Topic: Artificial Intelligence / Machine Learning

About the Seminar

Artificial intelligence and machine learning have the potential to greatly transform healthcare. Although these techniques have shown remarkable performance for many tasks including medical image analysis, we will share some of the challenges that we have faced in developing robust and trustworthy algorithms including a lack of repeatability, explainability, generalizability, and the potential for bias. Access to large, representative, diverse, and well curated datasets is vital to improving the performance of machine learning algorithms. Historically, concerns related to patient privacy, regulations, cost and logistical challenges have limited data-sharing. Approaches such as federated learning can improve the robustness of algorithms by providing a framework where the trained models have been exposed to multi-institutional datasets without the need for data-sharing. We will review examples of privacy preserving learning from multi-institutional datasets and discuss successes as well as directions for future research.

About the Speaker

Jayashree Kalpathy-Cramer is currently an Associate Professor of Radiology at Harvard Medical School, and a Co-Director of the QTIM lab and the Center for Machine Learning at the Martinos Center. She is the incoming chief of the new Division of Artificial Medical Intelligence in Ophthalmology at the University of Colorado (CU) School of Medicine. An electrical engineer by training, she worked in the semiconductor industry for several years. After returning to academia, she is now focused on the applications of machine learning and modeling in healthcare. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology, and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential that these techniques have to improve access to healthcare in the US and worldwide. Dr. Kalpathy-Cramer has authored over 200 peer-reviewed publications and has written over a dozen book chapters.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Rachel Pisarski(link sends e-mail) at 301-670-4990. Requests should be made at least five days in advance of the event.

During the May Accelerating Therapeutics for Opportunities in Medicine (ATOM) Webinar Series, discover how computing and machine learning can accelerate molecular optimization for cancer and infectious disease therapeutics.

The ATOM Consortium is a public-private partnership whose mission is to transform drug discovery by accelerating the development of more effective therapies for patients.

This webinar will demonstrate how ATOM strives to:

• speed up molecular optimization for applications ranging from cancer to infectious disease therapeutics with the help of computing and machine learning,
• establish multiparameter property optimization across efficacy, safety, pharmacokinetics, and developability, and,
• develop systems with the potential to guide and optimize experimental data collection and design validation.

Presenter:

Mr. Jim Brase is the deputy associate director for computing at Lawrence Livermore National Laboratory (LLNL). He leads LLNL research in the application of high-performance computing, large-scale data science, and simulation to a broad range of national security and science missions. Mr. Brase is also co-lead of the ATOM Consortium for computational acceleration of drug discovery and on the leadership team of the COVID-19 HPC Consortium. His research interests focus on the intersection of machine learning, simulation, and high-performance computing. He is currently leading efforts on large-scale computing for life science, biosecurity, and nuclear security applications.