Find Academic Supervisors

• Health Psychology
• Clinical Psychology
• Medical Education
• +4 more

Changiz Mohiyeddini serves as Professor of Foundational Medical Studies at Oakland University William Beaumont School of Medicine, where he teaches core courses including "Brain and Behavior", "Psychopathology", "Clinical-Health Psychology", "Personality Psychology", and "Advanced Quantitative Methods". He holds significant editorial leadership as Assistant Field Chief Editor for Frontiers in Psychology, Specialty Chief Editor for Health Psychology, and Associate Editor for Psychology for Clinical Settings, with 266 edited publications and 6 edited research topics documented. His research centers on critical intersections of health psychology and medical education, particularly addressing moral injury frameworks, resilience mechanisms in healthcare contexts, and cross-cultural competency development. Recent work investigates AI-driven assessment tools, pandemic-responsive curriculum adaptations, and statistical methodologies for improving medical examination quality, reflecting urgent real-world applications in clinical training environments. Analysis of his 2023-2024 publications reveals concentrated innovation in educational technology (including ChatGPT applications for pharmacology questions), trauma psychology, and globalized healthcare training. This body of work consistently bridges theoretical psychology with practical medical education challenges, emphasizing student feedback integration, cultural competence, and assessment reliability during systemic disruptions like the COVID-19 pandemic. Scientific Awards: No awards documented in available materials Advising and Grants: While editorial leadership demonstrates scholarly influence, specific information regarding student mentorship, research grants, or funded projects was not provided in the source materials. His longitudinal study on student feedback integration suggests active engagement with educational research methodologies. Labs and Teams: No dedicated research laboratories, collaborative teams, or institutional research centers were mentioned in the available documentation, though his editorial roles indicate broad cross-institutional academic networks.

Stanislav VolgushevView profile

Associate Professor

• Data Science
• Probability
• Theoretical Statistics
• +7 more

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

Carlos LamarcheView profile

Professor

• Econometrics
• Quantile Regression
• Longitudinal Data Analysis
• +3 more

Carlos Lamarche is a University Research Professor and Gatton Endowed Professor of Economics at the University of Kentucky , with a joint appointment as Professor of Statistics. His work focuses on developing advanced econometric methods for robust empirical analysis. Education: Ph.D. and M.S. from University of Illinois at Urbana-Champaign Research Interests: He pioneers innovations in quantile regression for longitudinal and panel data, addressing challenges in heterogeneous preferences, censored data, and interactive effects. His methodologies are applied to topics like welfare reform, child development, and strategic bidding. Publications: His influential work appears in top journals such as Journal of Political Economy , Journal of Econometrics , and International Economic Review , reflecting interdisciplinary impacts across economics, statistics, and data science. Contact: Email clamarche@uky.edu for inquiries.

Johann Peter GogartenView profile

Professor

• Molecular evolution
• Origin and early evolution of cellular life
• Evolution of the eukaryotic endomembrane system
• +6 more

