from the University of Wisconsin-Madison. Her research interests emphasize developing methodologies for latent structures in multivariate data, computational frameworks for model stability, and predictive modeling. Notable contributions include advancements in kernel discriminant analysis, Bayesian restricted likelihood methods, and sparse logistic tensor decomposition. Prof. Lee serves on editorial boards for journals such as Chemometrics and Intelligent Laboratory Systems , Econometrics and Statistics , and Journal of Machine Learning Research . Her articles span topics like support vector machines, quantile regression, and nonlinear embeddings, reflecting her expertise in bridging statistics and machine learning. Beyond research, she has advised numerous projects and contributed to interdisciplinary initiatives in translational data analytics.

Joseph AntonelliView profile

Assistant Professor

• Causal Inference
• High-Dimensional Modeling
• Bayesian Methods
• +3 more

Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +3 more

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Yang ChenView profile

Assistant Professor

• Statistical Methodology
• Computational Algorithms
• Bayesian Modeling
• +7 more

Yang Chen is an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute for Data Science (MIDAS), University of Michigan. Her research focuses on statistical methodology and computational algorithms for scientific applications, including Bayesian modeling, hidden Markov models, and applications in astronomy, space science, and epidemiology. She holds a Ph.D. in Statistics from Harvard University and a B.Sc. in Mathematics from the University of Science and Technology of China. Education: Ph.D. in Statistics, Harvard University, 2017 B.Sc. in Mathematics, University of Science and Technology of China, 2011 Research Interests: Her work emphasizes computational algorithms for statistical inference, with applications in astronomy (e.g., solar flare prediction, instrument calibration) and epidemiology. Key areas include spatial-temporal data analysis, uncertainty quantification, and machine learning for large-scale imaging data. Grants & Funding: Principal Investigator/Co-Investigator on multiple NSF, NASA, and industry grants totaling over $13M, including space weather modeling, solar physics, and data science initiatives. Teaching: Teaches advanced courses in Bayesian data analysis, statistical modeling, and computational methods at the University of Michigan. Professional Activities: Associate Editor for The American Statistician , Statistical Science , and Journal of the American Statistical Association . Active in organizing conferences and seminars on astrostatistics, machine learning, and space weather.

Gongjun XuView profile

Associate Professor

• Latent Variable Models
• Psychometrics
• Statistical Learning
• +6 more

Gongjun Xu is an Associate Professor of Statistics and courtesy Associate Professor of Psychology at the University of Michigan. He leads the Master's Program in Applied Statistics and holds positions in the Department of Statistics within the College of Literature, Science, and the Arts (LSA). His research focuses on latent variable models, psychometrics, statistical learning, and high-dimensional statistics. Xu completed his Ph.D. in Statistics at Columbia University and B.S. at the University of Science and Technology of China (USTC). His research interests include advanced statistical methodologies for educational and psychological measurement, such as cognitive diagnosis models, item response theory, and network data analysis. He has contributed to high-dimensional inference, survival analysis, and machine learning applications in healthcare. Xu serves as Co-Editor-in-Chief of the Journal of Educational and Behavioral Statistics and holds editorial roles in multiple top-tier journals. Xu has been recognized with prestigious awards including the AERA Research Methodology Award (2025), ICSA President’s Citation (2024), and COPSS Emerging Leader Award (2023). His team advises numerous graduate and undergraduate students, many of whom pursue academic and industry roles in statistics, data science, and education. Key collaborations include work on educational testing, health analytics, and AI-driven scientific methods via the Schmidt AI in Science Fellowship. He oversees a vibrant research group with active projects in latent variable modeling, differential item functioning detection, and scalable Bayesian methods for large-scale assessments. His lab emphasizes interdisciplinary applications spanning education, healthcare, and social sciences.

• Machine Learning
• Stochastic Optimization
• Networked Systems
• +4 more

Juan Bazerque Giusto is a Visiting Assistant Professor at the Department of Electrical and Computer Engineering, University of Pittsburgh, within the Swanson School of Engineering. He holds a B.Sc. in Electrical Engineering from Universidad de la República (Uruguay), and M.Sc. and Ph.D. degrees from the University of Minnesota. His research focuses on machine learning, stochastic optimization, and networked systems, with emphasis on reinforcement learning, swarm robotics, and power systems optimization. Education: B.Sc., Electrical Engineering, Universidad de la República, 2003 M.Sc., Electrical and Computer Engineering, University of Minnesota, 2010 Ph.D., Electrical and Computer Engineering, University of Minnesota, 2013 His work bridges theoretical advancements in optimization and signal processing with practical applications in robotics, energy systems, and wireless networks. Notable contributions include multiagent systems for mobile infrastructure, safe reinforcement learning algorithms, and sparse kernel-based methods for signal recovery. Publications: Over 15 peer-reviewed articles in IEEE Transactions and top conferences, emphasizing interdisciplinary research in reinforcement learning, distributed optimization, and cognitive networks. Recent work explores networked robotics and energy-efficient datacenter management. Awards: University of Minnesota Master Thesis Award (2009-2010) Best Paper Award at ICCRON 2007 Professional Experience: Previously served as Assistant Professor at Universidad de la República (Uruguay) before relocating to the U.S. in 2022.

