Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8
Pramita Bagchi is an Assistant Professor at the Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University. She holds a Ph.D. in Statistics from University of Michigan and completed postdoctoral research at Ruhr Universitat Bochum, Germany. Doctor of Philosophy (Ph.D.) in Statistics - University of Michigan, Ann Arbor (2015) Postdoctoral Researcher - Department of Mathematics, Ruhr Universitat Bochum, Germany (2015-2018) Master of Statistics - Indian Statistical Institute, Kolkata, India (2010) Bachelor of Statistics - Indian Statistical Institute, Kolkata, India (2008) Her research focuses on developing computationally efficient statistical methodologies for analyzing high-dimensional dependent data , including longitudinal, spatial, and time series observations. Applications span climate science, protein sequencing, medical imaging , and financial data . Methodologically, she explores functional data analysis, shape-constrained inference, asymptotic theory , and non-parametric methods . Current projects include frequency band analysis for functional time series and clinical data analytics for heart failure biomarkers . Dr. Bagchi has received research grants from the National Science Foundation (2022-2025) and INOVA Hospital (2020-2023). Her work emphasizes modeling data with minimal structural assumptions , addressing computational challenges in high-dimensional contexts.
Jeff Borggaard is a Professor of Mathematics at Virginia Tech, affiliated with the College of Science and the Interdisciplinary Center for Applied Mathematics (ICAM). His research focuses on numerical analysis, computational science, and control theory, with emphasis on optimization and control of systems governed by partial differential equations (PDEs). He specializes in sensitivity analysis, reduced-order modeling, and their applications in fluid dynamics and engineering systems. His work includes developing computational methods for PDE-constrained optimization, control of fluid flows, and uncertainty quantification. Key collaborations involve researchers at institutions like Florida State University and École Polytechnique de Montréal. Borggaard has been funded by agencies including the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF), supporting projects on model reduction, flow control, and energy-efficient building systems. Research highlights include advancements in proper orthogonal decomposition (POD) for turbulent flows, nonlinear balanced truncation techniques, and applications of reduced-order models in control and optimization. His contributions also extend to thermal energy modeling in buildings and parameter estimation in groundwater flow systems. Borggaard holds positions at both the Department of Mathematics (McBryde Hall) and ICAM (Wright House), and maintains active involvement in professional societies such as the Society for Industrial and Applied Mathematics (SIAM) and the American Mathematical Society (AMS).
Olalekan Babaniyi is an Assistant Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. His research focuses on inverse problems with applications in biomechanical imaging, computational mechanics, and PDE-constrained optimization. He develops mathematical techniques to improve medical imaging and environmental modeling, including real-time imaging of soft tissue mechanical properties and ice flow prediction. He teaches courses such as Probability and Statistics, Boundary Value Problems, and Partial Differential Equations. His work has been highlighted in media for innovative applications in noninvasive disease diagnosis and climate science. Babaniyi runs the Inverse Problems Seminar at RIT (InvPrS) and has published extensively in journals like The Cryosphere, Journal of Nonlinear and Variational Analysis, and Physics in Medicine & Biology. His research emphasizes uncertainty quantification, image denoising in biomedical contexts, and solving inverse problems in viscoelasticity and geophysics. While no personal awards are listed, his contributions have supported student achievements, such as Quinn Kolt’s Goldwater Scholarship in 2021.
Berwin Turlach is an Associate Professor in the School of Physics, Maths and Computing at The University of Western Australia, with a primary affiliation to the Mathematics and Statistics department. He holds secondary appointments in the UWA Medical School and the Institute for Paediatric Perioperative Excellence. His research focuses on computational statistics, smoothing methods, machine learning, and applications in healthcare analytics, dentistry, and sports science. Key research areas include nonparametric smoothing techniques using splines and wavelets, statistical computing for big data analysis, and methodological development in regression modeling with shape constraints. His work bridges theoretical statistics with applied domains like human milk composition studies, dental materials efficacy, and spatial epidemiology. Recent publications span topics from sensor-based gait assessment to geographic healthcare demand analysis. He has led projects on statistical software development (e.g., quadprog package) and contributed to interdisciplinary collaborations in biomedical research and public health policy analysis. Turlach has secured research grants including a project on shape-constrained smoothing techniques and an aging population study funded by the Channel 7 Telethon Trust. His work addresses UN Sustainable Development Goals related to health equity and innovation.