Johann Peter Gogarten is a Board of Trustees Distinguished Professor in the Department of Molecular and Cell Biology and Microbiology at the University of Connecticut. With a distinguished career spanning over three decades, he has established himself as a leading authority in molecular evolution and microbial genomics. His research bridges computational biology with experimental microbiology to address fundamental questions about the origin and evolution of cellular life. He maintains an active laboratory and collaborates with an international network of researchers advancing our understanding of evolutionary mechanisms. Dr. Gogarten's educational background includes: Ph.D. from the University of Giessen, Federal Republic of Germany Postdoctoral study at the University of California, Santa Cruz Professor Gogarten's research program centers on molecular evolutionary mechanisms, with particular emphasis on horizontal gene transfer as a major force shaping microbial evolution. His work challenges traditional tree-based evolutionary models by demonstrating how genetic exchange between distantly related organisms fundamentally alters evolutionary trajectories. He investigates the origin of cellular life, the development of the eukaryotic endomembrane system, and the evolutionary dynamics of molecular parasites like inteins. His laboratory employs both computational approaches and experimental validation to study these complex evolutionary processes across multiple levels of biological organization, from individual genes to entire genomes. Analysis of Professor Gogarten's recent publications (2017-2024) reveals a consistent research trajectory focused on horizontal gene transfer mechanisms and their evolutionary implications across diverse microbial systems. His work spans multiple domains of life with particular emphasis on archaea, examining genomic islands in haloarchaea, intein interference mechanisms, and bacterial cooperation through genetic exchange. His research increasingly incorporates advanced computational methods for phylogenomic analysis and genome comparison, reflecting the growing importance of bioinformatics in evolutionary studies. The interdisciplinary nature of his work connects molecular biology, computational science, and evolutionary theory to address fundamental questions about life's history. Among Professor Gogarten's notable recognitions is his appointment as a Board of Trustees Distinguished Professor at the University of Connecticut, representing one of the highest honors the university bestows on its faculty. This distinction recognizes his exceptional contributions to research, teaching, and service to the scientific community. His extensive publication record in top-tier journals including Nature Microbiology, PNAS, and Genome Biology and Evolution demonstrates the impact and significance of his work in the field of molecular evolution. Professor Gogarten has mentored numerous graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. His laboratory has received continuous funding from the National Science Foundation and other agencies to support research on molecular evolution and horizontal gene transfer. He has organized multiple international conferences and workshops that have brought together researchers from diverse fields to address fundamental questions in evolutionary biology. His collaborative network spans multiple continents, reflecting the global impact of his research program and his leadership in the field. The Gogarten laboratory at the University of Connecticut maintains a vibrant research program focused on molecular evolution and microbial genomics. His team combines computational approaches with experimental validation to investigate fundamental questions about evolutionary processes. The laboratory has developed innovative methods for detecting horizontal gene transfer and analyzing evolutionary relationships, which have been widely adopted by the research community. Current work focuses on inteins, genomic islands, and the evolutionary dynamics of archaeal and bacterial systems, continuing Professor Gogarten's long-standing commitment to understanding the complex mechanisms that have shaped life on Earth.

Patti Frazer LockView profile

Professor

• Graph Theory
• Mathematics Education
• Statistics Education
• +3 more

Patti Frazer Lock is a Professor of Mathematics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University . She has co-authored multiple influential textbooks including Statistics: Unlocking the Power of Data and works with the Calculus Consortium for Higher Education. Her academic contributions span mathematics education, graph theory, and curriculum development. Education: Ph.D. in Mathematics, University of Massachusetts (1981) M.A. in Mathematics, University of Massachusetts (1978) B.A. in Mathematics, Colgate University (1975) Her research focuses on graph theory and mathematics education , with particular emphasis on statistical pedagogy and calculus reform. Her publications include foundational work on uniquely Hamiltonian-connected graphs and innovative approaches to teaching introductory statistics through data-driven methods. Recent publications highlight her contributions to statistics education (2021), multivariable calculus (2021), and applied calculus (2022) textbooks. Earlier works explore quantum logics (1984), graph connectivity (1991), and interdisciplinary applications in biology (2008). Lock actively participates in professional service through roles on the AMS-ASA-MAA-SIAM Joint Data Committee , ASA-MAA Joint Committee on Undergraduate Statistics , and editorial boards for PRIMUS and Math Horizons journals. She has led workshops nationally on undergraduate mathematics and statistics education. Contact: plock@stlawu.edu | Office: Valentine Hall 219 | Sabbatical: Spring 2025

Henry CohnView profile

Professor

• Discrete Mathematics
• Sphere Packing
• Conformal Field Theory
• +8 more

Henry Cohn is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), specializing in discrete mathematics, sphere packing, conformal field theory, and fast matrix multiplication. His work bridges pure and applied mathematics, emphasizing symmetry in exceptional structures and applications to physics, cryptography, and information theory. He supervises graduate students and research interns, particularly at Microsoft Research and MIT. Education: Ph.D., Mathematics, Harvard University (2000) B.S., Mathematics, MIT (1995) Professional Background: Postdoc to long-term member, Microsoft Research Redmond (2000–2007) Head, Cryptography Group, Microsoft Research (2007–2008) Founding member, Microsoft Research New England (2008–2024) Professor, MIT Department of Mathematics (2025–present) Research Interests: Cohn explores symmetry in mathematics and physics, focusing on sphere packing, potential energy minimization, and applications of harmonic analysis and representation theory. His work spans discrete geometry, coding theory, cryptography, and theoretical computer science. Publications & Grants: He has published extensively on sphere packings, error-correcting codes, and matrix multiplication, often leveraging modular forms and semidefinite programming. His research is supported by institutions like the Simons Foundation. Teaching & Mentorship: Cohn teaches courses in number theory, discrete geometry, and mathematical logic at MIT and programs like PROMYS. He has advised numerous Ph.D. students and research interns, including Yichi Zhang, James Hirst, and Alex Cohen.

