Przemyslaw J. Porebski

According to our database, Przemyslaw J. Porebski authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson

PLoS Comput. Biol., 2024

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, Henning S. Mortveit, Srinivasan Venkatramanan, Mandy L. Wilson, Achla Marathe, Przemyslaw J. Porebski, Brian Klahn, Joseph Outten, Anil Vullikanti, Dawen Xie, Abhijin Adiga, Shawn Brown, Christopher L. Barrett, Madhav V. Marathe

J. Parallel Distributed Comput., 2024

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, Andrew S. Warren, Young Yun Baek, Przemyslaw J. Porebski, Achla Marathe, Dawen Xie, Samarth Swarup, Anil Vullikanti, Henning S. Mortveit, Stephen G. Eubank, Christopher L. Barrett, Madhav Vinayak Marathe

Int. J. High Perform. Comput. Appl., 2023

A Multi-Team Multi-Model Collaborative Covid-19 Forecasting Hub for India.

Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Ambedkar Dukkipati, Tony Gracious, Shubham Gupta, Nihesh Rathod, Rajesh Sundaresan, Sarath Yasodharan, Kantha Rao Bhimala, Vidyadhar Mudkavi, Gopal Krishna Patra, Siva Athreya

Proceedings of the Winter Simulation Conference, 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

Proceedings of the 5th IEEE International Conference on Trust, 2023

Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts.

Aniruddha Adiga, Gursharn Kaur, Lijing Wang, Benjamin Hurt, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan L. Lewis, Madhav V. Marathe

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources.

Aniruddha Adiga, Gursharn Kaur, Benjamin Hurt, Lijing Wang, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan L. Lewis, Madhav V. Marathe

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Recognizing and validating ligands with CheckMyBlob.

Dariusz Brzezinski, Przemyslaw J. Porebski, Marcin Kowiel, Joanna M. Macnar, Wladek Minor

Nucleic Acids Res., 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

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting.

Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan Leroy Lewis, Madhav V. Marathe

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

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, Anil Vullikanti, Dawen Xie, Abhijin Adiga, Shawn Brown, Christopher L. Barrett, Madhav V. Marathe

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

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

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

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