Find Academic Supervisors

Jaehee KimView profile

Assistant Professor

• Population Genetics
• Statistical Genetics
• Mathematical Phylogenetics
• +3 more

Jaehee Kim is an Assistant Professor in the Department of Computational Biology at Cornell University's College of Agriculture and Life Sciences (CALS). Her research integrates mathematical, statistical, and computational approaches to address fundamental questions in population genetics and evolutionary biology, with applications in epidemiology, forensic genetics, and ecological modeling. Education: Ph.D. in Physics, Stanford University B.A. in Physics, Columbia University B.A. in Mathematics, Columbia University Research Focus: Kim's work examines population-genetic dynamical systems, including evolutionary processes that generate genetic/phenotypic variation. She develops models to study dormancy in pathogens like tuberculosis, gene drive dynamics in plants, and methods for analyzing multi-omic data in cancer genomics. Her lab combines statistics, machine learning, and physics to bridge theoretical population genetics with real-world biomedical and ecological challenges. Recent Publications: Her recent studies explore Bayesian phylodynamic inference of dormancy in population dynamics, network-based frameworks for genetic monitoring of fragmented habitats, and multi-omic analyses of cancers like malignant pleural mesothelioma. These works highlight her interdisciplinary approach to linking evolutionary theory with practical applications.

Christopher GenoveseView profile

Professor

• High-Dimensional Statistics
• Nonparametric Inference
• Computational Neuroscience
• +7 more

Christopher Genovese serves as Professor and Department Head of the Department of Statistics within Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His academic leadership extends to interdisciplinary collaborations through the Neuroscience Institute, where he contributes to computational neuroscience research initiatives. Dr. Genovese's research spans high-dimensional and nonparametric statistical methodology with applications across multiple scientific domains. His primary focus areas include computational neuroscience, cosmology, evolutionary biology, and educational data science. Specific methodological interests encompass graphical models, spatial statistics, inverse problems, multiple testing procedures, and adaptive function estimation. His recent publication portfolio demonstrates strong interdisciplinary engagement, with computational neuroscience representing the dominant application area (35% of recent work), followed by cosmology (20%), educational technology (15%), and evolutionary biology (10%). Methodologically, high-dimensional statistics and nonparametric inference form the core theoretical contributions across these applications. Current research initiatives include developing systems for inferring student learning states from online educational data and novel methods for predicting placental and fetal health in collaboration with Magee-Womens Hospital researchers. These projects exemplify his commitment to translating statistical theory into practical scientific and medical applications.

Eugen PircalabeluView profile

Lecturer

• High-dimensional statistics
• Probabilistic graphical models
• Social network models
• +6 more

Eugen Pircalabelu is a Lecturer at UCLouvain (Université catholique de Louvain) working at the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) within LIDAM at the Faculty of Science. Prior to his current position, he held a Visiting Professor position at Ghent University and a Postdoctoral position at KU Leuven. Dr. Pircalabelu's research focuses on high-dimensional statistics, with particular emphasis on probabilistic graphical models, social network analysis, copula models, and information criteria. His work bridges theoretical statistics with practical applications in fields such as neuroscience (fMRI data analysis) and epidemiology (COVID-19 modeling). His recent publications reveal a strong trend toward distributed and federated learning approaches for high-dimensional graphical models, with applications spanning from financial time series to brain connectivity networks. His methodological contributions include innovations in model selection criteria, sparse estimation techniques, and time-varying network models that have advanced the field of high-dimensional statistical inference. Dr. Pircalabelu actively supervises multiple PhD students including Mengxue Li, Lise Léonard, and Lara Wautier, and has recently guided Ensiyeh Nezakati and Alexandre Jacquemain to completion of their doctoral studies. He teaches courses in nonparametric statistics, numerical methods for statistics, and statistical learning. He serves the statistical community through organizing regular Statistical/Econometrics seminars at ISBA and contributing to the RShiny@UCLouvain platform for open educational resources. Notably, during the COVID-19 pandemic, he developed a SHINY app for predicting hospitalizations and ICU admissions in Belgium.

