headshot

Amin Rahimian

Assistant Professor
RAHIMIAN@pitt.edu
aminrahimian.github.io @aminrahimian Google Scholar Industrial Engineering

overview

Dr. Rahimian joined Pitt IE in the fall of 2020. Prior to that, he was a postdoc with joint appointments at MIT Institute for Data, Systems, and Society (IDSS) and MIT Sloan School of Management. He received his PhD in Electrical and Systems Engineering from the University of Pennsylvania, and Master’s in Statistics from Wharton School. Broadly speaking his works are at the intersection of networks, data, and decision sciences. He borrows tools from applied probability, statistics, algorithms, as well as decision and game theory. Some of his current focus is on the challenges of inference and intervention design in complex, large-scale sociotechnical systems, with applications ranging from online social networks, public health, e-commerce and collective decision/action platforms to modern civilian cyberinfrastructure and future battlefields. He is especially interested in the critical role that information plays in the operation of sociotechnical institutions and its societal implications, including on diversity, fairness, and privacy. He has served on the program committee of the 2021 ACM Economics and Computation conference and the 2022 IISE annual conference (as the operations research track co-chair), and is currently serving on the advisory council of the vaccine confidence fund (a new industry alliance), as well as the program committees of EAAMO'22 (Equity and Access in Algorithms, Mechanisms, and Optimization) and SocialSens2022 (Special Edition on Information Operation on Social Media). He has published in the Proceedings of the National Academy of Sciences, Nature Human Behaviour, the Operations Research journal, the Automatica journal, and several IEEE Transactions. At Pitt he teaches Stochastic Processes (IE 2084) and Design of Experiments (IE 1072), as well as a new engineering elective called "Data for Social Good" (IE 1171) that he has developed as part of the Pitt Year of Data and Society initiative.

about

(2022) Pitt Cyber Accelerator Grant.

(2022) Pitt Momentum Funds.

(2021) Facebook Statistics for Improving Insights, Models, and Decisions request for proposals, Finalist.

(2021) Most Inspiring Research Paper Award at the ACM Collective Intelligence Conference.

Collis, A., Garimella, K., Moehring, A., Rahimian, M.A., Babalola, S., Gobat, N.H., Shattuck, D., Stolow, J., Aral, S., & Eckles, D. (2022). Global survey on COVID-19 beliefs, behaviours and norms. Nat Hum Behav.Springer Science and Business Media LLC. doi: 10.1038/s41562-022-01347-1.

Hązła, J., Jadbabaie, A., Mossel, E., & Rahimian, M.A. (2021). Bayesian Decision Making in Groups is Hard. Operations Research, 69(2), 632-654.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2020.2000.

Moehring, A., Collis, A., Garimella, K., Rahimian, M.A., Aral, S., & Eckles, D. (2021). Providing normative information increases intentions to accept a COVID-19 vaccine. Available at SSRN 3782082.Center for Open Science. doi: 10.31234/osf.io/srv6t.

Almaatouq, A., Rahimian, M.A., Burton, J., & Alhajri, A. (2020). When social influence promotes the wisdom of crowds. Center for Open Science. doi: 10.31234/osf.io/f5ky3.

Holtz, D., Zhao, M., Benzell, S.G., Cao, C.Y., Rahimian, M.A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P.S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 117(33), 19837-19843.Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.2009522117.

Eckles, D., Mossel, E., Rahimian, M.A., & Sen, S. (2019). Long ties accelerate noisy threshold-based contagions. SSRN Electronic Journal.Elsevier BV. doi: 10.2139/ssrn.3262749.

Eckles, D., Esfandiari, H., Mossel, E., & Rahimian, M.A. Seeding with Costly Network Information. Operations Research.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2022.2290.

Banerjee, S., Mukherjee, N., & Rahimian, A. (2022). Deep learning for simulation-based Bayesian inference of hidden parameters in online reputation systems. In The 2nd Annual Artificial Intelligence in Management Workshop and Conference.USC Marshall.

Eckles, D., Mossel, E., Rahimian, A., & Sen, S. (2022). Long Ties Accelerate Noisy Threshold-based Contagions. In Conference on Network Science and Economics.The University of Chicago.

Burton, J., Hahn, U., Almaatouq, A., & Rahimian, A. (2021). Algorithmically Mediating Communication to Enhance Collective Decision-Making in Online Social Networks. In ACM Collective Intelligence Conference.