 |
Yi Ding |
I am an Assistant Professor of the Elmore Family School of Electrical and Computer Engineering at Purdue University, where I lead the Learning Systems Group. Previously, I was an NSF Computing Innovation Fellow and Postdoctoral Associate at MIT CSAIL mentored by Michael Carbin. During my postdoc, I also held an visiting position at Meta Infra Data Center to improve maintenance efficiency of millions of servers in real-world hyperscale datacenters. I received my Ph.D. in Computer Science from The University of Chicago advised by Henry Hoffmann.
My research interests are in the broad areas of computer systems, computer architecture, and machine learning, with a recent focus on sustainable cloud computing, and causal inference for distributed systems.
What is New?
PhD hiring (2024 - 2025): I am looking for self-motivated Ph.D students who are devoted to research excellence or high-quality system building in energy-efficient datacenters, sustainable cloud computing, and learning-based techniques for computer systems optimization. If you are interested in working with me, please apply for Purdue ECE PhD program and mention my name in your application.
To students at Purdue: There are research opportunities on the intersection between computer systems and machine learning in my group, through independent studies (undergrad, MS and PhD) and thesis options (MS and PhD). If you are interested, I would recommend you to have a quick look at my active research and recent publications first before contacting me. To match the email filter, please include the magic word xyzzyx257 somewhere in your email body (not title). Otherwise, your email will not be read.
2023-08-14: Joined Purdue ECE as an Assistant Professor.
2022-08-16: Gave a talk at Meta Infrastructure Data Science Faculty Workshop at KDD 2022, DC.
2022-06-16: CAFQA accepted to ASPLOS 2023. I introduced Bayesian optimization to search the Clifford ansatz efficiently for quantum accuracy.
2022-04-08: Minimax designs for long-term causal effects accepted to Biometrika, a top theoretical statistics journal.
2022-01-14: NURD for online datacenter straggler prediction accepted to MLSys 2022.
2021-08-17: Won Meta Research Award on Statistics for Improving Insights, Models, and Decisions.
2021-07-29: Programming with Neural Surrogates of Programs accepted to Onward! 2021.
2021-05-20: GIL for generalizable and interpretable configuration extrapolation accepted to ESEC/FSE 2021.
2021-03-18: CIFellows Project Spotlight on CCC Blog and CRA Bulletin!
2021-01-04: Start my postdoc at PSG, MIT CSAIL!
2020-11-09: Attended EECS Rising Stars Workshop at UC Berkeley.
2020-11-03: Successfully defended my Ph.D. thesis!
2020-07-21: Selected as a 2020 Computing Innovation Fellow by CRA/CCC!
Current Teaching
ECE 20875 - Python for Data Science, Fall 2023