Find Academic Supervisors

Nikola SimidjievskiView profile

Research Fellow

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +4 more

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

Matey NeykovView profile

Assistant Professor

• High-Dimensional Inference
• Statistical Machine Learning
• Nonparametric Estimation
• +4 more

Matey Neykov is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University . Previously, he was on the faculty at Carnegie Mellon University and held a postdoctoral position at Princeton University under Han Liu. He earned his Ph.D. in Biostatistics from Harvard University, advised by Jun S. Liu and Tianxi Cai, and a B.S. in Applied Mathematics from Sofia University, Bulgaria. Research Interests: Neykov focuses on high-dimensional nonparametric estimation, variable selection, graph property testing, and statistical applications in medicine such as electronic medical records (EMRs) and personalized medicine. His work bridges theoretical advancements in machine learning and practical medical data analysis. Education: B.S., Applied Mathematics, Sofia University Ph.D., Biostatistics, Harvard University (2015) Professional History: Postdoctoral Researcher, Princeton University (Department of Operations Research and Financial Engineering) Faculty, Carnegie Mellon University (2017–202X) Current Position: Assistant Professor, Northwestern University No specific scientific awards or grants are explicitly listed in the provided texts. His research emphasizes both foundational statistical theory and medical applications, though no lab affiliations or team details are mentioned beyond his academic roles.

Maggie MakarView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Healthcare Applications
• +3 more

Maggie Makar is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of Michigan. Her research focuses on the intersection of machine learning and causal inference, particularly in developing robust and data-efficient methods for healthcare and resource-constrained settings. She received her PhD from MIT's CSAIL and a bachelor's degree in math and economics from UMass Amherst. Her work emphasizes causal reasoning to improve predictive models' reliability, with applications in healthcare decision-making and policy evaluation. Key areas include offline reinforcement learning for medical interventions, confounding variable handling, and mitigating biases in AI systems. Dr. Makar teaches courses such as CSE 598 (Causality and Machine Learning) and mentors PhD students in causal ML and healthcare AI. She actively seeks to expand her research group and collaborate on projects blending causal theory with real-world applications. Education: PhD in Computer Science, MIT (CSAIL), 2018 Bachelor's in Mathematics and Economics, University of Massachusetts Amherst, 2013 Awards: CAREER Award (NSF, 2024) CRII: SCH Grant (NSF, 2022) Research Themes: Causal inference in machine learning Data-efficient methods Healthcare applications Algorithmic fairness

Fabian ScheiplView profile

Researcher

• Functional Data Analysis
• Statistical Modeling
• Machine Learning
• +3 more

PD Dr. Fabian Scheipl is the Head of the Department of Statistics at Ludwig-Maximilians-Universität München (LMU). His work focuses on cutting-edge statistical methodologies, particularly in Functional Data Analysis, and he leads the Functional Data Analysis research group. He specializes in developing advanced statistical models and open-source software tools for data science applications. His research bridges theoretical statistics with practical implementations, emphasizing reproducible research and educational resources. Dr. Scheipl’s research interests include functional additive mixed models, cluster analysis, and high-dimensional data techniques. He has contributed significantly to R package development, including refund , gamm4 , and lme4 , which are widely used in statistical computing. His work spans diverse fields such as healthcare (e.g., analyzing critical care outcomes), environmental science (e.g., earthquake dynamics), and computational biology (e.g., epigenetic studies). His publications highlight innovations in statistical methodology, such as improving cluster validation metrics and advancing topological approaches to outlier detection. While no awards are explicitly listed, his impactful contributions to statistical software and theory are widely recognized in academic circles. He actively engages in advising doctoral candidates and contributes to teaching and mentorship in statistical education. Labs/Teams: Leads the Functional Data Analysis group within LMU’s Department of Statistics, fostering interdisciplinary collaborations across computational, biomedical, and environmental research domains.

