Find Academic Supervisors

• Numerical Analysis
• Stochastic Differential Equations
• Partial Differential Equations
• +3 more

Håkon Andreas Hoel serves as an Associate Professor in the Department of Mathematics at the University of Oslo, specializing in numerical methods for stochastic and partial differential equations, Monte Carlo techniques, and data assimilation. His work bridges theoretical probability with practical computational challenges in scientific modeling. His academic credentials include a PhD in Numerical Analysis from the Royal Institute of Technology (KTH) in Stockholm (2007-2012), preceded by a Master's (2006) and Bachelor's (2004) in Computational Science from the University of Oslo. Professional experience spans postdoctoral roles at KAUST, EPFL, and UiO, along with a junior professorship at RWTH Aachen (2019-2022). Research centers on developing efficient algorithms for uncertainty quantification, particularly multilevel Monte Carlo methods and ensemble Kalman filtering. His publications demonstrate consistent innovation in reducing computational costs for high-dimensional stochastic simulations while maintaining accuracy, with applications across natural sciences and engineering disciplines. Analysis of recent publications reveals a strong trajectory toward adaptive multilevel frameworks for spatio-temporal data assimilation, integrating statistical inference with numerical solution techniques for complex stochastic systems. This work emphasizes theoretical rigor alongside practical implementation challenges. No scientific awards or honors were documented in the source materials. The provided texts contain no information regarding graduate students supervised or research grants administered by Dr. Hoel. He is actively affiliated with the Computational Mathematics research group at UiO, which focuses on differential equations and computational methods within the Department of Mathematics.

Tobias WindischView profile

Professor

• Machine Learning
• Computer Vision
• Reinforcement Learning
• +4 more

Prof. Dr. Tobias Windisch is a Professor at the University of Applied Sciences Kempten, where he serves as head of the Institute for Machine Vision within the Faculty of Mechanical Engineering. He leads the Optical 3D Measurement and Computer Vision Laboratory (3D visionlab) and oversees research activities focused on machine learning applications for industrial automation. Dr. Windisch received his PhD in mathematics from OvGU Magdeburg under the supervision of Thomas Kahle, and holds an Honors Master's degree in mathematics from TU Munich within the elite TopMath program. Prior to his academic career, he worked on machine learning projects for Robert Bosch GmbH and Daimler TSS GmbH (now Mercedes-Benz Tech Innovation). His research spans machine learning, computer vision, and optical sensing with a strong focus on industrial applications. Windisch's work primarily explores how reinforcement learning can be combined with optical sensing to develop intelligent control strategies for manufacturing processes. His team develops mechanical processes built around machine learning models to further automate industrial applications using data from optical sensors. The research has practical applications in automotive production, quality control, and precision manufacturing. Analysis of his recent publications reveals a strong trend toward practical implementations of machine learning in industrial settings, with particular emphasis on reinforcement learning for process optimization, drift detection in high-dimensional data, and causal structure learning for manufacturing analytics. His work bridges theoretical machine learning with real-world industrial challenges. As a dedicated educator and research leader, Windisch maintains high standards for academic integrity and excellence. He believes in creating an environment where students can focus deeply, think boldly, and innovate through meaningful research. Dr. Windisch leads a dynamic research group with numerous Master's and Bachelor's students working on cutting-edge projects including reinforcement learning for active alignment, drift detection in sensory data, latent drift detection with Autoencoders, and representation learning for industrial processes. His laboratory, the 3D visionlab, serves as the physical hub for this research. The Institute for Machine Vision under his leadership develops practical tools and frameworks such as relign, lineflow, and driftbench that are openly available on GitHub, demonstrating his commitment to reproducible research and practical applications.

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +1 more

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.

Saharon RossetView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +3 more

Saharon Rosset is a Professor and Chair in Modern Statistics and Data Science at the Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, since 2007. His expertise spans statistical learning theory, machine learning, and computational biology, with a focus on integrating statistical methods into biological and data science applications. Research Interests: Statistical Learning, Computational Biology, Genomics, Data Science Teaching: Statistics of Big Data, Statistical Genetics, Bootstrap Methods, Data Science His recent publications explore diverse applications including ATP production mechanisms, SARS-CoV-2 evolution, gene-environment interactions, and music structure analysis. He employs methodologies from high-dimensional statistics, random effects modeling, and multiple hypothesis testing. Scientific Awards : Best paper award at KDD-2011 KDD Cup 2007 Winner Best application paper award at KDD-02 He has mentored numerous PhD and MSc students, including Oren Yuval, Giora Simchoni, and Rajesh Karmakar. His collaborative work appears in journals like PNAS , Biometrics , and Journal of Machine Learning Research .

