I am an NSF Computing Innovation Fellow and Postdoctoral Associate at MIT CSAIL mentored by Michael Carbin. Previously, I received my Ph.D. in Computer Science from The University of Chicago advised by Henry Hoffmann.
My research focuses on improving performance (e.g., latency, energy, and adaptivity) of computer systems by integrating geometric structure (i.e, shape, position, and dimension of objects in the design space) of the system problems with advanced machine learning techniques. My approaches demonstrate improved generalization (i.e., achieving accurate predictions for unseen data) and computational efficiency (i.e., delivering good results with reduced time and memory usage) of novel machine learning models when they are applied to computer systems.
2021-08-17: Won Meta Research Award on Statistics for Improving Insights, Models, and Decisions.
2021-01-04: Start my postdoc at PSG, MIT CSAIL!
2020-11-09: Attended EECS Rising Stars Workshop at UC Berkeley, virtually.
2020-11-03: Successfully defended my Ph.D. thesis!
2020-07-21: Selected as a 2020 Computing Innovation Fellow by CRA/CCC!
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization
Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding* (corresponding author)
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Yi Ding, Alex Renda, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
CAFQA: A Classical Simulation Bootstrap for Variational Quantum Algorithms
Gokul Subramanian Ravi, Pranav Gokhale, Yi Ding, William Kirby, Kaitlin Smith, Peter Love, Kenneth Brown, Henry Hoffmann, Frederic Chong
Poster presented in the 25th Annual Conference on Quantum Information Processing (QIP 2022)
To appear in the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2023)
Minimax Designs for Causal Effects in Temporal Experiments with Treatment Habituation
Guillaume Basse, Yi Ding, Panos Toulis
Presented at the 6th Conference on Digital Experimentation, CODE@MIT, 2019
In Biometrika, 2022 (a top theoretical statistics journal)
NURD : Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willet, Henry Hoffmann
In the 5th Conference on Machine Learning and Systems (MLSys 2022)
Programming with Neural Surrogates of Programs
Alex Renda, Yi Ding, Michael Carbin
In the ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward! 2021)
Generalizable and Interpretable Learning for Configuration Extrapolation
Yi Ding, Ahsan Pervaiz, Michael Carbin, Henry Hoffmann
In the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021)
Neighborhood Street Activity and Greenspace Usage Uniquely Contribute to Predicting Crime
Kathryn E Schertz, James Saxon, Carlos Cardenas-Iniguez, Luís MA Bettencourt, Yi Ding, Henry Hoffmann, Marc G Berman
In npj Urban Sustainability, Nature Research Journal (2021)
A Polynomial-time Algorithm for Learning Nonparametric Causal Graphs
Ming Gao, Yi Ding, Bryon Aragam
In the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods
Yi Ding, Panos Toulis
In the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
Generative and Multi–phase Learning for Computer Systems Optimization
Yi Ding, Nikita Mishra, Henry Hoffmann
In the 46th International Symposium on Computer Architecture (ISCA 2019)
Multiresolution Kernel Approximation for Gaussian Process Regression
Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler
In the 31st Conference on Neural Information Processing Systems (NeurIPS 2017) (Spotlight)
Large Scale Kernel Methods for Online AUC Maximization
Yi Ding, Chenghao Liu, Peilin Zhao, Steven CH Hoi
In the IEEE International Conference on Data Mining (ICDM 2017) (Long Oral)
An Adaptive Gradient Method for Online AUC Maximization
Yi Ding, Peilin Zhao, Steven CH Hoi, Yew-Soon Ong
In the 29th AAAI Conference on Artificial Intelligence (AAAI 2015) (Oral)
Learning Relative Similarity by Stochastic Dual Coordinate Ascent
Pengcheng Wu, Yi Ding, Peilin Zhao, Chunyan Miao, Steven CH Hoi
In the 28th AAAI Conference on Artificial Intelligence (AAAI 2014)