Lihua ChenView profile

Professor

• Statistics
• Bayesian Methods
• Categorical Data Analysis
• +3 more

Lihua Chen is a Professor of Statistics at the Department of Mathematics & Statistics, James Madison University, since 2009. His research focuses on model combining methodologies, categorical data analysis, Bayesian approaches, and statistical procedure integration. He teaches courses such as StatAssess in Spring 2025. Education: PhD in Statistics, Iowa State University (2005) MS in Finance, Beijing University (1993) BS in Economics, Beijing University (1990) His research interests emphasize advancing statistical frameworks for complex data structures and their applications. Notable contributions include work on factorial data analysis and econometric problem-solving techniques. His publications span theoretical advancements and applied methodologies across statistics and economics. Dr. Chen maintains a professional website at educ.jmu.edu/~chen3lx , which includes teaching materials and datasets like the 'StatAssess' resources.

• Statistics
• Statistical Inference
• Time-Series Analysis
• +3 more

Emanuele Taufer is a Full Professor of Statistics at the Department of Economics and Management of the University of Trento. His academic career includes roles as Vice Director of the Department of Computer Science and Business Studies and Faculty Delegate for International Relations. He holds a Ph.D. in Statistics from Cardiff University, an M.Sc. in Mathematical Statistics from George Washington University, and a Laurea in Economics from the University of Trento. His research focuses on statistical inference, stochastic processes, goodness-of-fit tests, and applications in ESG analysis. Notable contributions include work on exponentiality testing, graphical models, and financial dependence modeling. He has been recognized for his 2002 paper on mean residual life characterization at the SIS2002 conference. Recent research trends emphasize methodological advancements in ESG performance measurement, sparse network estimation for heavy-tailed data, and generalized precision matrices for financial risk modeling. His work spans theoretical statistics, applied econometrics, and interdisciplinary topics like environmental governance. Education: Ph.D. in Statistics, Cardiff University (UK) M.Sc. in Mathematical Statistics, George Washington University (USA) Laurea in Economics, University of Trento (Italy) Professional Roles: Full Professor of Statistics at University of Trento (2003–present) Associate Professor (2003–2003), Assistant Professor (1996–2002) Awards: 2002 SIS2002 Recognition for innovative statistical testing methodology Key Research Themes: Stochastic processes and estimation ESG methodology and financial reporting High-dimensional data analysis Goodness-of-fit tests and tail index estimation

• Machine Learning
• Statistical Learning
• Bayesian Methods
• +5 more

Pierre ALQUIER is a Professor at ESSEC Business School (Singapore) since 2023, specializing in statistical learning and machine learning. Previously, he held professorships at ENSAE Paris (2014–2019) and the University of Dublin (2012–2014). He earned his PhD in Mathematical Statistics from Pierre and Marie Curie University in 2006, with a focus on advanced statistical methodologies. His research centers on Bayesian methods, PAC-Bayes bounds, high-dimensional data analysis, and robust estimation, with applications in quantum computing and time series. He has authored over 60 peer-reviewed articles, including influential works on kernel mean embeddings and meta-learning. Alquier has received the 2019 Best Paper Award at the Asian Conference on Machine Learning. He actively contributes to academic leadership, serving as an associate editor for leading journals like the Journal of Machine Learning Research and organizing international workshops. His educational contributions include co-supervising multiple doctoral theses on topics like robust Bayesian inference and non-negative matrix factorization. Education: PhD in Mathematical Statistics (2006), Pierre and Marie Curie University MSc in Probability Theory and Statistics (2003), Pierre and Marie Curie University Diploma in Statistician-Economist (2003), ENSAE Research Focus: Machine learning theory, PAC-Bayes bounds, Bayesian computation, high-dimensional statistics, quantum tomography, and time series forecasting. Grants & Activities: Member of key academic societies (IMS, SFdS), reviewer for top conferences (NeurIPS, ICML), and organizer of workshops on approximate Bayesian inference and high-dimensional data analysis. His recent work emphasizes robust regression, meta-learning, and the theoretical foundations of deep learning, often addressing challenges in dependent data and model misspecification. He has developed R packages like regMMD for robust statistical estimation.

François BachocView profile

Assistant Professor

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

François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.