Megan Greischar is an Assistant Professor in the Department of Ecology and Evolutionary Biology at Cornell University, co-leading the Diversity and Inclusion initiative. She holds a Ph.D. from Pennsylvania State University (2014) and a B.S. from Indiana University (2007). Her research focuses on parasite evolution, particularly malaria infections, examining how ecological factors influence transmission strategies, host-parasite interactions, and disease dynamics. Key areas include developmental synchrony in malaria, plasticity in parasite responses to host environments, and the impact of vector ecology on disease spread. She leads the Greischar Lab, which employs mathematical modeling and statistical approaches to address these questions. Courses taught include Evolutionary Medicine and seminars on infectious disease ecology. Her work bridges theoretical and applied research, with implications for public health and drug resistance. Research interests span community ecology, population biology, and evolutionary processes, with a focus on malaria’s life history strategies. Her team develops models to predict parasite behavior under variable ecological conditions and analyzes time-series data to uncover adaptive cues parasites use. Recent work explores how resource limitations constrain virulence evolution and how vector ecology shapes transmission patterns. The lab also investigates pre-symptomatic disease transmission and synchronization mechanisms tied to host biological rhythms. Publications emphasize quantifying synchrony, transmission investment, and evolutionary trade-offs in malaria. While no scientific awards are listed, her contributions to malaria epidemiology and ecological modeling are notable. She advises students in the graduate programs of Ecology & Evolutionary Biology and mentors undergraduate research projects. The Greischar Lab collaborates with affiliated centers at Cornell, contributing to interdisciplinary efforts in infectious disease research and biodiversity conservation.
Zoltán Szabó is a Professor of Data Science at the Department of Statistics, London School of Economics (LSE). His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation. He has held roles such as Programme Director of the MSc Data Science program and is actively involved in academic service, including Area Chair positions at top conferences like NeurIPS and ICML. His work bridges theory and applications, with contributions to fields like safety-critical learning, economics, climate data analysis, and natural language processing. Education and Affiliations: While specific educational details are not explicitly listed, his career trajectory indicates advanced training in statistics and machine learning. He is affiliated with the LSE's Department of Statistics and the Turing Institute, contributing to academic leadership and interdisciplinary projects. Research Interests: His work emphasizes kernel-based methods, including kernel Stein discrepancies, Hilbert-Schmidt independence criteria, and shape-constrained prediction. Applications span finance, economics, robotics, and environmental data analysis. Recent projects include developing outlier-robust estimators and scalable algorithms for high-dimensional data. Publications: His recent work includes advancements in kernel methods for dependency testing, shape-constrained regression, and robust estimation. Key themes include improving computational efficiency, theoretical guarantees for kernel approximations, and practical applications in interdisciplinary domains. Awards and Grants: While no explicit awards are listed, his contributions to NeurIPS (e.g., a Best Paper Award in 2017) and his role in securing grants (e.g., Europlace Institute of Finance) highlight his impactful research. He also serves on editorial boards, including JMLR and ACM Transactions on Probabilistic Machine Learning. Students and Labs: Supervises PhD students in areas like functional data analysis and scalable computation. Collaborates with researchers on projects such as distribution regression and safety-critical learning, contributing to both theoretical and applied outcomes.
