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Aaron Schein is an Assistant Professor in the Department of Statistics and the Data Science Institute at the University of Chicago. His interdisciplinary work bridges statistics, computer science, and social sciences, developing innovative methodologies for analyzing complex data across multiple domains. His educational background includes: PhD in Computer Science from University of Massachusetts Amherst (2019), advised by Hanna Wallach in the Machine Learning for Data Science lab MA in Linguistics (Computational Linguistics) from University of Massachusetts Amherst BA in Political Science from University of Massachusetts Amherst Schein's research focuses on developing statistical models and computational methods for analyzing modern large-scale data in the social and biomedical sciences. His work spans several interconnected areas: Bayesian statistics - developing novel hierarchical Bayesian models for complex data structures Causal inference - designing methodologies for estimating causal effects in observational and experimental settings Machine learning - creating tensor decomposition methods and dynamical systems for high-dimensional data Computational social science - applying data science methods to political science, economics, and international relations Biomedical applications - developing statistical methods for genomics and cancer research His recent publications demonstrate a clear trajectory of methodological innovation applied to substantive problems. Early work focused on Bayesian tensor decomposition for analyzing country-to-country event data in international relations. This evolved into Poisson-randomized gamma dynamical systems for modeling sequential count data. More recently, Schein has conducted large-scale digital field experiments to assess the causal effects of friend-to-friend mobilization on voter turnout in US elections, bridging computational methods with real-world political applications. His work increasingly intersects with large language models and their applications in social science measurement. Aaron Schein has published in top-tier venues including NeurIPS, ICML, ACL, KDD, and PNAS, demonstrating the interdisciplinary impact of his research across computer science, statistics, political science, and biomedical fields. Before joining the University of Chicago, Schein was a postdoctoral fellow in the Data Science Institute at Columbia University, working with David Blei and Donald Green. He has industry experience from internships at Google, Microsoft Research, and policy work at the MITRE Corporation. He also serves as a senior technical advisor at Ocurate and a research affiliate at PredictWise.

• Bayesian statistics
• Causal inference
• Machine learning
• +9 more

Gengsheng (Jeff) Qin is a Professor of Statistics in the Department of Mathematics and Statistics at Georgia State University (GSU), part of the College of Arts & Sciences. He holds a PhD in Statistics from the Hong Kong University of Science and Technology (1999), and has been at GSU since 2001, progressing from Assistant to Associate Professor, and then full Professor in 2013. His research focuses on statistical methods in diagnostic medicine, survival analysis, bootstrap and empirical likelihood techniques, nonparametric and semi-parametric regression, and applications of empirical processes in statistics. He has also contributed to discrete data analysis, Edgeworth expansion, and time series modeling. Education includes a B.S. in Mathematics (Lanzhou University, 1983), M.S. in Statistics (Sichuan University, 1992), and PhD in Statistics (HKUST, 1999). Prior to GSU, he held faculty positions at Sichuan University, China, from 1983 to 2001. His teaching spans courses like Biostatistics, Mathematical Statistics, and Survival Analysis. Recent work emphasizes statistical methodologies for medical diagnostics, including ROC analysis, verification bias correction, and cost regression models. His articles often integrate empirical likelihood techniques with real-world applications in healthcare and biostatistics. A notable focus is developing confidence intervals and inference methods for medical cost data and diagnostic accuracy metrics. While no explicit awards are listed, his extensive publication record and tenure reflect sustained contributions to statistical theory and applications. Teaching and mentorship roles include advising students across undergraduate and graduate programs, though specific advisee names are not documented. No lab affiliations or grant details are provided in the text.

• Statistics
• Biostatistics
• Diagnostic Medicine
• +9 more

Junnan He is an Assistant Professor in the Department of Economics at Sciences Po (temporarily on leave) and will be the Alliance Visiting Professor at Columbia University's Department of Economics starting Fall 2025. He holds a BSc in Mathematical Economics and Mathematics from the Australian National University (ANU), an MA in Economics from Washington University in St. Louis (WUSTL), and a PhD in Economics from WUSTL. His research focuses on econometrics, decision theory, model sparsity, stochastic choice models, and industrial organization. Key research interests include variable selection in economic models, random choice models, Bayesian methods, and the application of econometric techniques to understand decision-making processes. His work has been recognized through prestigious awards such as the WUSTL Dissertation Fellowship (2017), the Best PhD SPEED Presentation at the OxMetrics Conference (2018), and the ANR Young Researcher Grant (2024) for his project on Complex Decision Making. His publications span topics ranging from VC funding gender gaps to market concentration dynamics in renewables and theoretical advancements in utility models. He has contributed to both empirical and theoretical work in econometrics, bridging methodological innovation with applied economic questions. Professional activities include organizing the IO/Metrics Seminar and maintaining an active presence in academic networks. His research agenda emphasizes bridging gaps between econometric theory and real-world applications in decision-making and market structures.

