AI for Cyberinfrastructure Workshop
Date: Friday, January 28
Time: 11:30 a.m.–5:00 p.m.
Location: Online
As research cyberinfrastructure grows rapidly in diversity, scale, and complexity, hardware resource management and application execution optimization are becoming increasingly challenging. The goals of this workshop are to bring together stakeholders to:
- identify problems with current approaches to infrastructure management and application optimization,
- identify what role AI/ML can potentially play in addressing these problems
- discuss potential opportunities for teaming
Who should attend?
This workshop is open by invitation only to Penn State faculty and stakeholders. If you are interested in attending, please contact Mahmut Taylan Kandemir, Distinguished Professor of Computer Science and Engineering, at mtk2@psu.edu.
Agenda (all times are in EST):
11:30 Welcome & Introductions
11:35 Keynote Talk
12:15 Break
1:00 Presentations
3:30 Instructions and move to breakout rooms
3:40 Parallel Breakout Sessions (2 sessions)
4:30 Summary of Breakout Sessions and Closing
5:00 Workshop Concludes
Keynote:
Manish Parashar, Director & Chair in Computational Science and Engineering, Scientific Computing and Imaging Institute, The University of Utah
Title: Advancing Science at Speed and Scale: Innovation, Translation & Advanced Cyberinfrastructure
Abstract: Twenty-first century science and engineering (S&E) is being transformed by the increasing availability and scales of computation and data and the national cyberinfrastructure (CI) ecosystem has become a key catalyst for discovery and innovation. As the US National Science Foundation (NSF) moves on this vision for advancing science at speed and scale, an agile, integrated, robust, trustworthy and sustainable CI ecosystem that can drive new thinking and enable transformative discoveries is essential. In this talk, I will present NSF’s strategy for realizing such a CI ecosystem and associated challenges and opportunities.
Biography: Manish is Office Director of the Office of Advanced Cyberinfrastructure at NSF. He joins NSF as an IPA from the University of Utah where he is the Director of the Scientific Computing and Imaging (SCI) Institute, Chair in Computational Science and Engineering, and Professor in the School of Computing. His research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Manish is Fellow of AAAS, ACM, and IEEE/IEEE Computer Society. For more information, please visit http://manishparashar.org/
Presenters:
| Presenter | Title |
| Ting He, Associate Professor of Electrical Engineering and Computer Science | Distributed Machine Learning from a Communication Perspective |
| Keith Cheng, Distinguished Professor of Pathology and Laboratory Medicine and ICDS Co-Hire | The Role of Machine Learning in Defining the Geometry of Life |
| Asok Ray, Distinguished Professor of Mechanical Engineering and Mathematics | Data-driven Learning via Probabilistic Finite State Automata : An Overview |
| Margaret Hu, Associate Dean for non-juris-doctor programs, Professor of Law and International Affairs, and ICDS co-hire | Biometric AI and AI Bill of Rights |
| Jian Wang, Post Doc in the Department of Biomedical Engineering | Machine learning in drug discovery and development |
| Bhuvan Urgaonkar, Professor of Electrical Engineering and Computer Science | Cost-efficient operation in the public cloud |
| Eric Ford, Professor and ICDS Co-Hire, Department of Astronomy and Astrophysics and ICDS Co-Hire | Incorporating Science-Guided AI/ML into Astronomical Survey Pipelines: Searching for Earth-mass Exoplanets with NEID |
| Adri van Duin, Professor of Mechanical Engineering and Chemical Engineering, Director of the Materials Computation Center | Neural-network based parameter optimization for ReaxFF reactive force field parameters, enabling molecular dynamics materials on realistic materials and their interfaces |
| Nilanjan Ray Chaudhuri, Associate Professor of Electrical Engineering and Computer Science | WAMS-based Mode Meters with Guarantees on Data Recovery Under Corruption |