Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)
Eric F. Lock is an Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota's School of Public Health. He is also a Member of the Masonic Cancer Center (MCC) and has been at the University of Minnesota since 2014, after completing his PhD in Statistics from the University of North Carolina in 2012 and a postdoctoral fellowship in Statistical Genomics at Duke University in 2014. Lock's research focuses on developing methods for the analysis of multi-faceted high-dimensional data, particularly in "omics" fields such as genomics, metabolomics, and proteomics. His work emphasizes the integrated analysis of data from multiple sources (e.g., gene expression, metabolomics, imaging) or measured in multiple dimensions (e.g., multiple tissue types or body regions). He also specializes in exploratory factorization and clustering methods, and Bayesian nonparametric inference. His recent publications demonstrate significant contributions to tensor data imputation (BAMITA), matrix decomposition (EV-BIDIFAC), and methods for handling complex genomic data. His work bridges statistical theory with practical applications in molecular biology, addressing challenges in data integration across multiple biological measurement platforms. Delta Omega, Honorary Society in Public Health (2019) As an active researcher and educator, Lock serves on dissertation committees, including for Mykhaylo M. Malakhov who recently defended his PhD at the University of Minnesota School of Public Health. He is involved in organizing and participating in major conferences such as STATGEN 2025, demonstrating his leadership in the biostatistics community.
Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.
Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .
Bryan L. Sykes is an Associate Professor at Cornell University , with joint appointments in the Department of Sociology and the Jeb E. Brooks School of Public Policy . He also holds affiliate roles at multiple research centers, including the Center for Demography and Ecology (CDE) at the University of Wisconsin-Madison and the Berkeley Population Center at the University of California-Berkeley. Education: PhD in Sociology and Demography from University of California-Berkeley; BS in Sociology from University of Wisconsin-Madison His research spans demography , criminology , and social inequality , focusing on how institutions and policies shape disadvantage. He leads a $1.61M RCT on monetary sanctions' effects in California courts and develops innovative demographic methods to address inequality in incarceration, fertility, and mortality data. Recent publications emphasize global health metrics , criminal justice reform , and methodological advancements . He serves as Senior Associate Editor for Science Advances and previously held editorial roles at PLoS One and Sociological Perspectives .
Farzad Sabzikar is an Associate Professor in the Department of Statistics at Iowa State University, specializing in stochastic processes, fractional models, and optimization algorithms. He integrates mathematical theory with applications in machine learning and time series analysis. Education: PhD in Statistics (Michigan State University, 2014), MS in Mathematics (Sharif University, 2009), BS in Mathematics (Isfahan University of Technology, 2006) His research bridges fractional calculus and statistical modeling, focusing on tempered processes and their applications in turbulence analysis, geophysical flows, and high-frequency data. He employs wavelet methods and asymptotic theory to study heavy-tailed phenomena and long-range dependencies. Recent publications emphasize tempered fractional Brownian motion, stable noise modeling, and functional data analysis. Key trends include transient anomalous diffusion, machine learning for cognitive decline classification, and optimized signal processing techniques. Scientific Awards: None listed His work has implications for machine learning, geophysics, and astrophysics, though no formal advising, grant, or lab affiliations are detailed in available sources.
Ottar Bjornstad is a Distinguished Professor of Entomology and Biology at Pennsylvania State University, holding the Huck Chair of Epidemiology. He is affiliated with multiple research centers, including the Center for Infectious Disease Dynamics, One Health Microbiome Center, and Center for Mathematical Biology. His research focuses on population ecology, epidemiological modeling, and the mathematical foundations of infectious disease dynamics. He investigates how climate change affects ecological systems, transmission patterns of zoonotic diseases, and the interplay between host demographics and disease spread. Recent publications highlight his work on dengue virus transmission, vaccine distribution logistics, and age-structured epidemic models. His studies span disciplines including ecology, virology, and computational epidemiology. Elected to Norwegian Academy of Sciences and Letters (2021) He collaborates with institutions worldwide, addressing global health challenges like Lassa virus spread and measles persistence mechanisms. His methodological contributions include statistical frameworks for epidemic analysis and spatial synchrony metrics.
Cantay Caliskan is an Associate Professor at the Goergen Institute for Data Science, University of Rochester. He teaches Data Mining, Statistical Machine Learning, and the Data Science Capstone courses in the undergraduate and graduate data science curriculum. Bachelor of Arts, Brandeis University Master of Arts, Koç University PhD in Political Science, Computer Science, and Statistics, Boston University (2018) His research focuses on computational social science, computer vision, and generative AI, with applications in deep learning, network analysis, and AI ethics in social contexts. His recent publications span interdisciplinary topics including: Geo-cultural bias in AI-generated urban models (SimCityNet) Comparative religious text analysis using LLMs (HalalLLM vs. KosherLLM) Political polarization metrics through social media interactions Article trends highlight AI's role in addressing social science challenges, from electoral geography to disaster response optimization. His work integrates natural language processing, dynamic network modeling, and cross-cultural analysis. He contributes to advancing accessible AI systems (ACROSS) and understanding misinformation dynamics. No scientific awards listed in available data.
