Current ASA Member Roster
Research Bios of Committee Members:
Co-Chair Maria Cuellar
Maria Cuellar is an assistant professor in the departments of Criminology and Statistics and Data Science at the University of Pennsylvania. Her PhD is in statistics and public policy from Carnegie Mellon University. Her research is at the intersection of statistics and the law, particularly how and whether we can answer legal questions by using statistics. She has written about causal inference as it relates to the legal question of whether one can attribute an outcome (e.g. cancer) to an exposure (e.g. exposure to a harmful chemical). Maria has also written about the statistical foundations of forensic science. She studies the proper way to state a conclusion in pattern-matching disciplines (e.g. fingerprints, hair microscopy, toolmarks identification) and what information a forensic examiner should not have access to when analyzing evidence. She recently developed an algorithm for toolmark identification using 3D data. And she is currently studying the accuracy of facial recognition technology as it is used by law enforcement.
Co-Chair Claire Kelling
Claire Kelling is an Assistant Professor of Statistics at Carleton College in Northfield, Minnesota. She received her Dual PhD in Statistics and Social Data Analytics from Penn State. Claire’s research engages statistics, sociology, and data science in order to study and develop statistical methods to inform evidence-based policy. Her primary focus recently is on the development of statistical methods in spatial statistics for the analysis of policing data in partnership with community organizations. She is interested in ways to accurately characterize intricacies in policing patterns and their interactions with communities through information about individual officers and civilians as well as characteristics of events (location, time, injuries, etc.). Her primary interest at the moment is in police use of force data. Claire recently organized the Ingram Olkin Forum on “Statistical Challenges in the Analysis of Police Use of Force” in November 2023.
Greg Ridgeway
Greg Ridgeway is the Rebecca W. Bushnell Professor of Criminology and Professor of Statistics and Data Science at the University of Pennsylvania. Professor Ridgeway’s research involves the development of statistical, computational, and analytical methods to improve our understanding of crime and the functioning of the justice system. He has developed several methods for evaluating police performance, including racial bias in traffic stops, benchmarking individual officer performance, and assessing the risk of use-of-force. Previously, Prof. Ridgeway was the Acting Director of the National Institute of Justice, the Justice Department’s science agency charged with strengthening the social, physical, and forensic sciences in order to improve our understanding of crime and advance justice. Prior to working at the Department of Justice, Ridgeway was Director of the RAND Safety and Justice Program and the RAND Center on Quality Policing where he worked with numerous criminal justice organizations around the world. Prof. Ridgeway is a Fellow of the American Statistical Association and Fellow of the Academy of Experimental Criminology..
Hal Stern
Hal Stern is Distinguished Professor in the Department of Statistics at the University of California Irvine (UCI). He also serves as the Provost and Executive Vice Chancellor of UCI since March 2020. Professor Stern is known for his research on Bayesian statistical methods and for collaborative projects in the life sciences and social sciences. Current areas of research include statistical methods for studying human behavior (psychology, neuroscience) and studies of forensic science. He is co-director of the Center for Statistics and Applications in Forensic Evidence, funded by the National Institute of Standards and Technology, and has worked on developing novel approaches to the analysis of footwear impression and bloodstain pattern evidence. He regularly teaches workshops on probability and statistics to members of the forensic science community (practitioners, lawyers, judges). Professor Stern is a fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute for Mathematical Statistics.
Joseph Antonelli
Joseph Antonelli is an assistant professor in the Department of Statistics at the University of Florida. He received his PhD in biostatistics at Harvard University. His research involves the development of causal inference methodology to provide policy-relevant information in criminology research. Specifically, he has examined the effects of policies like neighborhood policing and neck-restraint bans to better understand their downstream consequences on crime and arrest rates, as well as how these effects may vary across different population subgroups. Additionally, he has studied racial bias in policing decisions and how we can make inferences using available data that comes with inherent limitations, such as police use-of-force data. His work aims to provide more robust evidence by relying on the weakest assumptions possible and assessing the sensitivity of findings to untestable assumptions.
George Mohler
George Mohler is the Daniel J. Fitzgerald Professor of Data Science and Chair of the Department of Computer Science at Boston College. Professor Mohler’s research focuses on statistical and deep learning approaches to solving problems in spatial, urban and network data science. He has recently worked on several criminal justice related projects, including a CDC funded grant to model the impact of law enforcement drug seizures on overdose, a NIJ funded grant assessing the impact of gunshot detection technology on reducing gun violence, and using machine learning techniques to study police use of force. Professor Mohler holds a Ph.D. in Mathematics from the University of California Santa Barbara.
Mikaela Meyer
Mikaela Meyer is a senior data scientist at the MITRE Corporation. Prior to joining MITRE, she received her PhD in statistics and public policy from Carnegie Mellon University. During her PhD studies, Mikaela's work focused on quantifying racial disparities in police use of force, developing an algorithm to predict whether an individual was at risk of receiving an especially lengthy sentence, and evaluating models used to predict recidivism in the criminal legal system. She also published a paper on changes in crime rates during the COVID-19 pandemic.
Alex Chohlas-Wood
Alex Chohlas-Wood is an Assistant Professor of Computational Social Science at New York University’s Department of Applied Statistics, Social Science, and Humanities. He also co-directs the Computational Policy Lab at Harvard Kennedy School. His research explores how machine learning, causal inference, behavioral science, and generative artificial intelligence can advance public policy, with a particular focus on applications in the criminal justice system. Recent research includes an algorithm to help prosecutors make race-blind charging decisions, behavioral interventions that help people avoid incarceration, and a framework for ethical algorithm deployment. Previously, he served as Director of Analytics for the New York City Police Department, where he created an algorithm to help investigators detect and respond to crime patterns across the city. He holds a Ph.D. in computational social science from Stanford University.
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Previous Members:
Edward Cheng, Vanderbilt Law School
Weiwen Miao, Haverford College
Qing Pan, George Washington University
Jana Asher, independent statistical consultant