Hi, I'm Tian

About Me

I am Tian Li (李恬 in Chinese), a third-year PhD student in Computer Science Department at Carnegie Mellon University working with Virginia Smith. My reesearch interests are in distributed optimization, federated learning, and data-intensive systems. Prior to CMU, I received my B.S. in Computer Science and B.A. in Economics from Peking University in 2018.

Research

All
Selected
Manuscripts
Conference Publications
Workshop Papers
Journal Articles

Also see:


Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
Best Paper Award at ICLR 2021 Secure ML Workshop
[Arxiv] [Code]
Heterogeneity for the Win: One-Shot Federated Clustering
Don Kurian Dennis, Tian Li, Virginia Smith
[Arxiv]
Tilted Empirical Risk Minimization
Tian Li*, Ahmad Beirami*, Maziar Sanjabi, Virginia Smith
ICLR 2021
[PDF] [Arxiv] [Code] [Blog post] [Slides] [Poster] [Video]
Ease.ML: A Lifecycle Management System for MLDev and MLOps
..., 20 authors, ..., Tian Li, ..., Wentao Wu, Ce Zhang
CIDR 2021
[PDF] [Ce's talk]
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
MLSys 2020
[PDF] [Arxiv] [Code] [Slides] [Poster] [Video]
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
ICLR 2020
[PDF] [Arxiv] [Code] [Slides] [Video]
Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
IEEE Signal Processing Magazine, Special Issue on Distributed, Streaming Machine Learning, 2020
[PDF] [Arxiv][Blog post]
Learning Context-aware Policies from Multiple Smart Homes via Federated Multi-Task Learning
Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
IoTDI 2020
[PDF]
FedDANE: A Federated Newton-Type Method
Tian Li, Anit Kumar Sahu, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
Asilomar Conference on Signals, Systems and Computers 2019 (Invited Paper)
[PDF] [Arxiv] [Code]
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konecny, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
Federated Learning Workshop @ NeurIPS 2019
[Website] [Arxiv]

Ease.ml: Towards Multitenant Resource Sharing for Machine Learning Workloads
Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang
VLDB 2018
[PDF] [Arxiv]
CUTE: Query Knowledge Graphs by Tabular Examples
Zichen Wang*, Tian Li*, Yingxia Shao, Bin Cui
WAIM 2018 (demo)
[PDF] [Poster]
An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision
Ce Zhang, Wentao Wu, Tian Li
HILDA Workshop @ SIGMOD 2017
[PDF]

Contact Me

tianli@cmu.edu

GHC 6507, 5000 Forbes Avenue
Pittsburgh, PA 15213