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +8 more

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

Angelos AlexopoulosView profile

Assistant Professor

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +4 more

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).

Lu TangView profile

Associate Professor

• Biostatistics
• Machine Learning
• Health Data Science
• +7 more

Lu Tang is an Associate Professor and Vice Chair for Education in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. Her research bridges biostatistics and machine learning to advance health data science through integrative analysis of distributed and high-dimensional datasets, with significant applications in precision medicine, opioid use disorder, sepsis treatment, and public health policy. Dr. Tang's educational journey includes foundational training at Sun Yat-sen University followed by advanced degrees in quantitative fields: PhD in Biostatistics, University of Michigan (2018) MS in Statistics, University of Virginia (2013) BA in Mathematics, University of Virginia (2012) Her research program develops statistical methodologies for data integration, causal inference, and decision-making in healthcare settings. She pioneers techniques for federated learning, robust individualized treatment rules, and high-dimensional data analysis, directly addressing challenges in health disparities, electronic health records, and clinical trial heterogeneity. Her work consistently translates methodological innovations into practical tools for biomedical researchers. Analysis of Dr. Tang's publication trajectory reveals a strategic evolution toward solving real-world healthcare data challenges through distributed learning frameworks. Her recent work emphasizes robust decision rules for heterogeneous populations (2022-2025), transfer learning for federated systems (2024), and methodological innovations for longitudinal and high-dimensional data (2019-2023), demonstrating consistent contributions across biostatistics, machine learning, and public health domains. Dr. Tang has been recognized with: IMS New Researchers Travel Award (2023) She actively mentors 6 current graduate students across PhD and MS programs while supervising 8 alumni now in industry and academia. Her research program is sustained by major funding including NIH R21DA055672 (PI, 2023-2025) on federated Medicaid data analysis, NSF DMS 2310217 (PI, 2023-2026) for longitudinal missing data methods, and multiple NIH R01 grants as Co-I addressing opioid treatment equity and precision medicine in sepsis. Dr. Tang leads the development of critical open-source software including RISE (Python) for robust individualized decisions, ifedtree (R) for tree-based federated learning, and metafuse (R/CRAN) for data integration, establishing infrastructure widely adopted in health data science research communities.

Calvin TsayView profile

Lecturer

• Optimization Algorithms
• Process Control
• Machine Learning Optimization
• +2 more

Calvin Tsay is a Lecturer (Assistant Professor) in the Computational Optimisation Group at Imperial College London, holding the BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling. Education: PhD Chemical Engineering (UT Austin 2020), BS/BA (Rice University 2015). Research develops optimization methods bridging machine learning and process systems engineering. Specializes in mixed-integer programming for neural networks and Bayesian optimization for energy applications. Awards: President's Medal for Early Career Researcher RAEng Senior Research Fellowship CACE Best Paper (2023) COIN-OR Cup (2022) Leads research on AI-driven chemical process optimization. Supervises PhD students in ML optimization and process control. Collaborates with BASF on industrial applications.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +5 more

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

Ron YurkoView profile

Academic

• Sports Analytics
• Natural Language Processing & Large Language Models
• Biostatistics & Epidemiology
• +4 more

Ron Yurko is an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University , where he focuses on methods at the intersection of statistical inference and machine learning. He serves as Director of the Carnegie Mellon Sports Analytics Center (CMSAC) , overseeing initiatives like the sports analytics research lab, CMSACamp , and the annual Sports Analytics Conference. Research Interests His work bridges Sports Analytics and Natural Language Processing , with applications in Biostatistics , Public Policy , and Statistical Pedagogy . Recent projects include evaluating NFL player positioning ( NFL Ghosts ), analyzing language patterns in LLM outputs, and developing fractional tackle metrics for defensive evaluation. Google Scholar articles reveal deeper methodological contributions to Bayesian modeling for directional movement analysis, multilevel models for snap timing, and spatio-temporal frameworks in football event modeling. Academic Background PhD in Statistics (2022) from Carnegie Mellon University BS in Statistics with University Honors (2015) from Carnegie Mellon Additional Contributions He is developing a textbook Statistical Methods in Sports Analytics (2027), maintains the Statistical Thinking in Sports Analytics newsletter, and teaches course 36-460/660 at CMU.

