Faculty

	Daniel B. Neill, Director of the ML4G Lab, is Professor of Computer Science, Public Service, and Urban Analytics at NYU’s Courant Institute Department of Computer Science, Robert F. Wagner Graduate School of Public Service, and Center for Urban Science and Progress. His research focuses on developing new methods for machine learning and event detection in massive and complex datasets, with applications ranging from medicine and public health to law enforcement and urban analytics. He works to create, deploy, and evaluate data-driven tools and systems that can improve the quality of public health, safety, and security. He received his MPhil from Cambridge University and his MS and PhD in Computer Science from Carnegie Mellon University. (webpage)
	Sam Adhikari is Associate Professor of Biostatistics in the Department of Population Health, NYU Langone School of Medicine. She joined NYU after a PhD in statistics at Carnegie Mellon University and postdoctoral research at Harvard Medical School. Her research interests lie in developing and implementing statistical and machine learning tools to solve problems motivated by real-world applications in medicine, global health and education. Her methodological work has focused on statistical social network analysis, penalized regression for longitudinal data, and Bayesian causal inference. She is also passionate about developing ML infrastructures in low- and middle-income countries and has been involved in initiatives to teach AI in Nepal through Nepal Applied Mathematics and Informatics Institute. (webpage)
	Bennett Allen is an Assistant Professor of Epidemiology in the Department of Population Health at the NYU Grossman School of Medicine, where he is affiliated with the Center for Opioid Epidemiology and Policy. His research evaluates programs and policies in substance use, overdose prevention, and behavioral health using epidemiological and machine learning methods. Current projects include a longitudinal evaluation of NYC overdose prevention centers, spatiotemporal prediction of overdose mortality risk in Rhode Island, and qualitative assessments of public health and safety partnership interventions. Dr. Allen received his PhD in Epidemiology from the NYU Grossman School of Medicine and MPA in Public Policy from the NYU Wagner School of Public Service. Prior to joining NYU, he worked in substance use and mental health policy for the New York City government.
	Magdalena Cerdá is a Professor and Director of the Center for Opioid Epidemiology and Policy in the Department of Population Health, NYU Langone School of Medicine. Her research focuses on the effects that state and national drug and health policies have on substance abuse trends, and on the ways in which the urban context shapes violence. Her currently funded research projects focus on the impact of cannabis laws and opioid policies on substance abuse, mental illness, and associated health problems in the United States and South America. This work includes application of Bayesian hierarchical spatio-temporal models, agent-based modeling, and machine learning approaches. (webpage)
	Edward McFowland III is an Assistant Professor of Technology and Operations Management at the Harvard Business School. He received his Ph.D. in Information Systems and Management from Carnegie Mellon University. His research designs and utilizes statistical machine learning methods for anomalous pattern discovery, statistical inference, and causal inference in non-standard and complex settings, to solve real-world business and policy problems. His broad research goal is to build bridges between machine learning and the social sciences: creating methodological innovations and utilizing them to answer substantive questions in public policy and management.
	John R. Pamplin II is an Assistant Professor in the Department of Epidemiology at the Columbia University Mailman School of Public Health. His overall program of research studies the consequences of structural racism and systemic inequity on mental health and substance use outcomes in the US. His current work focuses on the application of novel statistical and computational methods to assess racialized impact of enactment and enforcement of state-level policy interventions designed to curb the overdose crisis, with a specific lens on potential heterogeneity driven by the criminal legal system. John received his MPH and PhD in Epidemiology from the Columbia University Mailman School of Public Health, and his B.S. in Biology from Morehouse College.
	Ravi Shroff is Associate Professor of Applied Statistics at NYU’s Steinhardt School, with an affiliated appointment at NYU’s Center for Urban Science and Progress (CUSP). His research interests are broadly related to computational social science, and in particular the development and application of statistical methods to measure and improve the equity and efficiency of decision making. Ravi studied mathematics at UC San Diego (MS and PhD), applied urban science and informatics at CUSP (MS), and mathematics and economics at the University of Washington (BS). (webpage)
	Kimberly Villalobos Carballo is an Assistant Professor at the Tandon School of Engineering in New York University. Her research integrates optimization and multimodal machine learning to develop data-driven algorithms that address practical challenges, such as working with small datasets, enhancing robustness, and improving model interpretability. She is particularly passionate about improving the quality of services and operations in healthcare institutions, and a large part of her research has been inspired by collaborations with Brigham and Women’s Hospital, Hartford HealthCare, and UMass Memorial Health. Kimberly received her PhD in Operations Research from the Massachusetts Institute of Technology in 2024.

