PNNL

Colby Ham is a computer scientist at Pacific Northwest National Laboratory (PNNL) with more than six years of experience in machine learning and data analytics. His primary research interest focuses on building deployable systems that incorporate domain knowledge to enable predictive modeling and model explainability. His recent work focuses on building intelligent medical decision support systems that ingest and integrate different modalities of electronic healthcare records (EHR) into a unified framework for prediction and reasoning in the context of diseases.

• Artificial Intelligence in healthcare
• Cloud computing
• Performance benchmarking
• Deep learning
• Big data

• BS in computer science, Oregon State University, 2017

• Outstanding Performance Award, PNNL, 2019, 2020, 2021, 2022

2022

• Agarwal K., S. Choudhury, S. Tipirneni, P. Mukherjee, C.M. Ham, S. Tamang, and M. Baker, et al. 2022. "Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction." Scientific Reports 12. PNNL-SA-166555. doi:10.21203/rs.3.rs-965815/v1

2021

• Wang P., K. Agarwal, C. Ham, S. Choudhury, C.K. Reddy. 2021. “Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks.” Proceedings of The Web Conference, 2946-2957. arXiv preprint arXiv.2007.11192