Researcher Spotlight
Diego Kleiman
Diego E. Kleiman is a Ph.D. candidate in Biophysics and Quantitative Biology at the University of Illinois Urbana-Champaign, where he works in the laboratory of Professor Diwakar Shukla. His research focuses on the development of machine learning methods for modeling protein conformational dynamics and accelerating molecular dynamics simulations. Before joining the Shukla Group, he earned a B.S. in Physics from New York University Abu Dhabi, where he worked with Professor Serdal Kirmizialtin on RNA ion-mediated interactions. In 2023, Kleiman was a recipient of the Seed Software Fellowship from the NSF Molecular Sciences Software Institute.
What do you see as the primary benefit to you of archiving your data in a repository?
The primary benefit for me is that archiving data in the Illinois Data Bank makes it much easier to disseminate my results and datasets in a reliable and scalable way. In my work, this is especially important because I often need to distribute large machine learning model weights and support a sizeable volume of requests for instant access through web-based applications. It also allows me to meet publisher requirements for DOI-compliant data repositories at all stages of peer review and publication, which simplifies the submission process and improves accessibility of the work.