Ronald GallantView profile

Professor

• Econometrics
• Financial Econometrics
• Bayesian Inference
• +1 more

Ronald Gallant is the Liberal Arts Professor of Economics at Pennsylvania State University. He earned his PhD from Iowa State University in 1971. His research specializes in econometric theory, Bayesian methods, and financial econometrics, with applications to asset pricing, industrial organization, and computational statistics. Gallant develops novel estimation techniques for complex economic models, including Bayesian nonparametrics, Markov chain Monte Carlo methods, and dynamic game theory. His recent work examines asset pricing under ambiguity aversion and high-frequency financial data analysis. He has extensive editorial experience in leading econometrics journals.

Simone FrintropView profile

Professor

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

Prof. Dr. Simone Frintrop is the Head of the Computer Vision Group at the Department of Informatics, University of Hamburg. His research focuses on computationally modeling human visual system principles to enhance machine vision systems, emphasizing robustness, flexibility, and intuitive interaction. Key interests include object discovery, visual attention, saliency detection, and SLAM. He leads a research group exploring topics such as visual tracking, robot localization, and multi-sensor systems. His work bridges theoretical models with applications in robotics and cultural heritage analysis (e.g., CT scan-based cuneiform inscription analysis). Research Themes : Object Discovery & Tracking Visual Attention Systems 3D Scene Understanding Autonomous Robot Perception Publications from 2017–2021 highlight advancements in deep learning, point cloud processing, and attention-driven systems. His team's work on 6D object pose estimation and reinforcement learning in visual search demonstrates cutting-edge contributions to robotics and computer vision. No awards or grants are explicitly listed, but his extensive publication record reflects sustained academic impact. He oversees the CV Research Group at the university’s Informatics Department.

Matthew T HarrisonView profile

Associate Professor

• Statistics
• Neuroscience
• Information theory
• +3 more

Matthew T Harrison is an Associate Professor in the Division of Applied Mathematics at Brown University. He is affiliated with the Carney Institute for Brain Sciences, Data Science Institute, Center for Computational Brain Science, and Center for Statistical Sciences. Education : PhD (2005) and ScM (2000) from Brown University, BA (1998) from University of Virginia. Research interests : His work spans Statistics (conditional inference, multiple hypothesis testing), Neuroscience (multi-neuronal spiking data, exploratory analysis), Information theory (rate distortion theory, model selection), and Computer vision (structured models, perceptual organization). Collaborations extend to brain-computer interfaces and ecological modeling. Publication trends : Recent articles focus on Bayesian filtering, statistical neuroscience methods, mixture models, and computational approaches to neural data. Themes include handling high-dimensional data, robust inference, and applications to biological and machine intelligence. Scientific awards : Phi Beta Kappa (1997) Jefferson Scholarship (1994-1998) Howard Hughes Medical Institute Predoctoral Fellowship (1998) National Defense Science and Engineering Graduate Fellowship (1998-2001) IBM Watson Research Award (2014) Philip J. Bray Teaching Award (2014) Advising : Mentors current PhD student Sicheng Liu and has advised former students including Jeffrey Miller (Harvard), Dahlia Nadkarni (Akamai), and Mona Khoshnevis. Collaborates with institutions like Carney Institute and Data Science Institute.

Stuart GemanView profile

Professor

• Machine Learning
• Computer Vision
• Statistical Theory
• +3 more

Stuart Geman is the James Manning Professor of Applied Mathematics at Brown University's Division of Applied Mathematics. He holds a Ph.D. from MIT (1977), focusing on stochastic differential equations. His research spans machine and natural vision, statistical theory, neuroscience, financial modeling, and computational linguistics. He has advised numerous students, including Asohan Amarasingham, Lo-Bin Chang, and Ya Jin. His work explores hierarchical models for visual recognition, neural spike train analysis, market dynamics, and generative image modeling. Key contributions include the 'compositionality' framework for efficient learning in biological systems, probabilistic image models with hierarchical structure, and statistical methods for neurophysiological data. He co-developed the Gibbs sampling method for image restoration and pioneered nonparametric statistical estimation via sieves. His recent work addresses scale invariance in natural images and transsaccadic neural coding in macaque V1. Publications span journals like Journal of Neuroscience , Neural Computation , and Proceedings of the National Academy of Sciences . His research integrates computational, statistical, and biological perspectives to address challenges in vision, neuroscience, and financial systems.

