Alexander RodríguezAssistant Professor, Computer Science and Engineering
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  1. A. Chopra, A. Rodríguez, B.A. Prakash, R. Raskar, T. Kingsley. Using Neural Networks to Calibrate Agent Based Models Enables Improved Regional Evidence for Vaccine Strategy and Policy. Journal Vaccine. (To appear). 2023.
  2. H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash. When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). August 2023.
  3. A. Chopra*, A. Rodríguez*, J. Subramanian, B. Krishnamurthy, B.A. Prakash, R. Raskar. Differentiable Agent-based Epidemiology. Proceedings of the 22th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). May 2023.
  4. A. Rodríguez. AI & Multi-agent Systems for Data-centric Epidemic Forecasting. Proceedings of the 22th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). May 2023. (Doctoral Consortium).
  5. A. Rodríguez, J. Cui, N. Ramakrishnan, B. Adhikari, B.A. Prakash. EINNs: Epidemiologically-informed Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). February 2023 (Oral Presentation)
  6. A. Rodríguez, H. Kamarthi, B.A. Prakash. Epidemic Forecasting with a Data-Centric Lens. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). August 2022. (Lecture-style Tutorial)
  7. A. Chopra, A. Rodríguez, J. Subramanian, B. Krishnamurthy, B.A. Prakash, R. Raskar. Differentiable Agent-based Epidemiological Modeling for End-to-end Learning. ICML 2022 Workshop AI for Agent-Based Modelling (AI4ABM @ ICML). Oral presentation – Best paper award.
  8. H. Kamarthi, A. Rodríguez, B.A. Prakash. Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. International Conference on Learning Representations (ICLR). April 2022.
  9. E. Cramer, et al. [collaborative effort of the COVID-19 Forecast Hub]. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. Proceedings of the National Academy of Sciences (PNAS). 2022.
  10. E. Cramer, et al. [collaborative effort of the COVID-19 Forecast Hub]. The United States COVID-19 Forecast Hub dataset. Scientific Data. 2022. Impact Factor: 6.4.
  11. H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash. CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting. The ACM Web Conference 2022 (WebConf). April 2022.
  12. H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash. When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting. Neural Information Processing Systems (NeurIPS). December 2021.
  13. A. Rodríguez*, N. Muralidhar*, B. Adhikari, A. Tabassum, N. Ramakrishnan, B.A. Prakash. Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). February 2021.
  14. A. Rodríguez, A. Tabassum, J. Cui, Jiajia Xie, J. Ho, P. Agarwal, B. Adhikari, B.A. Prakash. DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). August 2021.
  15. A. Rodríguez, B. Adhikari, A. González, C.D. Nicholson, A. Vullikanti, B.A. Prakash. Mapping Network States using Connectivity Queries. IEEE International Conference on Big Data (Big Data), Atlanta, GA. December 2020 (Long paper).