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

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


I am a Ph.D. student 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 COVID-19 data science challenges:

Nov. 2021 news: I was named a Rising Star in Data Science by the University of Chicago Data Science Institute (DSI) [LINK].

Dec. 2021 news: Our group gave an invited workshop/tutorial for data-driven computational methods for disease forecasting by the Forecasting for Social Good Research Network to researchers and practitioners. [LINK].


  1. EINNs: Epidemiologically-Informed Neural Networks [arXiv]
    Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash


  1. 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.

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

  3. CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting [arXiv]
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B Aditya Prakash
    The Web Conference (WebConf 2022)

  4. When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [arXiv]
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B Aditya Prakash
    Neural Information Processing Systems (NeurIPS 2021)

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

  6. DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting [medRxiv]
    Alexander Rodríguez, Anika Tabassum, Jiaming Cui, Jiajia Xie, Javen Ho, Pulak Agarwal, Bijaya Adhikari, B. Aditya Prakash
    AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-21)

  7. Mapping Network States using Connectivity Queries [arXiv]
    Alexander Rodríguez, Bijaya Adhikari, Andrés González, Charles Nicholson, Anil Vullikanti, B. Aditya Prakash
    IEEE International Conference on Big Data 2020 (IEEE BigData 2020)
    Shorter version in NeurIPS 2020 Artificial Intelligence and Humanitarian and Disaster Relief (AI + HADR) Workshop

  8. NetReAct: Interactive Learning for Network Summarization [arXiv]
    Sorour E Amiri, Bijaya Adhikari, John Wenskovitch, Alexander Rodríguez, Michelle Dowling, Chris North, B Aditya Prakash
    NeurIPS 2020 Human And Machine in-the-Loop Evaluation and Learning Strategies (HAMLETS) Workshop

  9. Incorporating Expert Guidance in Epidemic Forecasting [arXiv]
    Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan, B. Aditya Prakash
    ACM SIGKDD 2020 Epidemiology Meets Data Mining and Knowledge Discovery (epiDAMIK) Workshop

Last update: Feb. 2022