• Computational Statistics
• Simulation-Based Inference
• Stochastic Optimization
• +5 more

Dao Nguyen is an Associate Professor of Mathematics and Assistant Professor of Statistics at the University of Mississippi, affiliated with the Department of Mathematics within the College of Liberal Arts. He holds a Ph.D. in Statistics from the University of Michigan (2016) and served as a postdoctoral scholar at the University of California, Berkeley. His research focuses on computational statistics, simulation-based inference, stochastic optimization, and machine learning, with specific interests in Langevin Monte Carlo methods, blackbox sampling, and applications in infectious disease modeling. He teaches courses ranging from introductory statistics to advanced statistical computing. Research Interests: Computational Statistics Stochastic Optimization Monte Carlo Methods (e.g., Langevin Algorithms) Machine Learning Applications Mechanistic Modeling for Infectious Diseases Statistical Software Development (e.g., R packages) Publications Trends: Nguyen’s recent work emphasizes methodological advancements in sampling algorithms, non-convex optimization, and interdisciplinary applications in environmental health and global ecology. His articles frequently address challenges in high-dimensional data analysis, algorithmic efficiency, and bridging theory with practical implementations. Education: Ph.D. in Statistics, University of Michigan-Ann Arbor (2016) Postdoctoral Research, University of California, Berkeley (2016–2017) Teaching & Advising: Nguyen instructs courses such as Statistical Computing and Data Analysis and Mathematical Statistics . While no advisees are listed, his research group likely engages in collaborative projects across disciplines.

D. Andrew BrownView profile

Professor

• Uncertainty quantification
• neuroimaging data analysis
• Bayesian modeling
• +4 more

D. Andrew Brown is a Full Professor of Statistics in the School of Mathematical and Statistical Sciences at Clemson University. He received his PhD and MS in Statistics from the University of Georgia (2013 and 2010, respectively) and his BS in Applied Mathematics from Georgia Tech (2006). His educational background includes: PhD, Statistics, University of Georgia, 2013 MS, Statistics, University of Georgia, 2010 BS, Applied Mathematics, Georgia Tech, 2006 Dr. Brown's research focuses on uncertainty quantification, neuroimaging data analysis, and Bayesian modeling and computation. His work bridges statistical theory with practical applications in neuroscience and engineering. He has made significant contributions to the development of Bayesian methods for analyzing complex-valued fMRI data, spatial binary regression for neuroimaging, and Gaussian process modeling with functional constraints. His research demonstrates exceptional mathematical sophistication in handling complex data structures while maintaining computational feasibility. His recent publications reveal a strong trend toward sophisticated Bayesian approaches for neuroimaging data analysis, particularly focusing on complex-valued fMRI data. His work spans multiple disciplines including neuroscience, materials science, and engineering through statistical modeling and computer model calibration. The consistent publication record across top statistical journals demonstrates both methodological innovation and practical relevance of his work. Dr. Brown has received recognition for his work, including being named a Visiting Research Fellow at the Statistical and Applied Mathematical Sciences Institute in Spring 2016. He is an active member of several professional organizations: American Statistical Association International Society for Bayesian Analysis Society for Industrial and Applied Mathematics As a first-generation college student who was promoted to Full Professor in August 2025, Dr. Brown acknowledges the importance of mentorship and support in academic success. His professional journey reflects dedication to both research excellence and academic service, with particular attention to supporting underrepresented populations in STEM pathways. Dr. Brown maintains active collaborations across disciplines and has contributed to research in neuroscience, materials science, and engineering through his statistical methodology development. His work demonstrates how advanced statistical methods can solve real-world problems across scientific domains.

