My broad research interests are in multi-agent systems, computational economics and finance, and artificial intelligence. I apply techniques from algorithmic game theory, statistical machine learning, decision theory, etc. to a variety of problems at the intersection of the computational and social sciences. A major focus of my research has been the design and analysis of market-making algorithms for financial markets and, in particular, prediction markets — incentive-based mechanisms for aggregating data in the form of private beliefs about uncertain events (e.g. the outcome of an election) distributed among strategic agents. I use both analytical and simulation-based methods to investigate the impact of factors such as wealth, risk attitude, manipulative behavior, etc. on information aggregation in market ecosystems. Another line of work I am pursuing involves algorithms for allocating resources based on preference data collected from potential recipients, satisfying efficiency, fairness, and diversity criteria; my joint work on ethnicity quotas in Singapore public housing allocation deserves special mention in this vein. More recently, I have got involved in research on empirical game-theoretic analysis, a family of methods for building tractable models of complex, procedurally defined games from empirical/simulated payoff data and using them to reason about game outcomes.
Eric Gilbert is the John Derby Evans Associate Professor in the School of Information—and a Professor in CSE—at the University of Michigan. Before coming to Michigan, he led the comp.social lab at Georgia Tech. Dr. Gilbert is a sociotechnologist, with a research focus on building and studying social media systems. His work has been supported by grants from Facebook, Samsung, Yahoo!, Google, NSF, ARL, and DARPA. Dr. Gilbert’s work has been recognized with multiple best paper awards, as well as covered by outlets including Wired, NPR and The New York Times. He is the recipient of an NSF CAREER award and the Sigma Xi Young Faculty Award. Professor Gilbert holds a BS in Math & CS and a PhD in CS—both from from the University of Illinois at Urbana-Champaign.
Albert S. Berahas is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.
Harrison Crandall is a Web Developer and Social Media Assistant at MIDAS. He is a Senior at the University of Michigan, with a passion for Front-end Computer Science and Data Science. Harrison lives in Larchmont, New York and in 2015 he founded a company named Larchmont Web Design to create websites for local businesses. Prior to working at MIDAS he also worked as a Web Developer at an advertising agency in Norwalk, CT.
His research is broadly in the interplay of complex stochastic systems and big-data, including large-scale communication/computing systems for big-data processing, private data marketplaces, and large-scale graph mining.
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.
Dr. Soroushmehr’s research interests include the design and development of image processing methods applicable to computer-assisted clinical decision support systems, algorithm design and optimization.
I research how humans behave by observing the things we say, what we do, and who we are. My research combines linguistic analysis and network science together to understand behavior in its natural social context. I collaborate with colleagues from areas such as Psychology, Linguistics, Digital Humanities, and Sociology to improve our theories using data-driven insights and methodologies.
Image caption: Indians use online matrimonial websites to complement the traditional arranged marriage process. Data from these websites can reveal widespread attitudes on caste identity through individuals signaling their openness to marrying someone from a different caste, i.e., intercaste marriage. This figure shows a comparison of demographic factors affecting openness to intercaste marriage in family-posted (left) versus self-posted (right) matrimonial profiles on a major Indian website. Values for each factor reflect a logistic regression coefficients for predicting whether that individual will be open to intercaste marriage. The difference that social status as a function of education, income, affluence, and to some degree caste, drive attitudes, where lower social status individuals are less open to intercaste marriage. Significance levels for model coefficients are reported as ‘***’ for p<0.001 , ‘**’ p<0.01 , and ‘*’ p<0.05, and bars show standard errors. This figure is taken from a paper by
Ashwin Rajadesingan, Ramaswami Mahalingam, David Jurgens, “Smart, Responsible, and Upper Caste Only:Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles” in the Proceedings of the AAAI International Conference on Web and Social Media (ICWSM), 2019.
Current research includes a project funded by Toyota that uses Markov Models and Machine Learning to predict heart arrhythmia, an NSF-funded project to detect Acute Respiratory Distress Syndrome (ARDS) from x-ray images and projects using tensor analysis on health care data (funded by the Department of Defense and National Science Foundation).