Stefan SperlichView profile

Professor

• Nonparametric Statistics
• Semiparametric Methods
• Small Area Statistics
• +7 more

Stefan Sperlich is a Full Professor and Director of the Research Institute for Statistics and Information Science at the University of Geneva's Geneva School of Economics and Management. He holds dual appointments in the Department of Econometrics and Statistics, with affiliations in both the Research Institute for Statistics and Information Science and the Institute of Economics and Econometrics. Dr. Sperlich earned his diploma in mathematics from the University of Göttingen and completed his PhD in economics at Humboldt University of Berlin. His academic career includes professorships at University Carlos III de Madrid (1998-2006) and the University of Göttingen (2006-2010), before joining the University of Geneva in 2010. Professor Sperlich's research spans nonparametric and semiparametric statistics , small area estimation , and impact evaluation methods . His work bridges theoretical econometrics with practical applications in development economics, policy evaluation, and poverty measurement. He has made significant contributions to specification testing, causal inference methodologies, and the development of robust statistical techniques for small area estimation. His research often addresses real-world problems through collaborations with international institutions and development programs. His recent publications reveal a strong focus on advancing methodological frameworks for small area statistics, causal inference, and nonparametric estimation. Key themes include developing robust inference techniques for linear mixed models, improving bandwidth selection methods, and creating model-free approaches to difference-in-differences estimation. His work increasingly integrates computational statistics with traditional econometric methods to address challenges in big data analysis and distributed data environments. Professor Sperlich has received numerous accolades including: Tjalling C. Koopmans Econometric Theory Prize (2000-2002) Augusto Gonzalez Linares award (2014) for attracting international talent Elected member of the International Statistical Institute (since 2025) Special rewards from the Economics Department at University Carlos III de Madrid (2004-2005) Grants from the Institute Flores de Lemus (2001-2003) As an advisor and researcher, Professor Sperlich has supervised numerous graduate students and led significant research initiatives. He co-founded the research center 'Poverty, Equity and Growth in Developing Countries' at the University of Göttingen and serves as a research fellow at the Center for Evaluation and Development in Mannheim, Germany. His consultancy work spans regional, national, and international institutions, with participation in development programs like EUROSOCIAL and UN assessment reports. He has secured multiple research grants supporting his work in statistical methodology and economic applications. Professor Sperlich leads research teams focused on nonparametric methods, small area statistics, and impact evaluation. His research group develops innovative statistical approaches for poverty mapping, causal inference, and composite indicator construction. The team maintains strong connections with statistical offices and international organizations, ensuring their methodological advances have practical applications in policy development and evaluation.

Alice SuozziView profile

Research Fellow

• Linguistics
• Syntax
• Semantics
• +6 more

Alice Suozzi is a Research Fellow at the Department of Comparative Linguistic and Cultural Studies, Ca' Foscari University of Venice. She earned her cum laude PhD in Modern Languages, Cultures, and Societies and Language Sciences (Language Sciences curriculum) in 2024 after completing her cum laude Master’s in Italian Studies and Linguistic Sciences at the University of Bologna in 2019. Her research focuses on the syntax-semantics interface, Italian verb argument structures, and language acquisition, particularly instrument realization in typologically diverse languages. Education PhD in Lingue, culture e società moderne e scienze del linguaggio (Curriculum: Scienze del Linguaggio), Ca' Foscari University of Venice (2024) Master’s in Italianistica, Culture Letterarie Europee e Scienze Linguistiche (LM-14 - MODERN PHILOLOGY), University of Bologna (2019) Certificat de Spécialisation en Linguistique, Université de Genève (2024) Research Interests center on Italian syntactic optionality, instrumental argument structures, computational linguistics benchmarks, and comparative studies between spoken and sign languages. Her work integrates experimental methods, corpus analysis, and pre-trained language models to investigate how children acquire complex morphosyntactic structures and how instruments function in Italian dialects and cross-modal communication. Scientific Activities include organizing seminar series like Inclubembo and contributing to conferences such as Zadar Linguistic Forum, LSRL-53, and NOCroDeP 2023. Her publications address topics ranging from Italian clitic pronouns to syntactic bootstrapping hypotheses, with a 2025 article introducing the BAMBI baby language model for Italian.

