Bio

I am starting as an Assistant Professor at Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering (affiliate) at UC San Diego in Fall 2023.

I study large-scale distributed ML in the joint context of ML and systems, concerning performance, usability, cost, and privacy. My work spans across distributed ML algorithms, large models, parallelisms, performance optimizations, system architectures, ML privacy, and AutoML, with applications in computer vision, natural language processing, and healthcare.

I am currently a postdoctoral researcher at the Sky Lab, UC Berkeley, working with Prof. Ion Stoica. I am recently working on the Alpa project and the Sky project, aiming at democratizing large models like GPT-3.

I completed my Ph.D. at the School of Computer Science, Carnegie Mellon University. My advisor was Prof. Eric Xing. My Ph.D. thesis is Machine Learning Parallelism Could Be Adaptive, Composable and Automated. During my Ph.D. I spent the majority of my time working for the ML startup company Petuum. During my postdoc I work closely with another ML startup company AnyScale. Several of my works including Poseidon, Cavs, GeePS, and Alpa are parts of the CASL project and the Ray project.

Prospective students and postdocs:

I am recruiting self-motivated PhD students, postdocs, and research interns who are interested in machine learning, systems, NLP, security, and the intersection areas!
👉 Please fill out this form and feel free to drop me an email if you are intereted in joining!
👉 If you are applying to PhD programs in HDSI/CSE/ECE at UCSD, or are currently a UCSD student interested in working with me, see details here about how to get in touch!

News

• 🌟 Check out the alpa.llm_serving package which support serving almost all open-source LLMs.
• 🌟 We recently launched an open sourced version of GPT-3 text prompting service. Try it and share your results!
• I gave a tutorial at ICML 2022. Big Model Tutorial: Techniques and Systems to Train and Serve Bigger Models.

Publications

[ Selected / ML systems / Scalable ML models&algorithms / AutoML / Large ML applications / All by date ]

Software

• Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
• AutoDist: A Composable and Automated Synchronization System for Distributed Deep Learning
• AdaptDL: Resource-adaptive Cluster Scheduler for Deep Learning Training
• Cavs: An Efficient Programming Interface for Dynamic Neural Networks
• Poseidon v2.0: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
• DyNet: The Dynamic Neural Network Toolkit
• GeePS: a GPU-specialized Parameter Server
• Poseidon v1.0: Distributed Deep Learning on CPU/GPU Clusters

Teaching

• TA of 10-708 Probabilistic Graphical Models, Spring 2019, Carnegie Mellon University
• TA of 16-791 Applied Data Science, Spring 2019, Carnegie Mellon University
• TA of 10-701 Introduction to Machine Learning, Fall 2015, Carnegie Mellon University.
• TA of Artificial Intelligence, Spring 2012, Shanghai Jiao Tong University.