• Categorical Data Analysis
• Group Testing
• Bayesian Estimation
• +3 more

Christopher McMahan is a Full Professor at Clemson University's School of Mathematical and Statistical Sciences within the College of Science. His academic roles include serving as Associate Director for Graduate Studies and co-leading the Clemson-MUSC AI Hub. McMahan holds a Ph.D. in Statistics from the University of South Carolina (2012), M.S. in Mathematics from Western Kentucky University (2008), and B.S. in Mathematics from Austin Peay State University (2006). His research focuses on developing statistical methodologies for complex data problems, with interdisciplinary applications spanning epidemiology, agriculture, and biomedical sciences. Core research areas include Bayesian modeling, group testing designs, high-dimensional regression, and spatio-temporal analysis. Current projects involve genomic prediction in crop science, infectious disease modeling, and machine learning applications for public health surveillance. McMahan's publication record demonstrates consistent contributions to statistical theory and applied interdisciplinary research. Recent work exhibits strong emphasis on: Bayesian computational methods for large-scale problems Epidemiological modeling of COVID-19 transmission Genomic prediction of agricultural phenotypes Statistical learning approaches for public health data Biomedical applications of group testing designs Environmental risk assessment methodologies His scholarly recognition includes: Fellow of the American Statistical Association (2022) Multiple ASA Outstanding Statistical Application Awards ENAR Distinguished Student Paper Awards College of Science Dean's Associate Professor (2021-2023) McMahan maintains an active research program with substantial grant funding, including NIH R01 awards totaling over $2 million. He currently advises four postdoctoral scholars and has mentored eight doctoral graduates. His collaborative work involves interdisciplinary teams across computational biology, plant genomics, and public health epidemiology. Laboratory and team leadership includes directing graduate research through the Clemson-MUSC AI Hub and coordinating multi-institutional consortia like the NCAA-DoD CARE Consortium. Future research directions emphasize developing computationally efficient statistical methods for high-dimensional biomedical data and expanding applications in precision agriculture.

Perla ReyesView profile

Associate Professor

• Spatial-Temporal Statistics
• Bayesian Nonparametrics
• Model Selection
• +1 more

Dr. Perla Reyes serves as Interim Department Head and Associate Professor in the Department of Statistics at Kansas State University's College of Arts and Sciences. She holds a Ph.D. (2010) and M.S. (2008) from the University of Wisconsin-Madison, and a B.S. in Actuarial Sciences from National Autonomous University of Mexico (2001). Her statistical research develops methodologies for analyzing complex spatial-temporal dependencies and network-structured data, with applications in environmental science and social systems. Her expertise includes Bayesian nonparametric approaches and model selection techniques.

Abhijit MandalView profile

Associate Professor

• Robust Inference
• Nonparametric Statistics
• Big Data Analysis
• +4 more

Abhijit Mandal is an Associate Professor in the Department of Mathematical Sciences at the University of Texas at El Paso. He serves as the Director of the Data Analytics Lab and holds multiple coordination roles, including overseeing the Graduate Certificate programs in Applied Statistics and Big Data Analytics. His work focuses on developing robust statistical methods to handle noisy data, particularly in the areas of robust inference, nonparametric statistics, and biostatistics. His research spans robust statistical methodologies, including applications in high-dimensional data analysis, functional regression, and biostatistical modeling of metabolic and inflammatory risks. Key contributions include novel techniques for variable selection, spatial data analysis, and the mitigation of outlier effects in complex datasets. He also explores interdisciplinary applications, such as microglial signaling in metabolic dysfunction and the impact of environmental pollutants on health. Mandal’s recent publications emphasize robust statistical frameworks for functional data, panel data models, and healthcare analytics. His work often integrates computational methods like the Metropolis algorithm and advanced regression techniques to address real-world challenges. He is a UTEP Edge Curriculum Fellow, reflecting his commitment to innovative teaching and curriculum development in data science. While no specific scientific awards are listed, his active roles in directing research labs and coordinating academic programs highlight his leadership in fostering data-driven research and education. In advising, Mandal’s focus is on mentoring students in statistical methodologies applicable to diverse domains. His research teams collaborate across disciplines, including neuroscience and environmental health, as seen in studies linking air pollutants to metabolic disorders through microglial pathways.

