Find Academic Supervisors

Chang-Xing MaView profile

Associate Professor

• Biostatistics
• Statistical Genetics
• Experimental Design
• +3 more

Chang-Xing Ma, PhD, is an Associate Professor in the Department of Biostatistics at the School of Public Health and Health Professions, University at Buffalo. He serves as Co-Director of the MPH Biostatistics Concentration. With a PhD from Nankai University (1997), his expertise spans statistical genetics, experimental design, and advanced biostatistical methods. His research focuses on developing statistical methodologies for correlated data analysis, particularly in clinical trials and public health contexts. Dr. Ma’s work addresses challenges in analyzing paired and stratified data, including bilateral/unilateral studies in ophthalmology, pediatric care, and environmental health. Notable contributions include exact testing methods for proportion ratios, confidence interval construction for correlated outcomes, and applications in genetic epidemiology. His interdisciplinary projects integrate statistical theory with practical medical and environmental research. Key publications explore vaccine uptake barriers among pediatric patients, frailty assessment in veterans, and the efficacy of ketamine in delirium management. He has developed SAS macros for logistic regression modeling and contributed to studies on CFTR modulation in cystic fibrosis and statin-associated muscle symptoms. His work emphasizes rigorous statistical approaches to address real-world healthcare challenges.

Albert VexlerView profile

Professor

• Biostatistics
• Epidemiology
• Statistical Methods
• +7 more

Albert Vexler, PhD, is a Professor in the Department of Biostatistics at the University at Buffalo, within the School of Public Health and Health Professions. He specializes in advanced statistical methodologies with applications to biomedical and health-related research. His work focuses on biomarker analysis, nonparametric testing, ROC curve methodologies, and empirical likelihood techniques. Education: PhD in Statistics and Probability Theory from the Hebrew University of Jerusalem (2003), followed by a Postdoctoral Research Fellowship at the National Institute of Child Health and Human Development (2004–2007). Research Interests: Dr. Vexler’s research spans biostatistical methods, including optimal designs, censored data analysis, sequential analysis, and Bayesian approaches. He develops tools for handling complex data structures such as skewed biomarker measurements and limits of detection in clinical studies. His work emphasizes robust statistical inference and practical applications in clinical trials and public health. Publications: His recent work highlights advancements in hypothesis testing, biomarker evaluation, and statistical computing. Key areas include density-based empirical likelihood tests, multivariate dependence measures, and computational tools for analyzing biomedical data. Professional Activities: He serves as an Associate Editor for Biometrics and Journal of Applied Statistics , and has reviewed for top-tier journals such as the Journal of the American Statistical Association and Biometrika . He contributes to statistical software development, including R packages like dbEmplikeGOF and testforDEP . Lab/Teams: Leads a research group focused on innovative statistical methods for health sciences, collaborating on projects involving clinical trial design, epidemiological modeling, and precision medicine.

• Multiple Testing Procedures
• Conformal Prediction
• False Discovery Rate
• +4 more

Wenge Guo is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on statistical methodology, particularly in multiple testing procedures, conformal prediction, false discovery rate control, and ordinal classification. He has contributed to advancements in methodologies for clinical trials, machine learning applications, and algorithmic statistical inference. His research interests span areas such as familywise error rate control, distribution-free prediction methods, and adaptive testing strategies. Recent work emphasizes conformal prediction frameworks for ordinal and multi-label classification, as well as two-stage risk control in ranked retrieval systems. Guo’s expertise also extends to applications in biomedical research and information retrieval systems. Key Research Areas: Multiple Testing, Conformal Prediction, Ordinal Classification, Clinical Trials, Statistical Methodology Key Contributions: Development of the MHTdiscrete package, hierarchical testing procedures for non-inferiority trials, and adaptive FWER/FDR controls under block dependence. Notable projects include a National Science Foundation-funded study on large-scale multiple testing problems (2013–2016). His work bridges theoretical statistics with practical applications in machine learning and biomedical research.