Martin EigelView profile

Researcher

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +5 more

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

Chi WangView profile

Professor

• Cancer Biostatistics
• Bioinformatics
• Survival Analysis
• +10 more

Chi Wang, Ph.D., is Professor of Internal Medicine and Statistics at the University of Kentucky, Associate Director of the Biostatistics & Bioinformatics Shared Resource, and a lead computational scientist within the Markey Cancer Center. With an h-index of 39 and >5,300 citations, he develops cutting-edge survival-analysis and high-dimensional-omics methods that are translated into prostate, breast, lung and neuroblastoma trials. Education: Ph.D. Biostatistics, Johns Hopkins University, 2009 M.S. Statistics, Peking University, 2003 B.S. Mathematics, Peking University, 2001 Research Focus: Wang’s group integrates Bayesian statistics, machine-learning and single-cell sequencing to model tumor evolution, drug resistance and biomarker-driven adaptive trials. Major themes include enzalutamide resistance in prostate cancer, PLK1-mediated immune evasion in lung adenocarcinoma, obesity-adipose drivers of breast cancer, and epigenetic reprogramming of tumor-infiltrating lymphocytes. Recent Article Trends (2023-2025): Fifteen showcased papers reveal a dual trajectory—(1) mechanistic dissection of kinases (PLK1, DDR1, ABL1/2) and metabolic enzymes (FASN, NNMT, HSP47) that orchestrate resistance and metastasis, and (2) probabilistic algorithms for temporal mutation ordering, biomarker evaluation and single-cell deconvolution. Overarching keywords are precision oncology, drug resistance, tumor micro-environment, and computational systems biology. Funding & Awards: He is PI/co-I on 152 grants (32 active) from NCI, ACS, V Foundation, etc., totaling >$30 M direct costs. Highlights include NCI U01 for predictive biomarkers, American Cancer Society award on collagen lysyl hydroxylase metastasis drivers, and NCI R01s targeting NNMT in triple-negative breast cancer and TIL epigenetic reprogramming in NSCLC. Student & Team Mentorship: Wang trains a large multi-disciplinary cohort of Ph.D. students, post-docs and clinical fellows who routinely lead first-author papers and are funded on his grants. His lab sits within the Markey Cancer Center’s Molecular & Cellular Oncology program and the Center for Computational Sciences, fostering daily bench-to-bedside collaboration.

• Statistics
• Biostatistics
• Medical Statistics
• +13 more

Angela Andreella is an Assistant Professor in the Department of Economics and Management at the University of Trento. She holds a Ph.D., M.Sc., and B.Sc. in Statistics from the University of Padova. Her prior roles include Assistant Professor at Ca' Foscari University of Venice (2022–2024) and postdoctoral positions at the Universities of Padova and Insubria. Research Interests: Her work spans multivariate analysis, selective inference, social statistics, and permutation tests, with applications in biostatistics, neuroscience, and data analytics. She specializes in high-dimensional data, robust statistical methods, and quantitative approaches to social and medical research. Professional Memberships: Italian Statistical Society Italian Society of Biometry International Society of Biometry International Organization for Human Brain Mapping Institute of Mathematical Statistics Italian Association of Psychology

Alex TongView profile

Assistant Professor

• Generative Modeling
• Deep Learning
• Optimal Transport
• +4 more

Alex Tong is an incoming Assistant Professor at Duke University (as of 2025) and will join Aithyra in Vienna as a Principal Investigator. Previously, he completed postdoctoral research at Mila under Yoshua Bengio and a visiting postdoc at Oxford with Michael Bronstein. Education : PhD (2021) and MPhil (2020) in Computer Science from Yale University; BS/MS (2017) from Tufts University Research Interests span generative modeling , deep learning , optimal transport , and graph signal processing applied to protein design and single-cell biology . His work bridges mathematical formalism (e.g., SE(3) flows, Wasserstein manifolds) with biological discovery. Publication Trends (2023-2025) focus on diffusion models for protein structure prediction , optimal transport in single-cell analysis , and geometric machine learning for molecular dynamics . Collaborations include labs like Mila, Oxford, and Yale. Awards : Best Paper (Gen Bio @ ICML 2025), Outstanding Paper (DELTA @ ICLR 2025), Best Student Paper (IEEE MLSP 2020)