• Econometrics
• Decision Theory
• Model Sparsity
• +3 more

Robert Brandon Gramacy is a Professor and Department Head in the Department of Statistics within the College of Science at Virginia Polytechnic and State University (Virginia Tech). He is also affiliate faculty in VT's Computational Modeling and Data Analytics (CMDA) program. Previously, he was an Associate Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business and a fellow of the Computation Institute. Dr. Gramacy specializes in computational statistics, focusing on areas where ideal modeling approaches are impractical or current solutions lack fidelity. His research spans Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. He develops new models, methods, and algorithms while remaining grounded in real-world applications across diverse fields including aerospace engineering, climate science, epidemiology, finance, and environmental management. His work on NASA rocket booster design led to the development of the tgp R package, which has found wide applicability across numerous scientific domains. His recent research focuses on large-scale surrogate modeling, particularly with local approximate Gaussian processes (laGP) and heteroskedastic Gaussian processes (hetGP). He has made significant contributions to predicting atmospheric drag of satellites in orbit and is currently leading an NSF-funded project to forecast phytoplankton blooms for drinking water quality management. His publications demonstrate a consistent focus on scalable methods for complex computer experiments and real-world applications. Savage Award for best Ph.D. thesis in Bayesian applied methodology (2006) Facebook faculty award for research on large scale surrogate modeling (2017) Outstanding Service Award from the ASA Section on Physical and Engineering Sciences (2022) Editor-in-Chief of Technometrics (since Jan 1, 2023) Lloyd S. Nelson Award and Youden Award for significant contributions to applied statistics Dr. Gramacy mentors numerous graduate students and postdocs, emphasizing reproducible research, high-quality software development, and clear scientific communication. His lab members work on cutting-edge problems in statistical methodology and applications across multiple domains. As Editor-in-Chief of Technometrics, he plays a key role in shaping publication standards for applied statistics research and has published guidance for authors on writing effective statistical papers.

• Bayesian modeling
• Statistical computing
• Monte Carlo inference
• +7 more

Scotland C. Leman is an Associate Professor and Director of Graduate Programs in the Department of Statistics at Virginia Tech's College of Science. He holds a Ph.D. in Statistics from Duke University (2007) and advanced degrees from Stanford University and UC Davis. His research focuses on Bayesian statistics, visual analytics, stochastic modeling, and applications in molecular evolution and epidemiology. Leman has developed innovative methods in statistical simulation and interaction-driven data analysis, bridging computational statistics with domain sciences like genetics and public health. Education: Ph.D., Statistics, Duke University (2007) M.S., Statistics, Duke University (2005) M.S., Scientific Computing & Computational Mathematics, Stanford University (2003) B.S., Mathematics (Cum Laude), UC Davis (2001) Research Interests: Leman's work integrates statistical theory with applied problems in genetics and epidemiology. He develops Bayesian methods for analyzing complex biological systems, including pathogen evolution, disease transmission dynamics, and chromosomal evolution in malaria mosquitoes. His visual analytics research emphasizes user-driven interaction with statistical models, enabling more intuitive data exploration in domains such as public health and environmental science. Awards: Mu Sigma Rho (National Statistics Honor Society) Phi Kappa Phi (National Honor Society) Pi Mu Epsilon (Mathematics Honor Society) Golden Key Teaching & Leadership: As Director of Graduate Programs, Leman oversees the department's graduate curriculum while teaching advanced courses in Bayesian statistics, stochastic modeling, and data analytics. His teaching emphasizes hands-on learning through interactive visualizations and real-world data applications. Labs/Teams: Collaborates with interdisciplinary teams in computational biology, epidemiology, and visual analytics. Active in malaria genomics research through projects on Anopheles mosquitoes and pathogen host adaptation.

