Social Analytics Lab
The Social Analytics Lab is a collection of MIT Faculty, Post Doctoral Research Fellows and PhD students working at the forefront of research on digital social networks, machine learning and social dynamics on the Web. Sinan launched the lab in 2007 while at NYU and brought it to MIT in 2013. The team has produced award-winning research on information diffusion in online social networks, the spread of fake news online, social media manipulation of elections, digital paywall design, optimal experimentation policies, measuring economic network effects, marketplace experimentation, the pricing and bundling of digital goods, among many other topics. The team works directly with the world’s leading companies like Facebook, Twitter, LinkedIn, WeChat, Yahoo, AirBnB, Jet.com, Microsoft, IBM, Intel, Cisco, Oracle, SAP and many other leading Fortune 500 firms. Join us!
Director
Sinan Aral is the David Austin Professor of Management, IT, Marketing and Data Science at MIT, Director of the MIT Initiative on the Digital Economy (IDE) and a founding partner at Manifest Capital.
Co-Director
Dean Eckles is a social scientist and statistician. Dean is the KDD Career Development Professor in Communications and Technology at the Massachusetts Institute of Technology (MIT), an associate professor in the MIT Sloan School of Management, and affiliated faculty at the MIT Institute for Data, Systems & Society. He was previously a member of the Core Data Science team at Facebook. Much of his research examines how interactive technologies affect human behavior by mediating, amplifying, and directing social influence — and statistical methods to study these processes. Dean’s empirical work uses large field experiments and observational studies. His published papers appear in Proceedings of the National Academy of Sciences, Journal of the American Statistical Association, Science, and other peer-reviewed journals and proceedings in statistics, computer science, and marketing. Dean completed five degrees, including his PhD, at Stanford University.
Current Members
Paramveer Dhillon is an Assistant Professor in the School of Information at the University of Michigan. He is also a digital fellow at the Initiative on the Digital Economy (IDE) at MIT. Paramveer holds an AM in Statistics and a PhD in Computer Science from the University of Pennsylvania where his PhD dissertation won the “Morris and Dorothy Rubinoff Best Dissertation Award.” He’s an expert in statistical and econometric modeling of “big” datasets. His research has been published in several Machine Learning venues including JMLR, NeurIPS, ICML, AISTATS, EMNLP, and ACL as well as general science venues such as Nature Human Behaviour.
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Christos Nicolaides is a Marie S. Curie Fellow and a Lecturer (US eq. of tenure-track Assistant Professor) at the Department of Business and Public Administration at the University of Cyprus (UCY). He has also a second research appointment as a Digital Fellow at the Initiative on the Digital Economy at MIT. Since 2019, he is also a visiting lecturer at the MSO department at London Business School. Before joining UCY, Christos spent three years as a Postdoc Fellow at the MIT Sloan School of Management funded by the James McDonnell Foundation, under the guidance of Sinan Aral. Christos has a PhD in Engineering from MIT, MSc in Applied Mathematics from Imperial College London and BSc in Physics from University of Thessaloniki. His research focuses on applying mathematical, statistical and computational tools to large-scale empirical research questions with focus on identification of social influence, and mitigation strategies during disease spreading. He has worked closely with firms to unlock the business value of social media. Christos’ research has been published in major scientific journals including Nature Communications and Journal of The Royal Society Interface and has been featured at prestigious news outlets including The New York Times, CNN and Los Angeles Times. He is principal institutional investigator in research projects funded by EU, industry (e.g., SMIXIN Inc.) and the CY Ministry of Health and has totally attracted more than 1M euros in research funding.
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Seth Gordon Benzell is a postdoctoral associate at the MIT Initiative on the Digital Economy in the group on Productivity, Employment, and Inequality. He holds a PhD in Economics from Boston University. Most of his work is in digitization and networks. He has studied the role of social networks in driving platform monopolies, European war and dynastic marriage, and in the spread of COVID-19. He is also interested in automation and public economics generally. He will be joining Chapman University’s Argyros School of Business and Economics in Fall 2020.
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Naghmeh Momeni obtained her PhD in electrical engineering from McGill University. The title of her dissertation was “The structure of social networks: modeling, sampling, and inference.” She focused on the problem of inferring the structure of large-scale social networks from a small set of observations. She also studied the structural inequalities in online social networks, and network formation models that generate these properties. She then became a visiting researcher at Harvard University, where she worked on the dynamics of cooperation on social networks, and how network structure affects societal cooperation and coordination. She is currently a postdoctoral fellow at MIT Sloan School of Management. From the lens of digital experiments, she studies how information and misinformation affect decision making in social networks.
Amin Rahimian is a postdoctoral research fellow at the MIT Institute for Data, Systems, and Society (IDSS), with a joint appointment at the MIT Sloan School of Management. He is joining the University of Pittsburgh, Department of Industrial Engineering as an assistant professor. He received his PhD in Electrical and Systems Engineering from the University of Pennsylvania, and Master’s in Statistics from Wharton School. He works at the intersection of networks, data, and decision sciences. He borrows tools from applied probability, statistics, algorithms, as well as decision and game theory, to address problems of distributed inference and decentralized interventions in large-scale sociotechnical systems.
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Jenny Allen is a PhD student in the Marketing group at the MIT Sloan School of Management interested in misinformation, news, and social networks. She graduated with a BA in Computer Science and Psychology from Yale in 2016. Prior to MIT, she worked at Facebook as a software engineer and Microsoft Research as a Research Assistant for the Computational Social Science group.