Students

	Eliza Berman is a first-year Ph.D. student in Computer Science at NYU’s Courant Institute, researching bias and fairness in AI and NLP. She focuses on measuring and mitigating automated bias in NLP systems and LLMs, and understanding real-world impact of this bias in high-stakes domains. Prior to NYU, she received a B.A. in Computer Science and Hispanic Studies from Brown University and worked at the HealthNLP Lab at the University of Tübingen. Her previous research includes developing tools for the integration of LLMs into precision oncology.
	Kate Boxer is a computer science PhD student at NYU’s Courant Institute. She has previously held positions at the University of Chicago’s Center for Data Science and Public Policy, MDRC, and NYU’s Open Networks and Big Data Lab. Her previous work includes developing tools for targeted preventive services for at-risk populations, evaluating education policy, and detecting bias in pretrial risk assessment tools. Some of her broad research interests include fair resource allocation, bias detection and correction, and evaluation of impacts in observational data.
	Julie Cestaro is a MA candidate at NYU’s Gallatin School of Individualized Study where she is focusing on fair and responsible machine learning and the impact of technology on society. Her industry experience includes machine learning engineering at Target and BuzzFeed, and she is currently working in machine learning at Apple. She has contributed to research at Partnership on AI and her current research focuses on auditing for intersectional biases.
	Gordon Dai is a final-year NYU undergraduate student double majoring in mathematics and philosophy. He is interested in the dynamics within and between machine learning-based systems and society from a theoretical perspective, through the language of mathematics and philosophy. In addition to his work on model multiplicity at ML4G, he is also involved in machine learning theory, multi-LLM-agent systems, and the philosophy of technology. His first-authored works have been accepted to NeurIPS, EAAMO, and KDD, alongside media coverage from MIT Technology Review and China Youth Daily and talks delivered at TEDx and Microsoft Ignite. He is a co-author of the AI ethics comic book “Humans, Ethics, and Robots: A Book for Children by Children” (Peking University Press, 2023).
	Sophia Deng is a Master’s student in the Applied Statistics for Social Science Research program at NYU. She is broadly interested in computational social science, focusing on the representation and inequitable treatment of marginalized groups in public systems. Her research lies at the intersection of machine learning and policy, aiming to uncover subgroup disparities and decompose treatment effects to inform more effective and equitable resource distribution.
	Jackson Oleson is an MS student studying Computer Science with a focus on AI/ML at NYU’s Courant Institute. He is interested in general applications and problems related to Machine Learning. His current research focus is on fair clustering and developing tools to expand the capabilities of pattern detection methods.
	Chinelo Onyebeke is a Data Analyst in the Department of Epidemiology at Columbia University Mailman School of Public Health. She currently works with Dr. John Pamplin in researching how the enactment of state-level overdose prevention policies affects opioid overdose rates, using machine learning methods. Prior to working at Columbia, Chinelo was a data analyst in the Bureau of Vital Statistics at the NYC Department of Health and Mental Hygiene, where she completed data requests and co-authored research that involved NYC birth and death data, such as her paper, “Birth equity on the front lines: impact of a community-based doula program in Brooklyn, NY”, and “Summary of Vital Statistics” reports for 2017-2021. Chinelo received her MPH in Epidemiology from Columbia University Mailman School of Public Health and her B.S. in Public Health from Rutgers University.
	Pavan Ravishankar is a Ph.D. student in Computer Science at NYU Courant. He is broadly interested in Responsible AI, including both developing fair machine learning algorithms and evaluating the societal ramifications of using AI. His current focus is to understand how bias propagates across various stages of the machine learning pipeline. He completed his M.S. in Computer Science at IIT Madras, where his thesis focused on developing algorithms to mitigate discrimination, and analyzing how AI can facilitate financial inclusion. (webpage)
	Michael Sanfilippo currently works as Director, Clinical Informatics and Information Technology at New York University. His academic background is in computer science. He is currently pursuing an MBA at the NYU Stern Langone program. Professionally he works on delivering technical solutions that focus on improving patient experience, reducing cost, advancing population health and improving the provider experience. His current research interest is exploring how pre-syndromic disease surveillance may be extended to incorporate additional electronic health record data.
	Era Sarda is a Master’s student in Computer Science at New York University’s Courant Institute. Her research interests focus on AI ethics and policy interventions for social good. She is currently working on ML applications in public health as well as examining the use of AI tools in policing. Prior to NYU, she earned her Bachelor’s in Mathematics and Computing from IIT Delhi and worked as a GenAI developer at a startup.
	Will Stamey is a fourth-year PhD student in Analytics at the University of Notre Dame Mendoza College of Business. His research focuses on tools for extracting information from data or data generating processes for practical and efficient decision making. These tools range from subgroup identification algorithms in observational and experimental causal inference to sequential experiment design for quickly selecting optimal policies or treatments.
	Jack Tinker is a final-year undergraduate student at New York University, majoring in Computer Science and Data Science. His interests include fair machine learning, model transparency, and environmental applications of AI. His current research focuses on developing and evaluating bias detection algorithms to promote equity and accountability in machine learning systems.
	Rachel Yuan is a Master’s student pursuing an MS in Computer Science at NYU Courant. Their interests include developing fair machine learning models and the application of machine learning to the benefit of marginalized groups of people. Prior to NYU, they received a BS in Computer Science from UNC Chapel Hill and worked as an SDE II at Amazon.

