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)
	Sriram Somanchi is an Associate Professor of Business Analytics at the Mendoza College of Business, University of Notre Dame. His research interest is developing and deploying novel statistical machine learning methods to solve challenging real-world problems in various domains. His main application domain is healthcare, but he also works in digital experimentation, economic development, crowdsourcing, and social media. More broadly, his interdisciplinary research agenda is at the intersection of machine learning, computer science, statistics, information systems, and operations management. He received his Ph.D. in Information Systems and Management from Carnegie Mellon University and M.E from the Indian Institute of Science, Bangalore, India. (webpage)

Postdoctoral Researchers

Konstantin Klemmer is a postdoctoral researcher at Microsoft Research New England and part of the Machine Learning and Statistics group. His research focuses on the representation of geographic phenomena in machine learning methods, particularly in neural networks. Konstantin’s work is motivated by real-world challenges such as climate change and increasing urbanisation, combining technical and methodological research with application and deployment studies. Konstantin holds a PhD in Urban Science from the University of Warwick and spent time as a visiting student at NYU, as an Enrichment student at the Alan Turing Institute and as a Beyond Fellow at TUM and DLR. He obtained his Masters degree in Transportation from Imperial College London and University College London and an his Bachelors in Economics from the University of Freiburg (Germany). (webpage)

Students

	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 third-year NYU undergraduate student double majoring in mathematics and philosophy. He is interested in the dynamics between machine learning-based systems and society from a theoretical perspective. 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. He has delivered talks at TEDx and Microsoft Ignite, and his work has been covered by MIT Technology Review and China Youth Daily. He is a co-author of the book Humans, Ethics, and Robots: A Book for Children by Children (ISBN: 9787301338148, Peking University Press, 2023).
	Pranav Jangir is a Computer Science MS student at NYU Courant, with a interests in algorithms, machine learning, privacy, and game theory. Prior to joining NYU, Pranav worked in the Google Ads Quality team, where he focused on improving translation. He completed his bachelors in Mathematics from IIT Guwahati, where he conducted research on counting partition numbers for his thesis. Pranav is currently focused on designing social welfare maximising algorithms and the application of machine learning in healthcare.
	Jackson Oleson is an undergraduate student studying mathematics at NYU. He plans to attend NYU for an MS in 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 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 and uses machine learning methods to do so. Prior to working at Columbia University, Chinelo worked as 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 on research that involved NYC birth and death data, such as her research paper, “Birth equity on the front lines: impact of a community-based doula program in Brooklyn, NY” and the “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.
	Boyuan Zhang is an undergraduate student joint majoring in Computer and Data Science at NYU Courant and NYU Center for Data Science, along with double minors in Mathematics and Business Studies. He is passionate about transforming insight from data and is broadly interested in statistical inference, data science, machine learning, and their applications to healthcare, public policy, social science, and sports. In particular, he is interested in exploring Causal and Bayesian Inference techniques in observational settings.

Alumni

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

Rozalen Adous: ARISE Summer Program, 2022. Student, Brooklyn Technical High School.
Bennett Allen: Ph.D., Epidemiology, 2022. Assistant Professor, NYU Grossman School of Medicine.
Martina Balestra: Smart Cities Postdoctoral Fellow, 2019-2021. Senior Applied Scientist, Uber.
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.
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.
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.
Devashish Khulbe: M.S., Applied Urban Science and Informatics, 2019. Ph.D. student, Masaryk University.
Alexandra Lefevre: M.S., Mathematics, 2022. Economics Research Associate, JPMorgan Chase Institute.
Erica Liberman: ARISE Summer Program, 2022. B.S. 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.
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.
Ryan Zhang: ARISE Summer Program, 2023. B.A. student, Columbia University.