headshot of Amin Rahimian

Amin Rahimian

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

overview

Dr. Rahimian is an assistant professor of Industrial Engineering at the University of Pittsburgh, where he leads the Sociotechnical Systems Research Lab and is also affiliated with the Intelligent Systems Program (ISP) and the Institute for Cyber Law, Policy, and Security (Pitt Cyber). 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 warfare. He is especially interested in the critical role that information plays in the operation of sociotechnical institutions and its societal implications, particularly related to privacy, fairness and information integrity (e.g., issues of social learning and spread of misinformation and other harmful content). 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), SocialSens2022 (Special Edition on Information Operation on Social Media), 2023 ACM Economics and Computation conference, 2024 Web Conference, 2024 Privacy-Preserving Artificial Intelligence workshop (PPAI), 2024 Theory and Practice of Differential Privacy workshop (TPDP), 2024 Annual Modeling and Simulation Conference (as the US and Canada publicity chair), and 2025 PPAI and Web Conference. He has published in the Proceedings of the National Academy of Sciences, Nature Human Behaviour, Nature Communications, the Operations Research journal, the Automatica journal, and several IEEE Transactions. At Pitt he has tought or is teaching Stochastic Processes (IE 2084), Foundations of Statistics (IE 2117), Design of Experiments (IE 1072), Advanced Topics in Operations Research (IE 3080, focusing on probabilistic analysis of algorithms and randomized algorithms), and 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

(2023) Meta Foundational Integrity Research Award.

(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.

Rahimian, M.A., & Colaresi, M.P. (2025). Democratic Resilience and Sociotechnical Shocks.

Burton, J.W., Almaatouq, A., Rahimian, M.A., & Hahn, U. (2024). Algorithmically mediating communication to enhance collective decision-making in online social networks. Collective Intelligence, 3(2), 26339137241241307.SAGE Publications. doi: 10.1177/26339137241241307.

Eckles, D., & Rahimian, M.A. (2024). Long ties across networks accelerate the spread of social contagions. Nat Hum Behav, 8(6), 1012-1013.Springer Nature. doi: 10.1038/s41562-024-01866-z.

Eckles, D., Mossel, E., Rahimian, M.A., & Sen, S. (2024). Long ties accelerate noisy threshold-based contagions. Nat Hum Behav, 8(6), 1057-1064.Springer Nature. doi: 10.1038/s41562-024-01865-0.

Papachristou, M., & Rahimian, M.A. (2024). Differentially private distributed estimation and learning. IISE TRANSACTIONS, ahead-of-print(ahead-of-print), 1-17.Taylor & Francis. doi: 10.1080/24725854.2024.2337068.

Tiwari, K., Rahimian, M.A., Roberts, M.S., Kumar, P., & Buchanich, J.M. (2024). Measuring network dynamics of opioid overdose deaths in the United States. Sci Rep, 14(1), 29563.Springer Nature. doi: 10.1038/s41598-024-80627-4.

Fang, F., Liu, P., Song, L., Wagner, P., Bartlett, D., Ma, L., Li, X., Rahimian, M.A., Tseng, G., Randhawa, P., & Xiao, K. (2023). Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning. Front Immunol, 14, 1090373.Frontiers. doi: 10.3389/fimmu.2023.1090373.

Moehring, A., Collis, A., Garimella, K., Rahimian, M.A., Aral, S., & Eckles, D. (2023). Providing normative information increases intentions to accept a COVID-19 vaccine. Nat Commun, 14(1), 126.Springer Nature. doi: 10.1038/s41467-022-35052-4.

Papachristou, M., & Rahimian, M.A. (2023). Production Networks Resilience: Cascading Failures, Power Laws and Optimal Interventions. doi: 10.48550/arXiv.2303.12660.

Rahimian, M.A., Yu, F.Y., & Hurtado Campo, C.E. (2023). Seeding with Differentially Private Network Information. doi: 10.48550/arXiv.2305.16590.

Almaatouq, A., Rahimian, M.A., Burton, J.W., & Alhajri, A. (2022). The distribution of initial estimates moderates the effect of social influence on the wisdom of the crowd. Sci Rep, 12(1), 16546.Springer Nature. doi: 10.1038/s41598-022-20551-7.

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, behaviors and norms. Nature Human Behaviour, 6(9), 1310-+.Nature Research. doi: 10.1038/s41562-022-01347-1.

Eckles, D., Esfandiari, H., Mossel, E., & Rahimian, M.A. (2022). Seeding with Costly Network Information. Operations Research, 70(4), 2318-2348.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2022.2290.

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.

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. Proc Natl Acad Sci U S A, 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. (2018). Long Ties Accelerate Noisy Threshold-based Contagions. SSRN Electronic Journal.Elsevier. doi: 10.2139/ssrn.3262749.

Preciado, V.M., & Rahimian, M.A. (2017). Moment-Based Spectral Analysis of Random Graphs with Given Expected Degrees. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 4(4), 215-228.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNSE.2017.2712064.

Rahimian, M.A., & Jadbabaie, A. (2017). Bayesian Learning Without Recall. IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, 3(3), 592-606.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TSIPN.2016.2631943.

Ahmed, A.A., Rahimian, M.A., & Roberts, M.S. (2023). Inferring Epidemic Dynamics Using Gaussian Process Emulation of Agent-Based Simulations. In 2023 Winter Simulation Conference (WSC), 00, (pp. 770-780).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/wsc60868.2023.10408157.

Ahmed, A.A., Rahimian, M.A., & Roberts, M.S. (2023). Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 00, (pp. 1-4).Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/bhi58575.2023.10313496.

Rahimian, A., Yu, F.Y., & Hurtado Campo, C.E. (2023). Differentially Private Network Data Collection for Influence Maximization. In 2023 International Conference on Autonomous Agents and Multiagent Systems, (pp. 2795-2797).International Foundation for Autonomous Agents and Multiagent Systems.

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.

Sassine, J., Rahimian, A., & Eckles, D. (2022). Influence of Repetition through Limited Recall. In Proceedings of the Sixteenth International AAAI Conference on Web and Social Media, 16(1), 863-872.Atlanta, Georgia, USA.

Sassine, J., Rahimian, M.A., & Eckles, D. (2022). Influence of Repetition through Limited Recall. In Proceedings of the International AAAI Conference on Web and Social Media., 16, (p. 1).Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/icwsm.v16i1.19341.