Mogens Fosgerau is a Professor at the Department of Economics, University of Copenhagen, with a research focus on discrete choice theory, rational inattention, transportation and urban economics, congestion modeling, and entropy-based frameworks. He has held an ERC Advanced Grant (2017-2023) and completed a Grand Solutions project for the Innovation Fund Denmark (2016-20). Education: Mathematical Economics (Aarhus University, 1990), PhD in Mathematics (University College London, 1992). Current affiliations: Department of Economics (University of Copenhagen), Faculty of Social Sciences. Former roles: Guest Professor at DTU (2022-2023), member of the Commission for Green Transition of Passenger Cars (2019-2021). His research explores the intersection of information theory and discrete choice models, addressing complex substitution patterns and endogeneity issues through generalized entropy frameworks. He applies these models to transportation planning, urban economics, and climate policy analysis. Recent publications focus on perturbed utility models, inverse product differentiation logit, and rational inattention in spatial choice contexts. His work bridges theoretical econometrics with practical transport and environmental policy challenges. Awards: Recipient of the 2021 Transportation Science Meritorious Service Award. Former Editor-in-Chief of Economics of Transportation (2012-2020). Advising and Grants: Leads research projects funded by the European Research Council and Innovation Fund Denmark. Has participated in policy committees including the Danish Environmental Economic Council (2019-2025) and the Committee on Public Transport Mobility (2023-24).
Zhe Zhang is an Assistant Professor of Innovation, Technology, and Operations at the Rady School of Management, University of California San Diego (UCSD). He holds a Ph.D. in Information Systems and Management from Carnegie Mellon University's Heinz College and dual bachelor's degrees in Economics and Statistics from Stanford University. His research focuses on the societal and spillover impacts of information technology, including fairness in algorithmic decision-making, sharing economy dynamics, and digital transformation effects. Ph.D., Carnegie Mellon University (Heinz College) B.S. in Mathematical and Computational Sciences, Stanford University B.A. in Economics (with honors), Stanford University His work spans disciplines like machine learning, applied microeconomics, and operations management. Current research includes analyzing cashierless retail technology's operational and behavioral impacts, algorithmic bias mitigation strategies, and the economic implications of Amazon Prime adoption. Key findings highlight how digital innovations reshape consumer demand, manufacturer strategies, and algorithmic fairness outcomes. Recent publications address: Bias amplification through data imputation in healthcare Strategic overfitting in data science contests Sharing economy's effect on durable goods markets He has presented at top conferences in information systems (CIST, WISE), economics (NBER), and computer science (KDD, FAccT). Awards include runner-up for the ACM SIGMIS Doctoral Dissertation Award (2019) and a POMS 2017 Supply Chain Management best paper finalist. Prior to his current role, Zhang worked as a part-time Data Creative staff member at DataKind (NYC) and was a 2016 fellow at the Data Science for Social Good Summer Fellowship (Chicago). He has also contributed to fairness methods in AI at Facebook and nonprofit research at NRDC and Union of Concerned Scientists.
Pamela P Martinez serves as an Assistant Professor at the University of Illinois Urbana-Champaign within the College of Liberal Arts & Sciences, holding concurrent appointments in Microbiology, Statistics, and the Center for Latin American and Caribbean Studies. Her research program integrates computational modeling with epidemiological data to address critical questions in infectious disease dynamics. Her primary research focuses on the interplay between ecological and evolutionary processes in pathogens, specifically investigating how climatic and demographic factors shape spatial-temporal disease patterns and how pathogen diversity influences public health interventions. She employs mathematical and computational approaches applied to longitudinal time-series data, with particular emphasis on SARS-CoV-2, dengue, malaria, and rotavirus. Her work bridges theoretical epidemiology with practical public health applications, often incorporating climate science and socioeconomic variables. Analysis of her 15 most recent publications (2021-2025) reveals consistent themes across computational biology, climate-infectious disease interactions, and pandemic response. Key trends include the development of novel modeling frameworks for pathogen evolution (e.g., ortholog refinement algorithms), quantification of climate drivers on tropical diseases, and rigorous assessment of intervention strategies during the COVID-19 pandemic. Her research demonstrates increasing integration of high-resolution mobility data, immune history analysis, and equity considerations in infectious disease modeling. No scientific awards are documented in the available information. Details regarding student mentorship, grant funding, laboratory infrastructure, or collaborative research teams are not provided in the source materials.