Florian RichardView profile

Assistant Professor

• Econometrics
• Financial Econometrics
• Asset Valuation
• +3 more

Florian Richard is an Assistant Professor at the Department of Finance, Insurance and Real Estate within the School of Administrative Sciences (FSA ULaval) at Université Laval. His research focuses on econometrics, financial econometrics, and asset pricing, emphasizing statistical testing methodologies and multivariate modeling. Education: Doctor of Philosophy (Ph.D.) in Economics, Carleton University Master of Arts (M.A.) in Economics, Carleton University Bachelor of Arts (B.A.) in Economics and Mathematics, York University Research Interests: Florian specializes in developing simulation-based econometric techniques for model selection and validation, particularly in non-nested multivariate models. His work addresses challenges in asset pricing factor models and confidence set constructions in complex systems. Recent presentations include contributions on multiple testing frameworks at the Canadian Economics Association and Société canadienne de science économique conferences. Presentations: 19th International Conference on Computational and Financial Econometrics (2025, upcoming) 63rd Annual Meeting of the Société canadienne de science économique (2024) 57th Annual Conference of the Canadian Economics Association (2023) Working Papers: Ongoing research includes collaborations with Lynda Khalaf on model confidence sets and simulation-based testing methodologies.

• Reproducible Research
• Computational Science
• Statistical Modeling
• +3 more

Aaron Peikert is a Research Fellow and Principle Investigator at the Center for Lifespan Psychology, Max Planck Institute for Human Development. His work focuses on advancing reproducible research methodologies and open science practices in empirical sciences. He holds a doctorate in Psychology (summa cum laude) from the Max Planck Institute, with earlier degrees from Humboldt-Universität zu Berlin (M.Sc., B.Sc. Psychology). His research addresses computational reproducibility, preregistration frameworks, and statistical modeling for nested data. Key professional roles include leadership of the 'Formal Methods in Lifespan Psychology' group, fellowship in the Max Planck-UCL Research School, and visiting scholar positions at UCL, LMU Munich, and others. He has developed influential software tools like StructuralEquationModels.jl and the worcs R package, promoting reproducible workflows. Peikert has authored or co-authored multiple peer-reviewed articles in journals such as Psychometrika , Structural Equation Modeling , and Data Science . His awards include the Max Planck Society's Dieter Rampacher Prize (2023) for exceptional doctoral research and a German Academic Scholarship Foundation grant. He actively contributes to open science initiatives through workshops, policy drafting, and editorial reviewing for NeuroImage and other outlets. Teaching highlights include multivariate statistics courses at Humboldt-Universität and advanced reproducibility workshops at institutions worldwide. His work bridges theoretical statistical innovation with practical implementation, emphasizing rigor in computational workflows and transparent research practices.

• Econometrics
• Time Series Analysis
• Discrete Choice Models
• +3 more

Prof. Dr. Dietmar Bauer holds the Chair of Econometrics at the Faculty of Business Administration and Economics at Bielefeld University. He maintains multiple affiliations including the Department of Empirical Methods, Center for Statistics, Institute for Technological Innovation, Market Development and Entrepreneurship, BIGSEM (Bielefeld Graduate School of Economics and Management), and the Bielefeld Graduate School in Theoretical Sciences. His academic credentials include a Habilitation in Econometrics (2007), Doctorate in Technical Mathematics (1998), and Diploma in Technical Mathematics (1995), all from TU Wien, Austria. Prior to his position at Bielefeld, he served as Senior Scientist at the Austrian Institute of Technology (2005-2014) and Assistant at TU Wien (1995-2005), with additional postdoctoral positions at Yale University, University of Linköping, and University of Newcastle. Prof. Bauer's research focuses on two primary areas: time series analysis and discrete choice models. His work in time series investigates subspace methods for state space models, particularly for integrated and seasonally integrated processes. In discrete choice modeling, he examines computational efficiency in Probit models and transportation mode choice, with special attention to the MACML approach. His publications demonstrate consistent contributions to both theoretical econometrics and applied transportation research. Multa Scripsit Award from Econometric Theory (2013) Best paper awards at UrbComp 2012 (with Jameson Toole, Marta Gonzalez, and Michael Ulm) Oskar Morgenstern Award of IHS, Vienna (with Martin Wagner) Prof. Bauer teaches numerous courses across four institutes including Software Applications for Economists, Data Analysis, Statistical Methods, and Econometrics. He supervises diploma theses, master's theses, and doctoral dissertations. His research has been supported by funding from DFG, FWF, FFG, and EU-level projects, reflecting his strong record in securing competitive research funding. He is actively involved with the Center for Statistics and the Bielefeld Center for Data Science, contributing to interdisciplinary research initiatives.