Kamesh Munagala is a Professor in the Computer Science Department at Duke University's Pratt School of Engineering. His academic career spans theoretical computer science with a focus on approximation algorithms, online algorithms, and computational economics. He has made significant contributions to resource allocation, decision making, and provisioning problems across various applications including data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Professor Munagala's research interests span several key areas in theoretical computer science: Theoretical foundations of approximation algorithms and online algorithms Computational economics and market design Resource allocation with fairness constraints Algorithmic game theory and mechanism design Persuasion and information revelation in optimization contexts Group fairness based on proportionality and stability His recent publications demonstrate a strong focus on fairness in algorithmic decision-making, particularly in societal contexts like school assignment and participatory budgeting. He has also made significant contributions to the theory of Bayesian persuasion and information disclosure in competitive settings. His work bridges theoretical computer science with practical applications in social choice, economics, and policy-making. Notable scientific achievements include: Best paper award at WINE 2018 for 'A simple mechanism for a budget constrained buyer' Multiple publications in top theoretical computer science conferences including STOC, SODA, and FOCS Significant contributions to the understanding of fairness in resource allocation Innovative work on metric distortion in social choice Professor Munagala has advised numerous students and collaborators, with recent work involving researchers such as Govind S. Sankar, Yiheng Shen, and Kangning Wang. His research has been supported by various grants, though specific grant details aren't provided in the available information. He teaches advanced courses in algorithms, including Algorithm Design, Randomized Algorithms, and Algorithmic Game Theory, shaping the next generation of theoretical computer scientists. His work has implications for real-world systems requiring fair and efficient decision-making, from school assignment algorithms to data exchange markets and civic budgeting platforms. He is actively engaged in both theoretical advancements and practical implementations of his research.
Ted Westling is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on nonparametric statistics, causal inference, survival analysis, and shape-constrained inference. He holds a PhD in Statistics from the University of Washington and a BS in Mathematics from Stanford University. His work emphasizes methodological advancements in causal inference, particularly in observational studies and survival analysis. Notable contributions include developments in nonparametric causal effect estimation, machine learning integration for treatment-specific survival curves, and isotonic regression techniques for monotone functions. Westling's recent publications explore topics such as debiased covariate-adjusted regression, clustered observational studies, and statistical robustness in causal hypothesis testing. His research bridges theoretical statistics with applied problems in healthcare, epidemiology, and social network analysis. He is actively involved in interdisciplinary collaborations, addressing challenges in global health, vaccine efficacy evaluation, and emergency medical services demand forecasting. His methodological innovations aim to enhance the reliability and applicability of statistical methods in real-world settings.
Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.
Dong Li is a Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political and Policy Sciences. His research focuses on econometrics, industrial organization (particularly antitrust issues), financial economics, and the Chinese economy. He holds a Ph.D. in Economics from Texas A&M University (2000), an M.A. in Quantitative Economics from Huazhong University of Science & Technology (1994), and a B.A. in Quantitative Economics from the same institution (1991). Li's work emphasizes methodological contributions to panel data models, spatial econometrics, and semiparametric estimation techniques. His recent studies include analyses of cartel behavior in agricultural markets, military aid's impact on terrorism, and the implications of securities transaction taxes in emerging markets. His research bridges theoretical econometrics with applied policy questions, particularly in antitrust and financial regulation contexts. His articles span topics ranging from Bayesian auction analysis to China's economic policies, showcasing interdisciplinary rigor. While no specific awards are noted, his extensive publication record reflects sustained academic influence. His research often addresses practical economic challenges, such as optimizing college admissions systems and evaluating currency valuation impacts on macroeconomic variables like inflation and output growth.
Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.