• Bayesian statistics
• Visual analytics
• Statistical genetics
• +6 more

Jyotishka Datta is an Assistant Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech). Previously, he served as an Assistant Professor at the University of Arkansas (2016–2020) and completed a postdoctoral fellowship at Duke University and SAMSI. He holds a Ph.D. from Purdue University and degrees from the Indian Statistical Institute. His research focuses on Bayesian methodology for high-dimensional data, including shrinkage priors, sparse signal recovery, and applications in genomics, ecology, and crime forecasting. Notable contributions include work on Horseshoe priors and their extensions, as well as interdisciplinary projects in cancer genomics and public health. Key awards include the NSF CAREER Award (2025), the Robert and Sandra Connor Endowed Faculty Fellowship (2018–19), and the William J. Studden Publication Award (2013). His work bridges statistical theory with practical applications, including collaborations in oncology and environmental science. Recent articles explore Bayesian shrinkage methods, compositional data analysis, and applications in auditory neuroscience. Ongoing projects involve heavy-tailed priors for robust inference in astronomy and machine learning. He teaches courses in data analytics and statistical theory.

• High-dimensional data analysis
• Bayesian methodology and theory
• Shrinkage prior
• +7 more

Paul Rathouz is a Visiting Professor and former Chair in the Department of Population Health at Dell Medical School, University of Texas at Austin. He serves as Director of the Biomedical Data Science Hub, fostering collaborative data science initiatives in clinical and population health research. With a Ph.D. in Biostatistics from Johns Hopkins University, Rathouz has extensive experience in academic leadership, previously serving as Chair of Biostatistics and Medical Informatics at the University of Wisconsin and holding roles at the University of Chicago. His research focuses on methodological advancements in statistical modeling, including missing data analysis, generalized linear models, and outcome-dependent sampling designs. Applied work spans developmental psychopathology, sleep epidemiology, and arsenic exposure studies. He has led high-impact projects like longitudinal studies of speech development in children with cerebral palsy and interventions to improve provider-patient communication in surgery. Rathouz holds fellow status in the American Statistical Association (2015) and received the Van Ryzin Award from ENAR (1997). His collaborative approach emphasizes full lifecycle research involvement, from project inception to publication, and he actively contributes to workforce development in research methods within Dell Med. Professional affiliations include the American Statistical Association and International Biometric Society. The Biomedical Data Science Hub under his direction leverages UT Austin’s interdisciplinary strengths to advance translational science, particularly in health services research and surgical outcomes optimization.

• Biostatistics
• Epidemiology
• Developmental Psychopathology
• +5 more

James Hugh McVittie serves as an Assistant Professor in the Department of Mathematics and Statistics within the Faculty of Science at the University of Regina. His academic profile features active research engagement with contact details including email (james.mcvittie@uregina.ca) and office phone (306-585-5080), though no courses are scheduled for Spring/Summer 2025. McVittie's research program centers on Survival Analysis and Biostatistics, with specialized expertise in Statistical Inference for Combined Cohort Methodologies and Measurement Error. His methodological work addresses partially observed data structures, particularly focusing on right-censored and length-biased failure time data. Applications span health studies (including Parkinson's disease research using the Canadian Longitudinal Study on Aging) and modeling sports career durations, demonstrating translational impact across medical and social domains. Analysis of his 2022-2025 publications reveals three dominant research thrusts: (1) Advanced survival modeling for combined cohort data (incident/prevalent cohorts), (2) Bayesian nonparametric estimation techniques for censored data, and (3) Extreme value theory applications in environmental statistics with missing observations. His work consistently addresses methodological gaps in sample size determination, hazard modeling, and survival function estimation under complex data constraints. Scientific Awards No scientific awards were documented in the provided sources Advising and grant activities remain unreported in the available materials, with no mention of student supervision or funded research projects. Laboratory infrastructure and research team compositions were not specified in the institutional profiles or publication records.

• Survival Analysis
• Biostatistics
• Statistical Inference
• +2 more

Matteo Scandella is an Assistant Professor at the University of Bergamo , Italy, since February 2024. Previously, he served as a post-doctoral researcher at Imperial College London (2020–2024). He holds a PhD in Control Systems (2019) and advanced degrees in Computer Science Engineering from the University of Bergamo (Bachelor 2014, Master 2016). His research focuses on kernel-based machine learning techniques applied to system identification , nonlinear dynamics , and control systems , with emphasis on aerospace applications and mechatronics. He has developed methods for stable nonlinear system modeling, continuous-time system identification, and data-driven control strategies like SelfMPC. Education: Bachelor Degree in Computer Science Engineering (2014) – University of Bergamo Master Degree in Computer Science Engineering (2016) – University of Bergamo PhD in Control Systems (2019) – University of Bergamo Research interests span kernel methods (e.g., manifold regularization, RKHS), stability analysis of nonlinear systems, and data-driven control . His work bridges theoretical advancements with engineering applications such as health monitoring of aerospace actuators and urban traffic optimization. Recent publications highlight innovations in automated MPC tuning and graph-based system identification techniques. Teaching includes courses like Automatica (6 CFU) and laboratory modules in sustainable industrial systems. His research has been published in top journals like Automatica and conferences such as L4DC and SYSID.