Juan RestrepoView profile

Professor

• Applied Mathematics
• Computational Mathematics
• Data Science
• +12 more

Juan Restrepo is a Professor of Mathematics with courtesy appointments in Statistics, EECS, and Physical Oceanography at Oregon State University, where he holds a Courtesy Faculty position in the Department of Mathematics within the College of Science. He is also affiliated with the University of Tennessee and Oak Ridge National Laboratory, where he holds appointments in the Computer Science and Mathematics Division. He serves as Co-Director of the Dynamics and Data Science Institute (D2SI), a leading interdisciplinary research center. Ph.D. in Physics, Pennsylvania State University, 1992 M.S. in Engineering (Acoustics), Pennsylvania State University, 1987 B.S. in Music, New York University, 1983 Restrepo's research lies at the intersection of data science and dynamics, with two primary tracts: (1) applying statistical physics and data science to complex non-equilibrium systems such as climate and financial markets; and (2) studying ocean dynamics and transport in climate and nearshore processes. His work emphasizes uncertainty quantification, adaptive time series analysis, extreme events, and data assimilation. His recent publications reflect a strong focus on modeling geophysical systems under uncertainty, using advanced computational and statistical techniques. Themes include climate sensitivity, oil spill dispersion, sediment transport, wave breaking, and high-performance computing for large-scale simulations. His work integrates machine learning, stochastic modeling, and dynamical systems theory. SIAM Fellow SIAM Geosciences Career Award ORISE Distinguished Post-doctoral Fellow DOE Young Investigator Award Ruth Homeyer Graduate Student Award Restrepo has secured over $30 million in research funding from NSF, DOE, NASA, and GoMRI. He has advised numerous students in applied mathematics and computational sciences, and has held leadership roles in professional societies, including President of the Nonlinear Geophysics Section at the American Geophysical Union. He is actively involved in promoting diversity in science and mentoring underrepresented groups. He leads interdisciplinary research teams at D2SI and collaborates with national labs such as Argonne and Los Alamos. His work bridges applied mathematics, climate science, oceanography, and computational engineering, making significant contributions to both theory and real-world applications.

Milica TodorovicView profile

Visiting Professor

• Machine Learning
• Materials Science
• Quantum Mechanics
• +6 more

Milica Todorovic is a Visiting Professor at Aalto University's Department of Applied Physics , leading machine learning research within the Computational Electronic Structure Theory (CEST) group. Her work bridges quantum mechanical simulations and machine learning algorithms to optimize material functionality, particularly for solar cell components and organic-inorganic interfaces . Research Interests include: Data-driven materials science Active learning for molecular datasets Bayesian optimization in atomic structure prediction Quantum simulations of surfaces and adsorbates Thermodynamic property modeling for atmospheric molecules Article Trends (2017-2025) show focus on machine learning applied to materials science , with subfields spanning bayesian optimization , conformational analysis , and density functional theory . Key collaborations include Patrick Rinke and Hanna Vehkamäki . Activities include organizing workshops like the Young Researcher’s Workshop on Machine Learning for Materials Science (2019) and International Workshop on Machine Learning for Materials Science (2018).

Victor ChernozhukovView profile

Professor

• High-Dimensional Statistics
• Econometrics
• Quantile Regression
• +3 more

Victor Chernozhukov is a Professor at the Department of Economics and Center for Statistics at the Massachusetts Institute of Technology (MIT) . He also holds international fellowships at the University College London's CEMMAP and is a Professor by Courtesy at the New Economic School in Russia. His research spans econometrics, high-dimensional statistics, quantile regression, and causal inference. Education: Ph.D. in Economics from Stanford University (2000), M.S. in Statistics from University of Illinois at Urbana-Champaign (1997) His work focuses on developing statistical methods for high-dimensional data, including central limit theorems , post-selection inference , and quantile regression . He has contributed to partial identification , Bayesian inference , and extreme value analysis , with applications to economic policy evaluation. Recent publications emphasize high-dimensional causal inference , adaptive confidence bands , and set estimation . His research has been supported by the National Science Foundation and recognized with the Alfred P. Sloan Research Fellowship . Scientific Awards: Alfred P. Sloan Research Fellowship (2005-2007) Castle-Krob Career Development Chair (2004-2007) Grants include long-term support from the National Science Foundation since 2001 and software development collaborations for Stata and MATLAB implementations.