In Memoriam

Sriram Somanchi was an Associate Professor of Business Analytics at the Mendoza College of Business, University of Notre Dame. Prof. Somanchi made tremendous contributions to the development and deployment of novel statistical machine learning methods and the application of these methods to solve challenging real-world problems in domains including healthcare, digital experimentation, economic development, crowdsourcing, and social media. He received his Ph.D. in Information Systems and Management from Carnegie Mellon University, where he was one of Prof. Neill’s first doctoral students, and his M.E. from the Indian Institute of Science, Bangalore, India. As ML4G Lab affiliated faculty, Prof. Somanchi was deeply involved with many of our projects and helped to mentor many students over the years. We are all lucky that we had the opportunity to benefit from his wisdom and kindness, and he will be dearly missed.

Alumni

We are very proud of our lab’s many alumni, who continue to achieve great things!

Rozalen Adous: ARISE Summer Program, 2022.
Bennett Allen: Ph.D., Epidemiology, 2022. Assistant Professor, NYU Grossman School of Medicine; ML for Good Lab faculty.
Chris Anzalone: Master of Public Administration, NYU Wagner, 2024. MBA student, NYU Stern School of Business.
Martina Balestra: Smart Cities Postdoctoral Fellow, 2019-2021. Senior Applied Scientist, Uber.
Tamanna Begum: ARISE Summer Program, 2023.
Isaac Bohart: M.D./M.S. in Clinical Investigation, 2023. Internal Medicine Resident, Stanford Healthcare.
Jared Burke: CSTEP Research Initiative, 2022. Undergraduate student, New York University.
Ougni Chakraborty: M.S., Electrical Engineering, 2020. Machine Learning Engineer, Data.ai.
Elaine Chang: ARISE Summer Program, 2024. High school student, Benjamin Cardozo High School.
Peter Chang: Research Assistant, 2024. Senior Data Scientist, Keystone Strategy.
Boyuan Chen: Graduate Research Assistant, 2022. Ph.D. student, New York University.
Eric Corbett: Smart Cities / Provost’s Postdoctoral Fellow, 2020-2022. Research Scientist, Google Research.
Shizhan Gong: M.S., Data Science, 2020. Ph.D. student, Chinese University of Hong Kong.
Haorui Guo: B.A., Computer Science, 2021. Software Engineer, Two Sigma Investments.
Ellie Haber: B.S., Computer Science, 2022. Ph.D. student, Carnegie Mellon University.
Aanya Khanna: ARISE Summer Program, 2024. High school student, Stuyvesant High School.
Betty Li Hou: Graduate Research Assistant, 2022-2023. Ph.D. student, New York University.
Ben Jakubowski: Computer Science PhD student, 2019-2021. Director of Data Science, VNS Health.
Pranav Jangir: M.S., Computer Science, 2024. Ph.D. student, University of Massachusetts, Amherst.
Devashish Khulbe: M.S., Applied Urban Science and Informatics, 2019. Ph.D. student, Masaryk University.
Konstantin Klemmer: Ph.D., Computer Science, University of Warwick and NYU, 2022. Machine Learning Researcher, Microsoft Research.
Alexandra Lefevre: M.S., Mathematics, 2022. Economics Research Associate, JPMorgan Chase Institute.
Taisheng Li: B.A., Mathematics and Computer Science, and B.S., Finance, 2025.
Erica Liberman: ARISE Summer Program, 2022. Undergraduate student, Washington University in St. Louis.
Gabe Lora: CSTEP Research Initiative, 2021. B.S. Computer Science, 2023.
Neil Menghani: M.S., Mathematics, 2022. M.D. student, NYU Grossman School of Medicine.
Qingyu (Serene) Mo: B.A., Mathematics and Computer Science, 2022. Software Engineer, Microsoft.
Omotara Oloye: NSF CAMP Scholar, Summer 2022. Software Engineer, Meta.
John R. Pamplin II: Smart Cities / Provost’s Postdoctoral Fellow, 2020-2022. Assistant Professor, Columbia University; ML for Good Lab faculty.
Pragya Parthasarathy: B.A., Economics, 2022. Researcher, University of Chicago.
Katie Rosman: M.S., Computer Science, 2022. Data Scientist, Amazon.
Rushabh Shah: M.S., Computer Science, 2024. Machine Learning Engineer, TikTok.
Valay Shah: M.S., Computer Science, 2022. Software Developer, Invidi.
Michelle Vaccaro: Summer research assistant, 2021. Ph.D. student, MIT.
Sheng Wang: Smart Cities Postdoctoral Fellow, 2019-2021. Associate Professor, Wuhan University.
Andy Wei: B.A., Computer Science and Data Science, 2024. Scientist, Uber.
Boyuan Zhang: B.A., Computer Science and Data Science, 2024. M.S. student in Statistics, Stanford University.
Ryan Zhang: ARISE Summer Program, 2023. Undergraduate student, Columbia University.