Vasilije PerovicView profile

Associate Professor

• Matrix Theory
• Numerical Linear Algebra
• Nonlinear Eigenvalue Problems
• +5 more

Vasilije Perovic is an Associate Professor in the Department of Mathematics and Applied Mathematical Sciences at the University of Rhode Island's College of Arts and Sciences. He joined the university as an Assistant Professor in 2015 and was promoted to Associate Professor in 2023. Dr. Perovic's primary research focuses on matrix theory and numerical linear algebra, with special emphasis on nonlinear eigenvalue problems. His work extends to applications of linear algebra in data science and the development of scientific algorithms for shared and distributed memory systems. His research bridges theoretical mathematics with practical computational applications, particularly in the areas of matrix polynomials, singular value decomposition, and parallel computing. His publication record demonstrates a consistent focus on numerical methods for matrix computations, with recent work centered on hybrid algorithms for singular value decomposition and properties of matrix polynomials. His research shows a progression from theoretical aspects of matrix polynomials to practical computational algorithms with applications in data science. Dr. Perovic has collaborated extensively with James Baglama and other researchers at the University of Rhode Island. His work on software development includes MATLAB implementations of advanced singular value decomposition algorithms that are publicly available. He earned his Ph.D. in Mathematics from Western Michigan University in 2015, with a dissertation titled 'Spectrally Equivalent Matrix Polynomials: Non-standard Representations and Preservation of Structure,' advised by Professor D. Steven Mackey. He also completed his B.Sc. in Applied and Computational Mathematics at Western Michigan University, graduating Summa cum laude and as a member of Phi Beta Kappa and Pi Mu Epsilon honor societies.

• Econometric Time Series Analysis
• Macro-Econometrics
• Co-Integration
• +3 more

Heino Bohn Nielsen is a Professor at the Department of Economics , University of Copenhagen , specializing in Econometric Time Series Analysis , Co-Integration , Financial Econometrics , and Bootstrap Methods . He holds a PhD in Economics from the University of Copenhagen (2004). His research focuses on econometric theory and methodology, particularly in volatility modeling and simulation-based inference. He has led major research projects such as " Econometric Modeling of Instabilities in Financial Time Series " (2011–2013) and " Theory of the Bootstrap in Econometric Models with Time Varying Volatility " (2017–2021). His recent publications highlight advancements in unit root testing , GARCH-X models , and penalized quasi-likelihood estimation , reflecting his expertise in handling boundary parameters and instability in econometric models. He serves as an Associate Editor for the Oxford Bulletin of Economics and Statistics and has refereed for top journals like the Journal of Econometrics and Journal of Applied Econometrics . His teaching has been recognized with multiple Invisible Hand Awards and a Department of Economics Teaching Award .

Andrew WoodView profile

Professor

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +5 more

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

• Computational Statistics
• Nonparametric Regression
• Survival Analysis
• +4 more

Nora M. Villanueva is an Assistant Professor at the Department of Statistics and Operations Research, University of Vigo. She works at the intersection of computational statistics, nonparametric regression, and survival analysis, with applications to cybersecurity and industry 4.0. Her academic journey includes a PhD in Statistics and Operations Research from the University of Vigo (2021) and prior roles as a Data Scientist at Optare Solutions and Ecomanagement Technology. Education: PhD in Statistics and Operations Research (2021), University of Vigo Her research focuses on predictive modeling, nonparametric curve estimation, and software development for statistical analysis. She has contributed to the creation of four R packages and participated in 17 research projects and 39 conferences. Her most recent publications explore group determination in survival curves, explainable AI methods, and genomic sequence analysis. Her work combines theoretical statistics with practical applications in telecommunications, energy, and biomedical domains. Scientific Awards: Research Award (Science and Technology), Pontevedra Provincial Council (Spain) Premio a la mejor solución del reto IN1: 'Clasificación de ataques a honeypots SSH' Villanueva has extensive teaching experience at the University of Vigo, covering courses in statistics, regression models, and data analytics. She also contributed to industry workshops on predictive modeling and R package development.