Associate Professor Feng Chen is a faculty member at the School of Mathematics & Statistics, University of New South Wales, specializing in statistical methodology development and applications. His research bridges theoretical statistics and practical implementations across financial modeling, spatiotemporal processes, and public health analysis. PhD in Statistics from University of Hong Kong (2008) MSc in Applied Probability & Statistics from Lanzhou University (2004) BSc in Mathematics from Lanzhou University (2001) Research focuses include: Nonparametric and semiparametric statistical methods Point process modeling with emphasis on Hawkes processes Statistical computing and algorithm development Applications to financial data, earthquake analysis, and public health Recent publications demonstrate methodological advances in: Hawkes process estimation with complex data structures Renewal process applications in seismology GARCH modeling with missing data Spatiotemporal clustering analysis Scientific recognition includes: UNSW Science Staff Impact Award (2023) Professional roles: Director of Research Postgraduate Studies (2023--) Associate Editor for multiple journals Statistics Honours Coordinator (2013-2018) Active participant in statistical societies
Ben K. Grunwald serves as Professor of Law at Duke University School of Law, where he has been a faculty member since 2017 after completing a Bigelow Fellowship at the University of Chicago Law School. His scholarly work bridges legal theory and empirical analysis to address critical issues in criminal justice systems. JD, PhD in Criminology, AM in Statistics, and BA from the University of Pennsylvania Clerked for the Honorable Thomas Ambro on the U.S. Court of Appeals for the Third Circuit Bigelow Fellow at the University of Chicago Law School (2016-2017) Professor Grunwald's research program centers on evidence-based criminal justice reform, with particular emphasis on prosecutorial power dynamics, sentencing fairness, juvenile justice boundaries, and police accountability mechanisms. His methodological approach integrates advanced statistical techniques with legal scholarship to produce actionable insights for policymakers. Current projects examine racial bias in criminal records, optimal decarceration strategies, and the labor market consequences of police discipline. His recent publications demonstrate a consistent focus on translating empirical findings into practical legal reforms, particularly in law enforcement practices and juvenile justice systems. Through interdisciplinary collaborations with criminologists and statisticians, his work challenges conventional wisdom using rigorous data analysis while maintaining strong connections to constitutional principles and procedural justice. Professor Grunwald teaches foundational courses including Criminal Law and Criminal Procedure: Investigation, along with specialized seminars in Criminology and Criminal Procedure that emphasize empirical methodologies. His curriculum development reflects his commitment to training future lawyers in evidence-based legal practice and policy analysis.
Johann Guilleminot is an Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. He joined Duke in 2017 after a Maître de Conférences position at Université Paris-Est. His research bridges computational mechanics, materials science, and uncertainty quantification, with applications in additive manufacturing, biomedical implants, and naval systems. Education: M.S. in Theoretical Mechanics, Lille University of Science and Technology (2005) Ph.D. in Theoretical Mechanics, Lille University of Science and Technology (2008) Habilitation in Mechanics, Université Paris-Est (2014) His work focuses on probabilistic methods for heterogeneous materials, stochastic solvers, and scientific machine learning. Recent projects include data-driven uncertainty quantification in molecular dynamics and additive manufacturing simulations, funded by the Army Research Office, NSF, and national laboratories. Scientific Awards: No specific awards listed in the provided text. Lab & Collaborations: Leads the Guilleminot Lab at Duke, collaborating with Sandia National Laboratories and the U.S. Naval Research Laboratory. Research spans atomistic-to-continuum coupling, inverse problems, and stochastic modeling for predictive simulations.
Ronald J. Glotzbach is an Associate Professor at Purdue University's West Lafayette campus within the School of Applied and Creative Computing . His work focuses on web programming and development , leading projects that integrate dynamic content, databases, and educational technologies . He has taught courses such as CGT 356 (Web Programming), CGT 353 (Interactive Media), and CGT 456 (Advanced Web Programming). B.S. in Computer Graphics Technology M.S. in Technology Ph.D. in Curriculum and Instruction (pursuing) His research explores leading-edge web technologies for delivering interactive content, emphasizing web-enabling software, dynamic media integration, and mobile programming . Key trends in his publications include RSS technologies in education , web-based evaluation systems , and geospatial data tools for environmental sciences. Scientific awards include: Outstanding Professor (2003, 2004) CGT Dwyer Award for Outstanding Undergraduate Teaching (2005) CGT Outstanding Un-Tenured Faculty Award (2008) Professor Glotzbach has led numerous student teams in CGT projects , served as SIGGRAPH Student Volunteer Chair (2003-2005) , and collaborated with industry partners like Boeing (F-15 Distributed Mission Trainer) and Microsoft (XML Documents Testing Team) . He also provided expert testimony in a copyright case (2006) for Wargo & French, LLP.