Cathy Yiqun Cao is a PhD candidate in Quantitative Marketing at the MIT Sloan School of Management. Her recent work focuses on video analytics and observational studies. Cathy received her BS from Carnegie Mellon University.
Avinash (Avi) Collis is a PhD candidate in Information Technologies at the MIT Sloan School of Management and an incoming Assistant Professor in the Information, Risk and Operations Management department at the McCombs School of Business at the University of Texas at Austin (starting July 2020). His research interests include the economics of digitization focusing on measuring the welfare gains from digital goods. His research has been published in the Proceedings of the National Academy of Sciences and the Harvard Business Review and has been cited in major media outlets and policy reports including The Wall Street Journal, Washington Post, The Economist and the annual report of the White House Council of Economic Advisers.
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David Holtz is a PhD candidate in the Information Technology group at the MIT Sloan School of Management. His research interests span online marketplace and platform design, causal inference, applied machine learning, and network science. Prior to beginning his PhD, he worked in the private sector as a data scientist, most recently at Airbnb. As an academic researcher, he has conducted research with a number of firms, including Airbnb, Facebook, and Spotify. David holds an SM in Information Technology from MIT, an MA in Physics & Astronomy from Johns Hopkins University, and a BA in Physics from Princeton University.
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Madhav Kumar is a PhD student working with Sinan Aral and Dean Eckles. He is interested in consumer choice, recommendation systems, and targeting. He builds models for insights, scale, and fun.
Alex Moehring is a PhD student at the MIT Sloan School of Management in the Information Technology group interested in digital media and topics related to the digital economy. Before coming to MIT, he received a BS from the University of North Carolina at Chapel Hill.
Tara Sowrirajan is a PhD candidate in Computer Science at Harvard University and a visiting researcher at the MIT Media Lab. Her research centers around modeling human behavior, social decision making, and understanding the mechanisms behind social phenomenon such as inequity. She has also worked on analyzing wealth in networks to design fair taxation policies and psychologically driven models for social group formation and behavior prediction. Tara received her BS and MS in Computer Science from Caltech and Harvard.
Michael Zhao is a PhD candidate in the Information Technologies group at MIT Sloan. Michael studies spillover effects in digital media. His work combines modern machine learning techniques with traditional econometric methods to identify actionable causal insights from large-scale datasets. Recent projects focus on understanding how social networks and social media mediate social spillovers in peer behavior. Other projects focus on quantifying cross-channel spillover effects in digital advertising. Prior to starting his studies at MIT, Michael received a MA in Economics from New York University and a BA in Economics from the University of California, San Diego.
Alumni
Shan Huang is an Assistant Professor at the Foster School of Business, the University of Washington, Seattle. Her research aims to rethink the role of social networks in economics and organizations, leveraging the phenomena, data, and research tools enabled by new technologies. Her current work examines social advertising effectiveness, social referrals, and online content diffusion. She has published in prominent management journals, such as Marketing Science, and conducted research with leading tech firms, like Tencent. She received a bachelor’s degree from Tsinghua University, a master’s degree from the University of British Columbia, and a PhD degree from the MIT Sloan School of Management.
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Lev Muchnik is a Professor at the Hebrew University of Jerusalem, School of Business Administration, Internet Studies Department. Between 2008 and 2012, he was Senior research scientist at the Information, Operations Management and Statistics Department of the Leonard N. Stern School of Business at New York University. Lev earned his PhD in physics from Bar Ilan University. His expertise lies in the collection and analysis of massive data sets representing large-scale social systems, and their modeling using tools borrowed from social sciences and statistical physics. His recent work has been focused on theoretical and empirical problems related to the structure and evolution of social networks, as well as peer effects, the spread of behavioral norms, information diffusion, and other processes specific to networked environments. Jointly with collaborators, Lev developed a seminal method for the identification of peer influence in networks, and conducted large-scale randomized controlled experiments in online communities. His expertise includes the design of scalable microscopic simulations of complex multi-agent systems and time-series analysis, in particular of long-term memory and scaling characteristics of financial data.
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Sean J. Taylor is a research scientist manager at Lyft, where he works on causal inference, experimentation, and forecasting. Previously, he led the Statistics team within Facebook’s Core Data Science team, developing advancements in measurement, experimentation, survey modeling, forecasting, and machine learning. Sean earned his PhD in Information Systems from NYU’s Stern School of Business, as well as a BS in Economics from Wharton School. He specializes in using machine learning methods and randomized experiments for measurement, prediction, and policy decisions. Sean’s research spans a wide range of topics: forecasting, social influence, social networks, statistical methods for surveys and experiments, causal inference, and Bayesian modeling. He is also an avid engineer who enjoys putting research into practice by building tools and products.
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Dylan Walker is an Assistant Professor of Information Systems at the Questrom School of Business at Boston University. While he is a physicist by training, with a PhD in physics from Stony Brook University, he applies mathematical and computational tools from statistical physics and machine learning to large-scale empirical and data-oriented problems in computational social science. The primary focus of his research is on understanding how information, behaviors and economic outcomes diffuse through social and information networks. His work has been published in Science, Management Science, Information Systems Research and other venues. Recently, he is interested in the topics of skew, bias, polarization and misinformation in broadcast media, social media and digital information systems.