Matthew R. HodgesView profile

Professor

• Respiratory Physiology
• Neurophysiology
• Genetics and Genomics
• +3 more

Matthew R. Hodges is a Professor and Eminent Scholar in the Physiology Department at the Medical College of Wisconsin (MCW), serving as Director of Graduate Studies and Director of a NIH T32 Training Grant. His research focuses on neural control of breathing, Sudden Infant Death Syndrome (SIDS), and epilepsy-related respiratory dysfunction. He holds a PhD from MCW (2004) and a BA from Carleton College (1998). His lab employs advanced techniques like RNA sequencing and rodent models to study CO2 chemoreception and opioid effects. Education: PhD in Physiology, Medical College of Wisconsin, 2004 BA in Biology, Carleton College, 1998 Research Interests: Genetics/Genomics, Neurophysiology, Respiratory Physiology. Current projects include: Mechanisms of CO 2 sensing in brainstem neurons SIDS-related serotonin system abnormalities Seizure-induced cardiorespiratory failure (SUDEP) Opioid-induced respiratory depression Neuroplasticity in neonatal lung dysfunction Methodologies: Single-cell RNA sequencing Rodent models (rats, goats) Electrophysiology and microscopy Transcriptomics In vivo pharmacological interventions Awards: Parker B. Francis Fellow (2006-2009) Fellow of the American Physiological Society (FAPS) Advising & Grants: Mentor to 8 undergraduates, 2 medical students, and multiple PhD students. NIH-funded since 2009, with additional support from Children's Research Institute and Advancing Healthier Wisconsin Foundation. Collaborations include Universities of Missouri, Oregon, and MCW faculty across departments. Labs/Teams: The Hodges Lab emphasizes interdisciplinary training, studying breathing control in health/disease. Active projects involve epilepsy, opioid crisis mitigation, and neonatal respiratory adaptation. Collaborates with neurobiologists, neonatologists, and geneticists.

Ruobin GongView profile

Associate Professor

• Bayesian Methods
• Imprecise Probability
• Differential Privacy
• +3 more

Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research. Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics. Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.

Dedotsi SofiaView profile

Assistant Professor

• Anti-Oppressive Practice
• Social Work Education
• Migration and Refugee Studies
• +3 more

Sofia Dedotsi is an Assistant Professor at the Department of Social Work, University of West Attica, Greece. She holds a PhD and has extensive field experience in child protection and family support in the UK (2007-2010) and Greece (2014-2015). Her expertise spans critical social work theories, anti-oppressive practice, migration issues, and social policy advocacy. She has served as a board member of the European Social Work Research Association (ESWRA, 2014-2019), including a Vice-Chair role (2017-2019). Her research focuses on social work education, refugee crisis responses, and the intersection of migration and policy. Notable projects include international refugee activist collaborations and photovoice methodologies exploring student perspectives on migration. She actively engages in editorial roles, including the European Social Work Research Journal, and reviews for multiple journals. Her publications reflect interdisciplinary interests, bridging social work practice, policy analysis, and critical theory. Key themes include homelessness advocacy, refugee integration challenges, and mental health clinical training outcomes for students. Despite no listed scientific awards, her work emphasizes ethical, transformative, and community-centered approaches to social work in crisis contexts.

