Automatic recognition of ligands in electron density by machine learning. - Marcin Kowiel; Dariusz Brzezinski; Przemyslaw J. Porebski; Ivan G. Shabalin; Mariusz Jaskolski; Wladek Minor (Bioinform., 2019)

From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance. - Akhil Sai Peddireddy; Dawen Xie; Pramod Patil; Mandy L. Wilson; Dustin Machi; Srinivasan Venkatramanan; Brian Klahn; Przemyslaw J. Porebski; Parantapa Bhattacharya; Shirish Dumbre; Erin Raymond; Madhav V. Marathe (IEEE BigData, 2020)

AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US. - Parantapa Bhattacharya; Dustin Machi; Jiangzhuo Chen; Stefan Hoops; Bryan L. Lewis; Henning S. Mortveit; Srinivasan Venkatramanan; Mandy L. Wilson; Achla Marathe; Przemyslaw J. Porebski; Brian Klahn; Joseph Outten; et al. (IEEE BigData, 2021)

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. - Aniruddha Adiga; Lijing Wang 001; Benjamin Hurt; Akhil Sai Peddireddy; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan Leroy Lewis; Madhav V. Marathe (KDD, 2021)

Detecting anomalies in X-ray diffraction images using convolutional neural networks. - Adam Czyzewski; Faustyna Krawiec; Dariusz Brzezinski; Przemyslaw J. Porebski; Wladek Minor (Expert Syst. Appl., 2021)

Recognizing and validating ligands with CheckMyBlob. - Dariusz Brzezinski; Przemyslaw J. Porebski; Marcin Kowiel; Joanna M. Macnar; Wladek Minor (Nucleic Acids Res., 2021)

Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources. - Aniruddha Adiga; Gursharn Kaur; Benjamin Hurt; Lijing Wang 001; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan L. Lewis; Madhav V. Marathe (IEEE Big Data, 2022)

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts. - Aniruddha Adiga; Gursharn Kaur; Lijing Wang 001; Benjamin Hurt; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Bryan L. Lewis; Madhav V. Marathe (AAAI, 2023)

Synthetic Information and Digital Twins for Pandemic Science: Challenges and Opportunities. - Galen Harrison; Przemyslaw J. Porebski; Jiangzhuo Chen; Mandy L. Wilson; Henning S. Mortveit; Parantapa Bhattacharya; Dawen Xie; Stefan Hoops; Anil Vullikanti; Li Xiong; James Joshi; Madhav V. Marathe (TPS-ISA, 2023)

A Multi-Team Multi-Model Collaborative Covid-19 Forecasting Hub for India. - Aniruddha Adiga; Benjamin Hurt; Gursharn Kaur; Bryan Lewis 001; Madhav V. Marathe; Przemyslaw J. Porebski; Srinivasan Venkatramanan; Ambedkar Dukkipati; Tony Gracious; Shubham Gupta; Nihesh Rathod; Rajesh Sundaresan; et al. (WSC, 2023)

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support. - Parantapa Bhattacharya; Jiangzhuo Chen; Stefan Hoops; Dustin Machi; Bryan L. Lewis; Srinivasan Venkatramanan; Mandy L. Wilson; Brian Klahn; Aniruddha Adiga; Benjamin Hurt; Joseph Outten; Abhijin Adiga; et al. (Int. J. High Perform. Comput. Appl., 2023)

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread. - Parantapa Bhattacharya; Dustin Machi; Jiangzhuo Chen; Stefan Hoops; Bryan Lewis 001; Henning S. Mortveit; Srinivasan Venkatramanan; Mandy L. Wilson; Achla Marathe; Przemyslaw J. Porebski; Brian Klahn; Joseph Outten; et al. (J. Parallel Distributed Comput., 2024)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)