headshot of Amin Rahimian

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), Statistics (IE 2007), Design of Experiments (IE 1072), 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.

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.