Research Overview

Roughly speaking, I am interested in designing efficient machine learning (ML) algorithms and systems. I am particularly interested in 1) characterizing the fundamental limit of the amount and quality of data required for reliable learning and designing computationally efficient algorithms, 2) developing solutions to the ‘learning with scarce data’ problem, and 3) developing trustworthy and scalable ML systems. Casually speaking, my research goal is to answer the following three questions:

I also develop new ML algorithms and apply them to real-world applications.

Some of my recent work can be roughly clustered as follows.

Q1. How much data is needed for reliable ML? How can we design efficient algorithms?

• SAFFRON: Sparse-Graph Code Framework for Group Testing
  K. Lee, R. Pedarsani, and K. Ramchandran
  IEEE Transactions on Signal Processing 2019
  

• Community Recovery in Hypergraphs
  K. Ahn*, K. Lee*, and C. Suh
  IEEE Transactions on Information Theory 2019
  

• Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
  K. Ahn, K. Lee, and C. Suh
  IEEE Journal of Selected Topics in Signal Processing October 2018
  

• Information-theoretic Limits of Subspace Clustering
  K. Ahn, K. Lee, and C. Suh
  IEEE ISIT 2017
  

• PhaseCode: Fast and Efficient Compressive Phase Retrieval based on Sparse-Graph-Codes
  R. Pedarsani, D. Yin, K. Lee, and K. Ramchandran
  IEEE Transactions on Information Theory June 2017
  

Q2. What should I do if I don't have enough data?

• Discrete-valued Preference Estimation with Graph Side Information
  C. Jo, K. Lee
  Preprint
  

• Binary Rating Estimation with Graph Side Information
  K. Ahn, K. Lee, H. Cha, and C. Suh
  NeruIPS 2018
  

• Reprogramming GANs via Input Noise Design
  K. Lee, C. Suh, and K. Ramchandran
  ECML PKDD 2020
  

• Crash to Not Crash: Learn to Identify Dangerous Vehicles using a Simulator
  H. Kim, K. Lee, G. Hwang, and C. Suh
  AAAI 2019, ICML Workshop on Machine Learning for Autonomous Vehicles 2017
  

• Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
  K. Lee*, H. Kim*, and C. Suh
  ICLR 2018
  

Q3. How can we design trustworthy and scalable ML systems?

• Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
  S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos
  MLSys 2021
  

• FairBatch: Batch Selection for Model Fairness
  Y. Roh, K. Lee, S. Whang, and C. Suh
  ICLR 2021
  

• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
  H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos
  NeurIPS 2020
  

• FR-Train: A mutual information-based approach to fair and robust training
  Y. Roh, K. Lee, S. Whang, and C. Suh
  ICML 2020
  

• Improving Model Robustness via Automatically Incorporating Self-supervision Tasks
  D. Kim, K. Lee, and C. Suh
  NeurIPS 2019 Workshop on Meta-Learning (MetaLearn 2019)
  

• UberShuffle: Communication-efficient Data Shuffling for SGD via Coding Theory
  J. Chung, K. Lee, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
  SysML 2018, NIPS Workshop on Machine Learning Systems 2017
  

• Speeding Up Distributed Machine Learning Using Codes
  K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
  IEEE Transactions on Information Theory January 2018
  The Joint Communications Society/Information Theory Society Paper Award, 2020
  

• High-Dimensional Coded Matrix Multiplication
  K. Lee, C. Suh, and K. