ETH Zurich
Graphs in the LLM Era: Enabling Effective and Efficient LLM Ecosystems
Maciej Besta leads research on large language models and graph computations at the Scalable Parallel Computing Lab at ETH Zurich; he also works on network topologies and occasionally other aspects of the high-performance computing landscape. Maciej published more than 50 top conference and journal papers. He won, among others, the competition for the Best Student of Poland (2012), the first Google Fellowship in Parallel Computing (2013), the ACM/IEEE-CS High-Performance Computing Fellowship (2015), the IEEE TCSC Award for Excellence in Scalable Computing Early Career (2023), the IEEE TCHPC Award for Excellence in High-Performance Computing Early Career (2024), and the OlympusMons Award for contributions to scalable storage systems (2024). His doctoral dissertation on irregular computations received the ETH Medal for outstanding doctoral thesis (2021), and awards from IEEE (2021), SPEC (2022), and ACM (2022) for a distinguished dissertation worldwide in - respectively - scalable computing, performance analysis, and high-performance computing. He received Best Paper awards and nominations at ACM/IEEE Supercomputing 2013, 2014, 2019 (2 papers), 2022, and 2023 (2 papers); at ACM HPDC 2015 and 2016, ACM Research Highlights 2018, and others. More detailed information on https://people.inf.ethz.ch/bestam/
University of Texas, Arlington
Advancing Graph AI: Tackling Efficiency, Application, and Explainability
Dr. Yuede (YJ) Ji is an Assistant Professor from the Department of Computer Science and Engineering at the University of Texas at Arlington. He directs the Graph Lab, which focuses on graph-centric security, learning, and computing. He works at the intersections of High-Performance Computing (HPC), Security, Graph AI, and Graph Analytics. The goal of his research is to build effective and scalable system solutions to protect the security and privacy of critical software and HPC infrastructures. His research work has frequently appeared at prestigious security and HPC venues, including SC, HPDC, USENIX Security, and ISSTA. His research has won the best paper award at NPC 2014, and the best poster award at SMCDC 2023. His research has been supported by multiple grants from National Science Foundation (NSF), Department of Energy (DOE), and Google
PNNL
Rethinking Graph Analytics Benchmarks with the AGILE Workflows
Marco Minutoli is a research scientist in the Data Science and Machine Intelligence group at Pacific Northwest National Laboratory (PNNL). His research focuses on the design of parallel graph algorithms for combinatorial scientific computing and on the definition of hardware/software co-design and high-level synthesis methodologies and their compilation and optimization pipelines for the generation of custom computing devices optimized for irregular applications.