Kislaya RaviView profile

Researcher

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +2 more

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

Thierry KleinView profile

Professor

• Probability Theory
• Statistics
• Uncertainty Quantification
• +4 more

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.

• Machine Learning
• Bandit Algorithms
• Reinforcement Learning
• +2 more

Hamish Flynn is a Researcher in the Department of Engineering Artificial Intelligence and Machine Learning. His work focuses on developing advanced algorithms for sequential decision-making systems, with a strong emphasis on theoretical guarantees and practical implementations. His research interests span several core areas of machine learning: Bandit algorithms (contextual, linear, and multi-armed variants) PAC-Bayesian theory and applications Reinforcement learning systems Online optimization under uncertainty Statistical learning theory Flynn's recent publications (2022-2025) demonstrate consistent focus on improving sequential learning methods. Key research themes include: non-iid noise modeling in bandits, confidence bound optimization for sequential regression, sparse nonparametric methods, and hardware-efficient machine learning implementations. His work frequently combines theoretical frameworks (PAC-Bayes, martingale theory) with practical applications in reinforcement learning and adaptive systems. No scientific awards, students advised, grant activities, or lab affiliations are mentioned in the available information.

Jake SoloffView profile

Assistant Professor

• Statistical Machine Learning
• Empirical Bayes
• Nonparametric Estimation
• +6 more

Jake Soloff is an Assistant Professor of Statistics at the University of Michigan. He earned his Ph.D. in Statistics from UC Berkeley in 2022 under advisors Adityanand Guntuboyina and Michael Jordan, followed by a postdoctoral fellowship at the University of Chicago with Rina Foygel Barber and Rebecca Willett. Research Focus: Statistical machine learning, algorithmic stability, empirical Bayes methods, and theoretical analysis of calibration and false discovery rate control. Key Contributions: Developed the Inflated Argmax for stable classification, proposed Cutoff Calibration Error metrics, analyzed distribution-free properties of isotonic regression, and created resampling techniques for stability guarantees. Publication Trends : Recent work emphasizes stability in machine learning pipelines, empirical Bayes optimization for high-dimensional problems, and novel approaches to hypothesis testing with stochastic monotonicity constraints. Software Development : Maintains the NPEB Python package for nonparametric empirical Bayes estimation.

Steven CulpepperView profile

Professor

• Psychometrics
• Latent Class Models
• Statistical Computing
• +3 more

Steven Culpepper is a Professor at the University of Illinois at Urbana-Champaign with joint appointments in the Department of Educational Psychology (College of Education), Department of Statistics, Department of Psychology, and Beckman Institute for Advanced Science and Technology. He holds a PhD in Educational Psychology from the University of Minnesota (2006) and BS in Economics from Bowling Green State University (2001). His research program focuses on developing advanced psychometric models and statistical methods for educational and psychological measurement. Primary research areas include: Latent class and hidden Markov models for cognitive diagnosis Large-scale testing and assessment methodologies Bayesian inference for restricted latent class models Statistical computing applications for psychometrics Longitudinal modeling of learning trajectories His recent publications demonstrate consistent focus on improving parameter estimation for diagnostic classification models, with methodological innovations in Bayesian computation, model identifiability, and handling of polytomous response data. Research consistently addresses fundamental challenges in educational measurement through rigorous statistical frameworks.

Stefan Van AelstView profile

Professor

• Robust statistics
• Analysis of complex data
• Functional data analysis
• +5 more

Stefan Van Aelst is a Professor at the Department of Mathematics, KU Leuven, and heads the Statistics and Data Science unit. He is actively involved in research on robust statistical methods for complex and high-dimensional data. Affiliation: Faculty of Science, KU Leuven Research: Robust statistics, functional data, fuzzy data, and applications in chemometrics and bioinformatics Teaching: Advanced Statistical Methods, Robust Statistics, Statistical Data Analysis His recent publications focus on robust dispersion estimation, sparse logistic regression, functional data analysis, and multilinear PCA. Collaborations include researchers like Rousseeuw, Zamar, and Verdonck. He contributes to software development, including the MATLAB library LIBRA and R packages like robustbase and cellWise , and participates in organizing seminars and conferences.