General Chair
Joseph Gonzalez
UC Berkeley
Joseph is a Professor in the EECS department at UC Berkeley, a co-director and founding member of the UC Berkeley Sky Computing Lab, RISE Lab, and a member of the Berkeley AI Research (BAIR Group). His research interests span artificial intelligence and data systems and he has a wide range of projects including: large language models (LLMs) that interact with external systems (e.g. tool use, RAG, LLM agents, long context windows) system support for large language model deployment (e.g. inference, serving, RAG, batch) large model finetuning machine learning on edge devices new approaches to cloud computing accelerated deep learning for high-resolution computer vision software platforms for autonomous vehicles In 2020, Joseph launched a new stealth company based on his research to radically simplify the process of deploying advanced analytics and machine learning in modern data driven enterprises. Prior to joining Berkeley, Joseph co-founded Turi Inc. (formerly GraphLab), which was based on his thesis work on the GraphLab and PowerGraph Systems. Turi was eventually acquired by Apple.
Joseph is a Professor in the EECS department at UC Berkeley, a co-director and founding member of the UC Berkeley Sky Computing Lab, RISE Lab, and a member of the Berkeley AI Research (BAIR Group). His research interests span artificial intelligence and data systems and he has a wide range of projects including: large language models (LLMs) that interact with external systems (e.g. tool use, RAG, LLM agents, long context windows) system support for large language model deployment (e.g. inference, serving, RAG, batch) large model finetuning machine learning on edge devices new approaches to cloud computing accelerated deep learning for high-resolution computer vision software platforms for autonomous vehicles In 2020, Joseph launched a new stealth company based on his research to radically simplify the process of deploying advanced analytics and machine learning in modern data driven enterprises. Prior to joining Berkeley, Joseph co-founded Turi Inc. (formerly GraphLab), which was based on his thesis work on the GraphLab and PowerGraph Systems. Turi was eventually acquired by Apple.
Program Chair
Song Han
MIT / NVIDIA
Song Han is an associate professor with tenure at MIT EECS. He got his PhD from Stanford University advised by Bill Dally. Song pioneered efficient AI computing techniques including “Deep Compression” (pruning, quantization) and the “Efficient Inference Engine,” which first introduced weight sparsity to modern AI chips, making it one of the top-5 most cited papers in the 50-year history of ISCA (1953-2023). His innovations, including TinyML and hardware-aware neural architecture search (Once-for-All Network), have advanced AI model deployment on resource-constrained devices. His recent work on LLM quantization and acceleration (SmoothQuant, AWQ, StreamingLLM) has improved efficiency in LLM inference, adopted by NVIDIA TensorRT-LLM. Song received best paper awards at ICLR'16, FPGA'17, and MLSys'24, the NSF CAREER Award, “35 Innovators Under 35,” IEEE “AI’s 10 to Watch,” and the Sloan Research Fellowship. He developed the open lecture series EfficientML.ai to share advances in efficient ML research.
Song Han is an associate professor with tenure at MIT EECS. He got his PhD from Stanford University advised by Bill Dally. Song pioneered efficient AI computing techniques including “Deep Compression” (pruning, quantization) and the “Efficient Inference Engine,” which first introduced weight sparsity to modern AI chips, making it one of the top-5 most cited papers in the 50-year history of ISCA (1953-2023). His innovations, including TinyML and hardware-aware neural architecture search (Once-for-All Network), have advanced AI model deployment on resource-constrained devices. His recent work on LLM quantization and acceleration (SmoothQuant, AWQ, StreamingLLM) has improved efficiency in LLM inference, adopted by NVIDIA TensorRT-LLM. Song received best paper awards at ICLR'16, FPGA'17, and MLSys'24, the NSF CAREER Award, “35 Innovators Under 35,” IEEE “AI’s 10 to Watch,” and the Sloan Research Fellowship. He developed the open lecture series EfficientML.ai to share advances in efficient ML research.
Program Chair
Rashmi Vinayak
Carnegie Mellon University / Google
Rashmi Vinayak is an associate professor in the Computer Science department at Carnegie Mellon University. She received her Ph.D. from UC Berkeley in 2016, and was a postdoctoral scholar at UC Berkeley from 2016-17. Her research interests broadly lie in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas. Rashmi has received several awards for her interdisciplinary research including the Sloan Faculty Fellowship, IEEE ITSoc Goldsmith Lecturer award, VMware Systems Research Award, NSF CAREER Award, TIFR Memorial Lecture Award, UC Berkeley Eli Jury Dissertation Award, two USENIX NSDI Community (Best Paper) Awards, VLDB Best Paper Honorable Mention, multiple research awards from Meta and Google, a IEEE Data Storage Best Paper, and a IEEE Data Storage Best Student Paper award. Her research has been adopted by industry, including at VMware, Google, Meta, Twitter, Cloudflare, Microsoft, and numerous open source libraries, and has been featured on popular media platforms including HackerNews. During her Ph.D. studies, Rashmi was a recipient of the Facebook Fellowship, the Microsoft Research PhD Fellowship, and the Google Anita Borg Memorial Scholarship.