• Optimal Control
• Operations Research
• Game Theory
• +4 more

Dr. Loïc Cellier is an Associate Professor specializing in Mathematics and Computer Science at the School of Aerospace Engineering (ELISA Aerospace, ISAE group) in Bordeaux, France. He holds a Ph.D. in Applied Mathematics from the University of Toulouse and has extensive qualifications from the French National Council of Universities. His research focuses on Optimal Control, Operations Research, and Game Theory, with applications in Air Traffic Management, Combinatorial Games, and Mathematics Education. Education includes a Ph.D. from the University of Toulouse (2015), M.Sc. in Applied Mathematics from Sorbonne University (2010), and B.Sc. in Mathematics from the University of Montpellier (2008). He has received multiple awards, including top rankings in national competitions and international recognition for his work in Hex game theory and optimization. Research interests span Optimal Control (aircraft conflict avoidance), Operations Research (combinatorial optimization), Game Theory (Hex and Chomp), and Mathematics Education. He has published extensively in journals like Optimal Control Applications and Methods, and contributed to educational platforms like POEM for personalized learning systems. Teaching responsibilities include courses in Mathematics, Computer Science, and Numerical Analysis at universities in France, Azerbaijan, and Switzerland. His pedagogical materials cover advanced topics like Probability, Optimization, and Programming in Python/C++. Notable projects include leading the ATOMIC project on air traffic optimization, co-founding the HEX&CO organization to promote combinatorial games, and developing educational resources for UNESCO’s POEM initiative.

Shuangqing WeiView profile

Professor

• Wireless Communications
• Signal Processing
• Network Security
• +4 more

Shuangqing Wei is the Michel B. Voorhies Distinguished Professor and Division Chairman in the Division of Electrical & Computer Engineering at Louisiana State University (LSU). He holds a Ph.D. (2003) from the University of Massachusetts and is affiliated with the School of Electrical Engineering and Computer Science. His research focuses on wireless communication systems, including OFDMA networks, multiple antenna systems, and privacy-enhanced communication protocols. Key research interests include: Multi-agent reinforcement learning and cooperative strategies Privacy-preserving techniques in federated learning and data analysis Covert communication over AWGN channels Optimization algorithms for distributed systems Cognitive radio networks and spectrum management Recent work emphasizes communication efficiency in distributed optimization (e.g., ADMM with Gaussian process regression) and trade-offs between privacy and utility in machine learning systems. His publications span over two decades, addressing topics from information theory fundamentals to applied security in vehicular networks and social media systems. He maintains an active research group at LSU, exploring interdisciplinary challenges at the intersection of signal processing, machine learning, and network security. Labs/Teams : Active involvement in LSU's wireless communication and machine learning research initiatives, including collaborative projects on federated learning frameworks and secure multi-agent systems.

Keith FeigensonView profile

Associate Professor

• Biopsychology
• Neuroscience
• Cognitive Science
• +3 more

Dr. Keith Feigenson is an Associate Professor of Psychology at Albright College, specializing in Biopsychology and Neuroscience. He holds a B.A. in Psychology from Haverford College and a Ph.D. in Neuroscience from the University of Pennsylvania. His research focuses on perceptual organization, predictive reasoning, biomarkers in mental health, and cognitive training. Dr. Feigenson’s work explores how neurobiological processes underlie human behavior, particularly in populations with schizophrenia and other mental illnesses. He teaches courses including Biological Psychology, Behavioral Neuroscience, and Advanced Topics in Psychology. His research has been supported by grants such as the NIGMS K12 Award and the Albright Creative Research Experience Award. Notable publications investigate perceptual illusions, neuroimaging indices of spatial context, and the role of inflammation in schizophrenia pathogenesis. Dr. Feigenson serves as Faculty Advisor for Psi Chi and is a member of multiple professional organizations, including the Society for Research in Psychopathology. His awards include the Early Career Research Award from Robert Wood Johnson Medical School. His research integrates experimental models from psychology and biology to address questions about mental health, emphasizing translational approaches to understanding cognitive and perceptual deficits in psychiatric disorders.