Nancy ReidView profile

Professor

• Statistical Theory
• Mathematical Statistics
• Asymptotic Methods
• +6 more

Nancy Reid is a University Professor in the Department of Statistical Sciences at the University of Toronto, Canada. Her academic leadership includes roles such as OC (Order of Canada), FRS (Fellow of the Royal Society), and FRSC (Fellow of the Royal Society of Canada). She specializes in foundational statistical theory, asymptotic methods, likelihood inference, and Bayesian-frequentist comparisons. Reid's research bridges theoretical advancements and practical applications in biostatistics, machine learning, and interdisciplinary data science. Her work emphasizes robust statistical methods for high-dimensional and complex data, including contributions to partial likelihood (Cox model), saddlepoint approximations, and accuracy in directional inference. Key recognitions include the COPSS Distinguished Achievement Award, Gold Medal from the Statistical Society of Canada, and numerous editorial and advisory roles. Reid collaborates globally, delivering invited lectures at institutions like Harvard, Stanford, and the Royal Society. Reid's academic output spans over 30 years, with influential books like Theory of the Design of Experiments (with D.R. Cox) and Applied Asymptotics . Her recent focus includes replicability in data science, foundations of statistical inference, and bridging gaps between theoretical and applied statistics. She actively promotes methodological rigor and interdisciplinary collaboration through initiatives like the Canadian Statistical Sciences Institute.

• Statistical Methodology
• Latent Variable Models
• Tensor Decompositions
• +3 more

Zhongyuan Lyu is a Research Fellow (Postdoctoral Research Scientist) at Columbia University's Data Science Institute, mentored by Professors Yuqi Gu and Kaizheng Wang. His research focuses on statistical methodology for latent structures in mixture models, graphical models, and tensor decompositions, with applications to heterogeneous data analysis. Prior to Columbia, he earned his PhD in Mathematics from the Hong Kong University of Science and Technology under Professor Dong Xia's supervision. His academic background includes advanced work in high-dimensional data analysis, latent variable modeling, and computational statistics. Research interests emphasize developing theoretically grounded algorithms for complex data types, particularly in network science and multilayer data frameworks. Recent publications highlight contributions to spectral clustering optimization, adaptive transfer learning frameworks, and tensor-based methodologies for higher-order networks. His work bridges statistical theory and practical applications, addressing computational limits and optimal estimation challenges in modern data science problems. No scientific awards or grants are explicitly mentioned in the provided data. His current position is full-time within the Data Science Institute's research team.

Indranil MukhopadhyayView profile

Professor

• Biostatistics
• Statistical Genetics
• Genomics
• +3 more

Indranil Mukhopadhyay serves as a Professor in the Department of Statistics at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of statistics, genomics, and public health. His work bridges theoretical statistical methodology with urgent real-world health challenges, particularly in infectious disease modeling and genomic data analysis. His primary research interests encompass biostatistical methodology development for multi-omics integration, epidemiological modeling of infectious diseases (notably HIV/AIDS and COVID-19), and statistical genetics for complex trait analysis. He has pioneered robust computational frameworks for handling high-dimensional biological data, with emphasis on isoform quantification, gene expression clustering, and genetic association studies in diverse populations. Analysis of his recent publications reveals a strong trajectory toward public health applications , where statistical models directly inform pandemic response strategies and disease control policies. His work consistently demonstrates innovation in adapting mathematical frameworks (e.g., critical community size theory) to contemporary health crises, while maintaining rigorous methodological foundations in sequential estimation and robust statistics. No scientific awards were documented in the provided materials. Information regarding student advising, research grants, or laboratory infrastructure was not available in the source text. His publication record suggests active collaboration with medical researchers, particularly in studies involving Indian population cohorts and rare genetic disorders. No details about specific research laboratories or collaborative teams were provided in the available documentation.

Jaeyoung ParkView profile

Assistant Professor

• Healthcare Analytics
• Machine Learning Applications in Healthcare
• Patient Outcome Prediction
• +5 more

Jaeyoung Park is an Assistant Professor in the School of Global Health Management and Informatics at the University of Central Florida's College of Community Innovation and Education. His work focuses on healthcare analytics, machine learning applications in clinical settings, and patient-reported outcome measurement. Key research interests include predictive modeling for surgical outcomes, critical care delivery optimization, and health information technology's impact on patient engagement. He holds an office in DPAC 402O and can be reached at jaeyoung.park@ucf.edu. Research Themes: His studies bridge clinical data analysis with healthcare operations, emphasizing translational research to improve patient care through digital tools and predictive systems. Recent work addresses challenges in ICU care team dynamics, heart failure readmission prediction, and the operational impact of patient portal usage. Publications: His 2021-2025 articles reveal a focus on machine learning applications for clinical decision support, patient outcome prediction, and healthcare system optimization. He explores topics ranging from digital twin models in critical care to mitigating silent abandonment in healthcare contact centers.