• Control Systems
• System Identification
• Machine Learning
• +3 more

Bahaedin Khaledi is an Assistant Professor in the Department of Applied Statistics and Research Methods at the University of Northern Colorado (UNC), part of the College of Education and Behavioral Sciences. He holds a PhD in Statistics from the Indian Statistical Institute (2000), an M.Sc. from Shahid Beheshti University (1991), and a B.Sc. from Shahid Chamran University (1988, ranked 1st). His career includes visiting roles at institutions like Florida International University and Portland State University, alongside his primary faculty position at UNC since 2016. Dr. Khaledi's research focuses on stochastic comparisons, actuarial science, Bayesian analysis, and risk modeling. His work addresses challenges in insurance claims reserving, mortality forecasting, and reliability theory. Notable contributions include studies on policy limit allocations, stochastic orderings of systems, and machine learning applications in demographic projections. His publications span over 50 peer-reviewed articles in journals like Statistics and Probability Letters and Communications in Statistics . While no awards are explicitly listed, his extensive academic collaborations and global research engagements highlight his disciplinary impact. His teaching and advising roles at UNC further underscore his commitment to graduate education in applied statistics.

• Applied Statistics
• Probability Theory
• Actuarial Science
• +4 more

Justin Kinney is an Associate Professor at Cold Spring Harbor Laboratory and Interim Chair of the Simons Center for Quantitative Biology. His research combines mathematical theory, machine learning, and experiments to study gene regulation mechanisms. Research Focus: The Kinney Lab develops DNA sequencing tools to dissect biophysical mechanisms of gene regulation, with applications in bacterial transcriptional regulation and alternative mRNA splicing in humans. This work has implications for understanding diseases like Spinal Muscular Atrophy and Cancer. Key Methods: Massively Parallel Reporter Assays (MPRAs), information theory approaches, and machine learning algorithms for analyzing multiplex assays of variant effect (MAVEs). Education: Ph.D. in Physics from Princeton University B.A. in Physics and Mathematics from Cornell University Publication Trends: Recent work focuses on developing interpretable machine learning models for genomic data, biophysical modeling of sequence-function relationships, and mechanistic studies of splice-modifying drugs. Articles demonstrate strong cross-disciplinary integration of physics, computational biology, and molecular genetics.

• Biophysics
• Machine Learning
• Sequence-Function Relationships
• +1 more

Michael Baron is a Professor in the Department of Mathematics and Statistics at American University's College of Arts and Sciences. Previously, he spent 19 years at the University of Texas at Dallas before joining American University in 2014. His academic journey includes a University Diploma in Mathematics from St. Petersburg State University, Russia (1992), and a Ph.D. in Statistics from the University of Maryland (1995). Dr. Baron's research focuses on sequential analysis, change-point detection, and Bayesian inference, with applications spanning epidemiology, clinical trials, semiconductor manufacturing, actuarial science, energy finance, and cyber security. His work combines theoretical developments with practical implementations across multiple disciplines. His research has been supported by grants from the National Science Foundation, the National Security Agency, the Actuarial Foundation, and the Semiconductor Technical Council. Notably, he spent a year as an Academic Visitor at IBM T. J. Watson Research Center in 2003-04. His publication record shows a consistent trajectory of impactful research, with recent work focusing on change-point detection in multiple channels, applications of statistical methods to Gulf War Illness treatment, semiconductor manufacturing quality control, and developing novel approaches to sequential analysis. His publications span both theoretical statistics journals like Sequential Analysis and application-focused venues in computer science, engineering, and medical research. Fellow of the American Statistical Association (2013) Regents' Outstanding Teaching Award (2014) Abraham Wald Prize for the best paper in Sequential Analysis (2007) Elected Member of the International Statistical Institute (2024) Associate Editor of the Journal of Sequential Analysis Dr. Baron has successfully mentored twelve doctoral students, all of whom are employed in academic and research positions. His current research projects include 'Statistical methods for gaining precision in credibility estimation' funded by the Actuarial Foundation and 'ATD: Efficient online detection based on multiple sensors, with applications to cybersecurity and discovery of biological threats' funded by the National Science Foundation. He has also made significant contributions to statistical education through his widely used textbook 'Probability and Statistics for Computer Scientists,' now in its third edition.