Rashmi Vinayak is an associate professor in the Computer Science department at Carnegie Mellon University. She received her Ph.D. from UC Berkeley in 2016, and was a postdoctoral scholar at UC Berkeley from 2016-17. Her research interests broadly lie in computer/networked systems and information/coding theory, and the wide spectrum of intersection between the two areas. Rashmi has received several awards for her interdisciplinary research including the Sloan Faculty Fellowship, IEEE ITSoc Goldsmith Lecturer award, VMware Systems Research Award, NSF CAREER Award, TIFR Memorial Lecture Award, UC Berkeley Eli Jury Dissertation Award, two USENIX NSDI Community (Best Paper) Awards, VLDB Best Paper Honorable Mention, multiple research awards from Meta and Google, a IEEE Data Storage Best Paper, and a IEEE Data Storage Best Student Paper award. Her research has been adopted by industry, including at VMware, Google, Meta, Twitter, Cloudflare, Microsoft, and numerous open source libraries, and has been featured on popular media platforms including HackerNews. During her Ph.D. studies, Rashmi was a recipient of the Facebook Fellowship, the Microsoft Research PhD Fellowship, and the Google Anita Borg Memorial Scholarship.
Industry Track Chair
Bilge Acun
Meta / FAIR
Bilge Acun is a Research Scientist at Meta’s Fundamental AI Research (FAIR), where she works on efficient, high-performance, and sustainable machine learning systems. She received her PhD in Computer Science from the University of Illinois Urbana-Champaign and previously worked at IBM Research.
Bilge Acun is a Research Scientist at Meta’s Fundamental AI Research (FAIR), where she works on efficient, high-performance, and sustainable machine learning systems. She received her PhD in Computer Science from the University of Illinois Urbana-Champaign and previously worked at IBM Research.
Industry Track Chair
Huizi Mao
OpenAI
Huizi Mao is an AI systems researcher and engineer at OpenAI focused on efficient model training and inference. He previously co-founded OmniML, acquired by NVIDIA in 2023, and led work on NVIDIA’s TensorRT Model Optimizer; he received his PhD in Electrical Engineering from Stanford University.
Huizi Mao is an AI systems researcher and engineer at OpenAI focused on efficient model training and inference. He previously co-founded OmniML, acquired by NVIDIA in 2023, and led work on NVIDIA’s TensorRT Model Optimizer; he received his PhD in Electrical Engineering from Stanford University.
Industry Track Chair
Yawen Wang
Google
Yawen Wang is a researcher and software engineer on Google’s Systems Research Group, where she applies machine learning to improve infrastructure efficiency and application performance. She received her PhD from Stanford University, where her research focused on using online learning to improve cloud systems.
Yawen Wang is a researcher and software engineer on Google’s Systems Research Group, where she applies machine learning to improve infrastructure efficiency and application performance. She received her PhD from Stanford University, where her research focused on using online learning to improve cloud systems.
Sponsor Chair
Wenming Ye
Model AI Corp
Wenming Ye works on product and AI systems at Google, with a focus on GenAI and JAX frameworks. He has been an active organizer and sponsor chair for major machine learning and systems conferences.
Wenming Ye works on product and AI systems at Google, with a focus on GenAI and JAX frameworks. He has been an active organizer and sponsor chair for major machine learning and systems conferences.
Workflow Chair
Max Wiesner
MLSys
Max Wiesner is a data / AI infrastructure engineer who builds data systems, AI products, and the infrastructure behind them. He’s also been involved in organizing major ML conferences like NeurIPS, ICML, ICLR, AISTATS, and MLSys for years, working across both the technical and operational sides of running them.
Max Wiesner is a data / AI infrastructure engineer who builds data systems, AI products, and the infrastructure behind them. He’s also been involved in organizing major ML conferences like NeurIPS, ICML, ICLR, AISTATS, and MLSys for years, working across both the technical and operational sides of running them.
Logistics Chair
Mary Ellen Perry
Admin.
MLSys Staff
Mary Ellen Perry is a longtime academic conference organizer and administrator who has helped manage major machine learning conferences including NeurIPS and ICML. She served as Executive Director of the NeurIPS Foundation while affiliated with the Salk Institute, and has also been listed in ICML conference operations and contact roles. Her work has supported the logistics, administration, and execution of some of the world’s leading machine learning research conferences
Mary Ellen Perry is a longtime academic conference organizer and administrator who has helped manage major machine learning conferences including NeurIPS and ICML. She served as Executive Director of the NeurIPS Foundation while affiliated with the Salk Institute, and has also been listed in ICML conference operations and contact roles. Her work has supported the logistics, administration, and execution of some of the world’s leading machine learning research conferences
Logistics Chair
Susan Perry
MLSys Staff
Susan Perry is a longtime member of the machine learning conference community and currently serves as a Logistics Chair, helping coordinate production, venue operations, and on-site logistics for major conferences including NeurIPS and ICML. She has been involved with these events for years, supporting the behind-the-scenes work that keeps large international conferences running smoothly.
Susan Perry is a longtime member of the machine learning conference community and currently serves as a Logistics Chair, helping coordinate production, venue operations, and on-site logistics for major conferences including NeurIPS and ICML. She has been involved with these events for years, supporting the behind-the-scenes work that keeps large international conferences running smoothly.