Pinyuen ChenView profile

Professor

• Statistical Signal Processing
• Sequential Analysis
• Ranking and Selection
• +9 more

Professor Pinyuen Chen is a faculty member in the Department of Mathematics at Syracuse University, affiliated with the Applied Statistics Program within the College of Arts & Sciences. He has held his position since 1982 and has advised doctoral students such as Elena Buzaianu. His research focuses on statistical signal processing, ranking and selection, sequential analysis, and interdisciplinary applications in fields like radar technology and biostatistics. He currently serves as Director of the Interdisciplinary Statistics Program (July 2024–June 2025) and has held roles including Applied Statistics Minor Advisor. Education: Ph.D. in Statistics, University of California, Santa Barbara (1982) M.S. in Mathematics, University of Miami (1978) B.S. in Mathematics, Cheng-Kung University (1973) Research Interests: Chen’s work emphasizes sequential analysis techniques, including procedures for comparing treatments with multiple endpoints, selection of populations with optimal means, and applications in signal processing. His expertise spans statistical methodologies for real-time data analysis, heteroscedasticity, and multivariate systems. He collaborates with researchers globally, notably in military projects at the U.S. Air Force Research Laboratory and academic institutions like Shanghai University of Finance & Economics. Recent Trends in Articles: His publications over the past years consistently address subset selection problems in statistical contexts, particularly focusing on Bernoulli outcomes, normal means, and control-based comparisons. Recent works explore curtailed procedures to enhance efficiency in experiments, often integrating sequential methods for faster decision-making. Awards: Abraham Wald Prize (2024) for best publication in Sequential Analysis Journal Travel Award (2023) from China Exchange Program, American Mathematical Society Advising & Grants: Chen advised PhD student Elena Buzaianu (Syracuse, 2006). His grants include military-funded radar signal-processing projects at the U.S. Air Force Research Laboratory in Dayton, Ohio, aimed at improving target detection accuracy. He is also an Editorial Review Board Member for the Journal of Sequential Analysis since 2004. Labs & Teams: Affiliated with Syracuse University’s interdisciplinary Applied Statistics Program and Statistics Group, collaborating with faculty from computer science, management, psychology, and social sciences. His work spans both mathematical and applied statistics, with recent activities in China, Taiwan, and international conferences.

• High Energy Physics
• Particle Physics
• Experimental Physics
• +3 more

Erik Brucken is a University Lecturer at the Department of Physics, University of Helsinki, affiliated with the Faculty of Science. He is also a supervisor for doctoral programs in Particle Physics and Universe Sciences, and Materials Research and Nanosciences. Research focuses on high energy physics and experimental particle physics, particularly in CMS experiment collaborations at CERN. Active in projects like the Euroopa hiukkasfysiikan tutki (2024–2027) and ALICE Time Projection Chamber Upgrade Project (2013–2018). His research interests include Higgs boson studies, top quark dynamics, jet physics, and detector development. He has published extensively on topics such as rare decays, muon identification, and quantum entanglement in collider experiments. Advises doctoral students in particle physics and collaborates widely, participating in conferences like the International Europhysics Conference on High Energy Physics and CMS Tracker Weeks. His work contributes to advancing detector technologies and computational tools for high-energy physics.

• High-dimensional data analysis
• Nonlinear dependency measurement
• Statistical testing methods
• +3 more

Professor Xu Kai is a faculty member at the School of Mathematics and Statistics of Anhui Normal University, specializing in high-dimensional data analysis and nonlinear dependency measurement. He earned his bachelor's degree in engineering before transitioning to statistics, obtaining his master's and PhD under Professors He Daojiang and Zhu Liping respectively. His research addresses challenges posed by the 'curse of dimensionality' through innovative statistical methodologies published in top journals like The Annals of Statistics . He has secured multiple grants including National Natural Science Foundation projects and won prestigious awards such as the Anhui Youth Mathematics Award. Education: Bachelor's in Engineering (top scores) Master's in Probability Theory & Mathematical Statistics (Anhui Normal University) PhD in Statistics (under Prof. Zhu Liping) Research focuses on developing statistical tools for high-dimensional data, with applications in biomedicine and economics. His testing methods address covariate significance in regression equations, exemplified by work on gene mutation analysis. Teaching emphasizes functional thinking and interdisciplinary problem-solving, mentoring students like Zhang Jialong who praised his rigorous academic standards. Awards: 2023: Anhui Natural Science Foundation Young Scholar 2022: National Natural Science Foundation General Project 2021: 5th Anhui Youth Mathematics Award Advising & Grants: Directed 5+ student research projects Main investigator for 3 National Natural Science Foundation grants Labs/Teams: Leads the High-Dimensional Statistical Analysis Research Group at Anhui Normal University.