Leah FeuerstahlerView profile

Associate Professor

• Item Response Theory
• Psychometric Model Specification
• Bayesian Statistics
• +3 more

Leah Feuerstahler serves as Associate Professor of Psychology and Director of the Psychometrics and Quantitative Psychology Program at Fordham University's College of Arts and Sciences. Her academic appointments include teaching graduate courses in Psychometric Theory, Item Response Theory, and Bayesian Statistics, alongside undergraduate Statistics instruction. Her research centers on advancing psychometric methodologies with critical applications in environmental health and clinical assessment. Key interests include Item Response Theory extensions, metric stability analysis, Bayesian statistical approaches, and nonparametric methods for modeling complex data structures. Her recent work demonstrates innovative adaptations of psychometric models to quantify exposure burdens from chemical mixtures like PFAS and phthalates, bridging psychological measurement with environmental epidemiology. Feuerstahler's publication trends reveal strong methodological contributions to psychometrics alongside applied environmental health research. Approximately 60% of her recent work focuses on chemical mixture exposure modeling using item response theory, while 30% addresses core psychometric theory development, and 10% involves clinical instrument validation for oral health and cancer-related hopelessness. She actively mentors graduate students, with consistent inclusion of student co-authors (marked with asterisks) across publications and conference presentations. Her professional affiliations include the Psychometric Society and National Council on Measurement in Education (NCME), reflecting her commitment to advancing measurement science. Feuerstahler maintains active research laboratories focused on psychometric model development and environmental exposure analysis, with current projects developing exposure burden calculators for chemical mixtures and validating health literacy instruments. Her work demonstrates growing interdisciplinary collaboration between psychology, environmental science, and public health departments.

• Bayesian Modeling
• Statistics in Artificial Intelligence
• Statistical Omics
• +3 more

Dr. Sherry Wang is a Jenkins-Garrett Professor of Mathematics at The University of Texas at Arlington. She holds affiliate professorships at Southern Methodist University. Her research focuses on Bayesian Modeling, Statistical Omics, and Meta-Analysis. She earned her PhD from The University of Texas at Austin (2002) and has held roles at SMU from 2003 to 2022. Her work includes federal grants on deep learning for neoantigen analysis and T-cell receptor binding. Key awards include being an Elected Fellow of the American Statistical Association (2024). She advises numerous PhD students and chairs dissertation committees in Data Science and Biostatistics. Dr. Wang teaches courses like Bayesian Data Analysis and collaborates on projects like infrastructure equity and normalization of genomic data. Her service roles include College Director for Research in Data Science.

Joshua M. TebbsView profile

Professor

• Categorical Data Analysis
• Biostatistics
• Group Testing Methods
• +2 more

Dr. Joshua M. Tebbs is a Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a BS in Mathematics, an MS in Statistics, and a PhD in Statistics from the University of Iowa and North Carolina State University, respectively. His research focuses on categorical data analysis, statistical methods for group testing, order-restricted inference, and applications in public health and biostatistics. He is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. Dr. Tebbs has served as Editor of the American Statistician (2020–2023) and contributed to numerous academic courses, including STAT 513 (Theory of Statistical Inference), STAT 512 (Mathematical Statistics), and STAT 110 (Introduction to Statistical Reasoning). His work emphasizes methodological advancements in group testing for disease prevalence estimation and has been supported by NIH funding. Key awards include recognition from the ASA and ISI, reflecting his scholarly contributions. His research outputs span statistical methodology, computational tools (e.g., binGroup2 ), and applications in infectious disease surveillance and public health decision-making.