Kirsten SchorningView profile

Assistant Professor

• Design of experiments
• Biostatistics
• Model selection
• +3 more

Kirsten Schorning is a Junior Professor of Mathematical Statistics at the Department of Statistics, Faculty of Statistics, Technical University of Dortmund. She maintains her office in the Mathematics Building, Room 725, and leads a research group focused on statistical methodology development with applications in biostatistics and toxicology. Education: Bachelor's and Master's degree in Mathematics, Ruhr-University Bochum (2007-2012) Doctorate in Mathematics (Dr. rer. nat.), Ruhr-University Bochum (2012-2016) Dr. Schorning's research primarily centers on Design of Experiments , Biostatistics , and Model Selection and Averaging . Her work develops innovative statistical methodologies for optimal experimental design, particularly in drug combination studies, toxicological assessments, and dose-response modeling. She has made significant contributions to addressing day-to-day variability in toxicity assessments and developing optimal concentration designs for in vitro experiments. Analysis of her recent publications (2019-2025) reveals a strong focus on optimal experimental design methodologies with applications in pharmaceutical research and toxicology. Her work bridges theoretical statistics with practical applications, particularly in determining effective drug doses, analyzing mixture toxicity, and developing robust statistical approaches for concentration-response relationships. A notable trend is her increasing focus on addressing real-world complexities such as day-to-day variability in toxicity assessments and developing guidance frameworks for toxicological dose-response experiments. Dr. Schorning leads a research group at TU Dortmund that includes team members Sabine Bell (secretary), Pauline Baur, Julia Eichhorn, Justin Lamberti, and Onur Gül. Her research has significant implications for pharmaceutical development, environmental toxicology, and clinical trial design, providing statisticians and researchers with improved methodological tools for experimental planning and analysis.

• Cosmology
• Quantum Field Theory
• String Theory
• +4 more

Enrico Pajer is a Lecturer at the University of Cambridge , affiliated with the Department of Applied Mathematics and Theoretical Physics and part of the General Relativity research group . His academic career includes previous roles as Assistant Professor at Utrecht University, Research Associate at Princeton University, and Postdoc at Cornell and Princeton. Education : PhD from Ludwig Maximilian University of Munich (2005-2008), Bachelor and Master from Scuola Normale Superiore and University of Pisa (2000-2005) Research Interests span theoretical cosmology, quantum field theory, and string theory. Key areas include: Inflationary cosmology Large-scale structure formation Cosmic microwave background Quantum field theory in curved spacetime String theory applications General relativity Publications focus on topics like inflationary unitarity bounds, open effective field theories, cosmological cutting rules, and graviton interactions, with a strong emphasis on quantum gravity and early universe dynamics. Contact : Email: enrico.pajer@gmail.com Room: B1.02, University of Cambridge Phone: 01223 766918 Personal Homepage: http://www.damtp.cam.ac.uk/user/ep551/index.html

• Enumerative Geometry
• Quantum Algebra
• Quantum Topology
• +7 more

Professor Gaëtan Borot is a faculty member at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Humboldt-Universität zu Berlin. He leads a research group in Mathematical Physics with connections to quantum field theory, string theory, and geometric analysis. His work is supported by multiple Research Training Groups including Rethinking QFT (2019-2029), From geometry to numbers (2024-2029), and the College of Mathematics and Physics Berlin (2020-2029). Professor Borot's research centers around topological recursion and its applications across mathematical physics. His work connects enumerative geometry, quantum algebra, moduli spaces, and random matrix theory through a unifying framework that reveals deep connections between seemingly disparate fields. He investigates how topological recursion provides methods to study quantization of geometric objects and field theories, with applications ranging from Gromov-Witten theory to statistical physics models on random surfaces. Analysis of his recent publications (2021-2024) shows a strong focus on the mathematical structures underlying conformal field theories, particularly Liouville CFT, where he explores Virasoro representations, scattering matrices, and equations of motion. His work on topological recursion continues to expand into new domains including cohomological field theories, Hurwitz theory, and spectral curves, demonstrating the versatility of this algebraic structure across mathematical physics. Professor Borot actively supervises PhD students including Giacomo Umer (who successfully defended in May 2025), Davide Scazzuso, and Niklas Martensen, along with numerous master's and bachelor's students. He organizes the Mathematical Physics Seminar held weekly in Adlershof and has taught courses on integrable systems, random matrix theory, and differential geometry. Despite currently being on leave until September 2025, he continues to supervise thesis projects with availability starting October 2025.