• Sequential analysis
• Change-point detection
• Bayesian Inference
• +7 more

Peter Orbanz is a Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (UCL). He previously held roles as Assistant and Associate Professor in the Department of Statistics at Columbia University, and completed postdoctoral research at the University of Cambridge under Zoubin Ghahramani. His research focuses on machine learning, Bayesian nonparametrics, symmetry in statistical models, and dynamics. He has spent sabbaticals at the Isaac Newton Institute, Microsoft Research New England, and UC Berkeley. Orbanz earned his PhD in 2008 from ETH Zurich under Joachim M. Buhmann, with a thesis on Bayesian nonparametric models. His work bridges theoretical foundations of machine learning with practical applications, emphasizing symmetry properties and scalable inference methods. Current research includes causal uncertainty quantification, Gaussian process dynamics, and equivariant neural networks. He teaches advanced courses on probabilistic learning, Bayesian nonparametrics, and statistical theory at UCL and Columbia. His research group at UCL includes PhD students Hugh Dance, Kevin H. Huang, Vasco Portilheiro, and Vince Velkey. Former advisees include Morgane Austern (Harvard), Benjamin Bloem-Reddy (UBC), and Victor Veitch (Chicago). Orbanz has authored influential papers on topics such as exchangeable random structures, Gaussian universality, and scalable MCMC methods. His work often integrates concepts from probability theory, statistical physics, and computational neuroscience. He maintains active collaborations with institutions worldwide and contributes to open-source tools for Bayesian modeling.

• Machine Learning
• Bayesian Nonparametrics
• Statistical Theory
• +3 more

Ioanna Manolopoulou is a Professor of Statistical Science at University College London (UCL), affiliated with the UCL ELLIS Unit. She holds the role of Associate Director of the HDRUK-Turing PhD programme. Her research focuses on developing Bayesian models for interpretable inferences in practical applications, with emphasis on causal inference, health data science, and retail analytics. She leads a research group advising numerous PhD students and collaborates with institutions like dunnhumby ltd and the Alan Turing Institute. Education: PhD in Statistical Science from the University of Cambridge (supervised by Prof. S. Tavaré and Prof. S.P. Brooks) Postdoctoral research at Duke University and SAMSI (Statistical and Applied Mathematical Sciences Institute) Research Interests: Bayesian nonparametric methods for causal effects and heterogeneous treatment estimation Interpretability in mixture models and topic models Applications in health economics, retail analytics, and immune cell dynamics Key Contributions: Her work bridges statistical theory and real-world applications, with notable contributions in causal machine learning, retail transaction modeling, and rater agreement analysis. Recent projects include Bayesian causal forests for treatment effects and segmented topic models for grocery retail analysis. Advising & Students: Supervises over 20 current and past PhD students, including Alberto Caron (postdoc at Alan Turing Institute) and Ilina Yozova (presenting at 2024 causal inference conferences). Labs & Affiliations: Active in UCL's Statistical Science department and the ELLIS Unit, collaborating with industry partners like dunnhumby ltd to advance applied statistical modeling.

• Bayesian Statistics
• Causal Inference
• Health Data Science
• +4 more

Luca Ganassali is an Assistant Professor in the Department of Mathematics at Université Paris-Saclay and a member of the Inria Celeste team. Previously, he was a postdoctoral researcher at EPFL (BAN Chair) and completed his PhD at Inria Paris under Laurent Massoulié and Marc Lelarge. His research focuses on algorithmic fairness, optimal transport in statistical learning, statistical inference in graphs and matrices, and informational computational thresholds for random instances. Education: PhD in Computer Science from Inria Paris (2022), postdoctoral studies at EPFL (2022). Research interests include causal discovery, graph alignment, and the intersection of machine learning with mathematical statistics. He actively collaborates on projects like the PhD offer on handling unfairness in data through mathematical and machine learning approaches. His work bridges theoretical computer science with applied statistics, addressing challenges in alignment of graph databases, detection of correlations in tree structures, and developing efficient algorithms for sparse graph alignment. Recent contributions include foundational results on Gaussian-weighted graph alignment thresholds and impossibility results in partial graph recovery. Lab affiliations: Inria Celeste team. Advising: Currently seeking a PhD student for a project on mathematical statistics and machine learning.

• Algorithmic Fairness
• Optimal Transport
• Statistical Inference in Graphs
• +4 more