Alexander Rodríguez
Ph.D. Candidate
School of Computational Science and Engineering
Georgia Institute of Technology

CODA Building S1349
756 W Peachtree St NW, Atlanta GA, 30308



CV: [PDF]

I am in the 2022-2023 academic job market for tenure-track positions.

I am a Ph.D. candidate in Computer Science at Georgia Tech, where I am fortunate to be advised by Prof. B. Aditya Prakash. I am interested in addressing technical challenges in machine learning and data mining motivated from disciplines facing pressing social needs, such as epidemiology and community resilience.

Our group is working several projects in response to the COVID pandemic. I'm leading our group's efforts in COVID forecasting with our model called DeepCOVID. Our forecasts are being featured by CDC and FiveThirtyEight. Check more details of our response to the pandemic at LINK.

Our research work on pandemic forecasting was awarded in several venues:

Recent news:

Preprints: (under review)

Note: * denotes equal contribution.
  1. Data-Centric Epidemic Forecasting: A Survey [arXiv]
    A. Rodríguez*, H. Kamarthi*, P. Agarwal, J. Ho, M. Patel, S. Sapre, and B.A. Prakash.

  2. Differentiable Agent-based Epidemiology [arXiv]
    A. Chopra*, A. Rodríguez*, J. Subramanian, B. Krishnamurthy, B.A. Prakash, R. Raskar

  3. PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series [arXiv]
    H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash


Selected publications:

  1. EINNs: Epidemiologically-Informed Neural Networks [arXiv]
    A. Rodríguez, J. Cui, N. Ramakrishnan, B. Adhikari, B.A. Prakash
    AAAI Conference on Artificial Intelligence (AAAI-23)

  2. Differentiable Agent-based Epidemiological Modeling for End-to-end Learning [arXiv]
    A. Chopra*, A. Rodríguez*, J. Subramanian, B. Krishnamurthy, B.A. Prakash, R. Raskar
    ICML 2022 Workshop AI for Agent-Based Modelling (AI4ABM @ ICML 2022). Oral presentation -- Best paper award.

  3. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US [medRxiv]
    Estee Cramer, et al. [collaborative effort of the COVID-19 Forecast Hub]
    Proceedings of the National Academy of Sciences (PNAS). 2022.

  4. Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [arXiv]
    H. Kamarthi, A. Rodríguez, B.A. Prakash
    International Conference on Learning Representations (ICLR 2022)

  5. CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting [arXiv]
    H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash
    The Web Conference (WebConf 2022)

  6. When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [arXiv]
    H. Kamarthi, L. Kong, A. Rodríguez, C. Zhang, B.A. Prakash
    Neural Information Processing Systems (NeurIPS 2021)

  7. Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [arXiv]
    A. Rodríguez*, N. Muralidhar*, B. Adhikari, A. Tabassum, N. Ramakrishnan, B.A. Prakash
    AAAI Conference on Artificial Intelligence (AAAI-21)
    Shorter version in NeurIPS 2020 Machine Learning in Public Health (MLPH) Workshop

  8. DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting [medRxiv]
    A. Rodríguez, A. Tabassum, J. Cui, J. Xie, J. Ho, P. Agarwal, B. Adhikari, B.A. Prakash
    AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-21)

  9. Mapping Network States using Connectivity Queries [arXiv]
    Alexander Rodríguez, B. Adhikari, A. González, C.D. Nicholson, A. Vullikanti, B.A. Prakash
    IEEE International Conference on Big Data 2020 (IEEE BigData 2020)
    Shorter version in NeurIPS 2020 Artificial Intelligence and Humanitarian and Disaster Relief Workshop (AI + HADR @ NeurIPS 2020)

  10. NetReAct: Interactive Learning for Network Summarization [arXiv]
    S.E. Amiri, B. Adhikari, J. Wenskovitch, A. Rodríguez, M. Dowling, C. North, B.A. Prakash
    NeurIPS 2020 Human And Machine in-the-Loop Evaluation and Learning Strategies Workshop (HAMLETS @ NeurIPS 2020)

  11. Incorporating Expert Guidance in Epidemic Forecasting [arXiv]
    A. Rodríguez, B. Adhikari, N. Ramakrishnan, B.A. Prakash
    ACM SIGKDD 2020 Epidemiology Meets Data Mining and Knowledge Discovery Workshop (epiDAMIK @ KDD 2020)


Last update: Nov. 2022