A Constellation of Ideas in a Sky of Innovation: Insights from the 2024 A3D3 All-Hands Meeting
By: Cymberly Tsai
December 19, 2024
About the author: Cymberly Tsai is a high school senior at Council Rock High School North working with Prof. Joshua Agar at Drexel University. Cymberly presented a poster titled “Foundation Model for Real-Time Model Selection and Fitting” at the A3D3 All-Hands Meeting at Purdue University.
The A3D3 Institute hosted its annual All-Hands Meeting at Purdue University on October 14, 2024. The conference was a constellation of ideas as participants presented bright projects, which contributed to a sky of innovation, and guided others at various stages of education. As a student at Council Rock High School North, I had the opportunity to attend this conference with the Drexel Multifunctional Materials and Machine Learning lab with professor Josh Agar, after working with the lab the past summer. Taking place at the intersection of artificial intelligence and scientific research, the conference included attendees from a vast range of universities and locations, offering both in-person and virtual participation.
The conference emphasized accelerating machine learning for applications in a variety of fields. Located at Purdue’s Convergence Center, the meeting began with talks from several principal investigators, including Dr. Song Han‘s talk introducing efficient multi-modal large language model (LLM) innovations and Dr. Pan Li’s work on Graph Machine Learning under distribution shifts. These in-depth presentations offered insight into specific innovations, inspiring further research from attendees. Highlighting many varied applications of machine learning, trainees presented overviews of fields with high potential for fast machine learning, such as multi-messenger astronomy, neuroscience, high-energy physics, and hardware/algorithm co-development. The trainee-led talks covered such cutting-edge topics as the challenges of processing massive data rates in high-energy physics research and real-time brain modeling in primates with machine learning accelerated by Field-Programmable Gate Arrays (FPGAs). As one of the five National Science Foundation’s (NSF) Harnessing the Data Revolution (HDR) institutes, A3D3 also announced the NSF HDR ML Anomaly Detection Challenge at the conference, which leverages datasets in multiple fields to encourage data-driven discovery.
The All-Hands Meeting was particularly beneficial for those early in their research careers, welcoming them into the academic community. The conference offered a poster session, providing a chance to engage with ideas in a low-stress setting. Beyond research-specific content, the conference included a panel discussion on how to apply to faculty, postdoc, and industry jobs, led by Dr. Zhijian Liu, an NVIDIA research scientist and incoming assistant professor at UCSD, Dr. Julia Gonski, a Panofsky Fellow at SLAC/Stanford, and Dr. Dylan Rankin, an assistant professor at the University of Pennsylvania. Postbaccalaureate fellows of A3D3 also described their experience, giving insight into another avenue of research. A highlight of the All-Hands Meeting was the trainee-led townhall, which gave trainees and early-career researchers a chance to discuss ideas such as potential presentation opportunities and future collaborations.
The A3D3 conference experience expanded my understanding of machine learning and the world of research, sparking a greater curiosity and motivation to pursue scientific advancements with artificial intelligence. Following the All-Hands Meeting, the Fast Machine Learning for Science Conference conveniently took place at Purdue from October 15 through 18, allowing A3D3 attendees to dive deeper into machine learning advancements in research. Attending the conference was an invaluable experience for me to engage in the world of research and witness innovations in machine learning that I hope to contribute to in the future.