Nicolas JouvinView profile

Researcher

• Model based clustering
• Dimension reduction
• Variational inference
• +4 more

Nicolas Jouvin is an INRAE researcher at the MIA-Paris laboratory specializing in applied mathematics and statistics. He is based at AgroParisTech Saclay (Bureau E4.512, Bâtiment E, 22 place de l'agronomie, 91120 Palaiseau) and works within the MIA-Paris-Saclay research unit. His educational background includes a PhD in Applied Mathematics (2017-2020) from Université Paris 1 Panthéon-Sorbonne and an MVA degree (2016) from ENS Paris Saclay. His doctoral research focused on high-dimensional data and graph clustering with discrete latent variable models under the supervision of Pr. Pierre Latouche, Pr. Charles Bouveyron, and Dr. Alain Livartowski, with collaboration at Institut Curie. Jouvin's research centers on probabilistic models for unsupervised learning, particularly model-based clustering and dimension reduction techniques. His work incorporates variational inference, sparse regularisation, and high-dimensional statistics with applications to medical data analysis. He has developed methodologies for hierarchical clustering using discrete latent variable models and the integrated classification likelihood criterion. His publications demonstrate expertise in developing algorithms for clustering count data through multinomial PCA mixtures and discriminative Gaussian subspace clustering using Bayesian approaches. The Greed R package, which implements some of his methodological contributions, was developed in collaboration with Etienne Côme. Charged de cours for Master 2 Data Science (Evry) - Unsupervised learning Master 2 data science (Evry) - Introduction à Python Master 1 data science (Evry) - Analyse des données TD de M1 MAEF Python TD de L3 MIAGE Technique de calcul TD de L1 MIASHS

Aldo SOLARIView profile

Professor

• Statistics
• Statistical Inference
• Biostatistics
• +3 more

Aldo Solari is a Full Professor at the Department of Economics of Ca' Foscari University of Venice. His research focuses on statistical inference, conformal prediction, and biostatistics, with applications in genomics, medical decision-making, and neuroscience. He teaches courses in statistics for economics, probability, and data science. Recent publications include advancements in false discovery rate control and hybrid human-AI collaboration in medical diagnostics. Solari co-edits the Proceedings of Machine Learning Research (PMLR) and serves as a referee for the US-Israel Binational Science Foundation. His work integrates theoretical developments with practical software tools for statistical analysis. Teaching: STATISTICA, INTRODUCTION TO PROBABILITY FOR ECONOMICS, DATA ANALYTICS FOR BUSINESS AND SOCIETY Research Themes: Multiple Testing, Conformal Prediction, Biomedical Applications, Neuroimaging Analysis Publications span peer-reviewed journals like Biometrika and Journal of the Royal Statistical Society , emphasizing methodological contributions with real-world impact. Solari’s projects include funding from Italy-Israel collaborations on high-dimensional inference in neuroscience and genomics.

• Data Science
• Econometrics
• Economics
• +5 more

Univ.-Prof. Martin Wagner is a Professor at the Department of Economics, Alpen-Adria-Universität Klagenfurt, where he serves as Vice-Dean of the Faculty of Management, Economics and Law and Head of the Quantitative Economics Division. His research bridges theoretical and applied econometrics with environmental and macroeconomic challenges. Affiliation: Department of Economics, Faculty of Management, Economics and Law, Alpen-Adria-Universität Klagenfurt Wagner’s work focuses on econometric theory and its applications, particularly in modeling nonlinear cointegrating relationships, environmental economics, and macroeconomic forecasting. His expertise includes: Cointegrating polynomial regressions Unit root processes and stationarity High-dimensional time series analysis Environmental Kuznets curves Panel data econometrics Macroeconomic modeling Recent publications highlight his contributions to robust estimation techniques and the intersection of climate science with economic growth dynamics. Contact: martin.wagner@aau.at