Stitching Together Innovation with FABRIC Users
Date: December 17, 2024
Time: 2:00 PM – 3:00 PM ET
Join us on December 17, 2-3 PM ET, for an insightful webinar tailored for new users, offering a deep dive into the APPFL experiment on FABRIC featuring Zilinghan Li. This session will cover:
- A comprehensive overview of the APPFL experiment.
- The rationale behind selecting FABRIC as the experimental platform.
- A live demonstration showcasing how to run the experiment
- How FABRIC can enhance and support your research initiatives.
The tutorial will conclude with a 15-minute Q&A session, allowing participants to engage directly with Ravi Madduri, Zilinghan Li, and Ze Yang, who all work on the APPFL experiment.
This webinar will be recorded and available on our YouTube channel for future reference. Whether new to FABRIC or seeking to optimize your experimental processes, this session will equip you with valuable insights and practical strategies.
Register now to explore the potential of FABRIC for your research!
REGISTER HERE
Presenters
Zilinghan Li is a Machine Learning Engineer in the Data and Science Learning Division at Argonne National Laboratory. He holds a Bachelor of Science in Computer Engineering from Zhejiang University, China, and a Master of Science in Computer Science from the University of Illinois at Urbana-Champaign. He is the lead developer of the Advanced Privacy-Preserving Federated Learning framework, and he is also deeply engaged in developing AI models for biomedical and scientific applications, advancing the frontiers of data-driven discovery.
Ravi Madduri is a senior computer scientist focused on applying AI and high-performance computing (HPC) to accelerating scientific discovery process. His research interests are in building sustainable, scalable services for science, reproducible research, large-scale data management, analysis using HPC and AI. He leads the PALISADE-X project that is developing Privacy-preserving Federated Learning framework to build robust, trust-worthy AI models. He co-leads the MVP-CHAMPION project, which is a collaboration between VA and DOE and develops methods to perform large-scale genetic data analysis using DOE’s high performance computing capabilities. Additionally, Ravi was one of three key contributors to the National Institutes of Health $100M Cancer Biomedical Informatics Grid (caBIG), which linked 60 NIH-funded cancer centers and clinical sites engaged in cancer research. For his efforts in project management, tool development, and collaboration, Ravi received several Outstanding Achievement Awards from NIH. For his work on “Cancer Moonshot” project, he received the Department of Energy Secretary award in 2017.
Ze Yang is a dedicated Visiting Research Associate at Argonne National Laboratory and a graduate student at the University of Illinois at Urbana-Champaign, specializing in AI and HPC. With expertise in training large language models, federated learning, and optimization for inference, Ze has contributed to cutting-edge projects enhancing medical diagnostics and scientific research. Ze is passionate about leveraging innovative technologies to solve complex challenges and drive advancements in computational efficiency.