Iliopoulos Georgios is a Professor and Department Chair at the Department of Statistics and Actuarial Science, School of Finance and Statistics, University of Piraeus. He has held this position since 2015, following his progression from Assistant Professor (2003-2010) to Associate Professor (2010-2015) at the same institution. Education: 1993: B.A. in Mathematics, Department of Mathematics, University of Patras 1999: PhD in Statistics, Department of Mathematics, University of Patras Iliopoulos specializes in statistical theory and methodology, with particular expertise in Markov chain Monte Carlo methods, Statistical Decision Theory, and Scale parameter estimation. His research focuses on accurate inference under censorship and constrained inference arrangement, addressing fundamental challenges in statistical analysis of complex data structures. His work bridges theoretical statistics with practical applications in reliability analysis and survival analysis. His publication record shows consistent contributions to exact statistical inference methods, particularly for censored data and complex distributions like Laplace and Gamma. His research demonstrates expertise in both parametric and semiparametric approaches, with significant work on variance reduction techniques in computational statistics and solutions to the label switching problem in Bayesian mixture models. Iliopoulos teaches undergraduate courses including Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics), as well as the postgraduate course Computational Statistical Techniques for the Master of Science in Applied Statistics.
Professor Georgios Iliopoulos is a distinguished academic serving as Professor and Department Chair of the Department of Statistics and Actuarial Science at the University of Piraeus. With an extensive academic career spanning over two decades, Professor Iliopoulos has established himself as a leading expert in statistical theory and methodology. His leadership as Department Chair demonstrates his significant contribution to the academic community and his commitment to advancing statistical education and research. Professor Iliopoulos completed his educational journey at the University of Patras, earning his B.A. in Mathematics in 1993 followed by a PhD in Statistics in 1999. His academic career progressed through various institutions before he settled at the University of Piraeus, where he has held positions from Assistant Professor (2003-2010) to Associate Professor (2010-2015) and ultimately to Professor (2015-present). Professor Iliopoulos's research focuses on several key areas of statistical theory, with particular emphasis on Markov chain Monte Carlo methods, Statistical Decision Theory, Scale parameter estimation, Accurate inference under censorship, and Constrained inference arrangement. His work bridges theoretical statistics with practical applications, demonstrating how sophisticated statistical methods can solve real-world problems across various domains. His research has consistently addressed challenging theoretical questions while maintaining relevance to practical statistical challenges faced by researchers and practitioners. Analysis of Professor Iliopoulos's publication record reveals a strong focus on theoretical statistics with applications in reliability analysis, Bayesian inference, and censoring methodologies. His work demonstrates a consistent pattern of advancing statistical theory while maintaining practical relevance, particularly in the areas of parameter estimation, confidence interval construction, and inference with censored data. The interdisciplinary nature of his research is evident in publications spanning journals in statistics, biostatistics, and computational statistics. Professor Iliopoulos is actively involved in teaching both undergraduate and postgraduate courses. At the undergraduate level, he teaches Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics). For postgraduate students, he offers Computational Statistical Techniques as part of the Master of Science in Applied Statistics program. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of advanced statistical methods.
Dr. Joshua Meggitt is a Lecturer in Acoustical and Audio Engineering at the University of Salford's School of Science, Engineering & Environment, where he completed both his BEng in Acoustic Engineering (2015) and PhD in Acoustics (2017). He is a core member of the Acoustics Innovation Institute and leads the EPSRC-funded xDEA project in collaboration with University of Nottingham and major industry partners including Airbus, Siemens, and BAE Systems. His research focuses on vibro-acoustics and structural dynamics, specializing in: Component-based simulation methodologies Transfer Path Analysis and blocked force techniques Uncertainty quantification in experimental acoustics Hybrid experimental-numerical modeling approaches Recent work explores vibration-induced noise in electric aircraft systems, structural health monitoring using transmissibility functions, and sound insulation of timber building elements. Dr. Meggitt received the prestigious Tyndall Medal in 2023 for his contributions to acoustics as an early-career researcher. His work bridges academic research and industrial applications through collaborations with Boeing, Bentley, Dyson, and Bosch across automotive, aerospace, and consumer product sectors. He teaches Acoustic Labs to first-year students and Microphone/Loudspeaker Design to second-years while supervising final-year projects and PhD candidates. As early career representative for the IOA Noise and Vibration Engineering Committee and UK Acoustic Network's SIG-VA, he actively shapes the acoustics research community.
