Minjia Zhang

Minjia Zhang is a tenure-track Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering (ECE) and the Center for Artificial Intelligence Innovation (CAII) at the National Center for Supercomputing Applications (NCSA). He leads the Supercomputing System and AI Lab (SSAIL) and previously served as a Principal Researcher at Microsoft Research Redmond from 2016 to 2023, contributing to projects like DeepSpeed and DeepCPU.

Education: Ph.D. in Computer Science and Engineering from Ohio State University (2016), advised by Prof. Michael D. Bond. Prior to UIUC, he held visiting positions at Microsoft Research during his doctoral studies. His research focuses on efficient machine learning systems, including training/inference optimization on parallel/distributed/heterogeneous hardware, model compression, and large-scale AI applications such as agentic AI and vision-language models.

Research Interests: Large-scale DL/AI applications (e.g., agentic AI, multimodal systems), effective efficiency algorithms (model compression, data efficiency), and high-performance ML systems. He emphasizes cross-disciplinary collaboration, integrating system design with AI advancements to tackle scalability challenges in scientific discovery and industrial deployment.

He has been recognized with the NSF CAREER Award (2024), Honorable Mention of the ICLR 2024 Outstanding Paper Award, and OOPSLA 2015 Distinguished Paper and Artifact Awards. His work has been applied to Microsoft products like Bing and Azure, enhancing latency and cost efficiency.

Advising and Grants: Actively mentoring students in PhD/Master's/Undergraduate programs, emphasizing systems and AI. Recipient of NSF grants and the Amazon PhD Fellowship (Xinyu Lian). His lab's VecFlow and MiniKV projects highlight contributions to vector databases and efficient LLM inference.

Labs/Teams: SSAIL Lab, collaboration with Microsoft Research (DeepSpeed), BigScience consortium (BLOOM 176B model), and NCSA's CAII. His work bridges systems research and AI, targeting high-impact applications in science and industry.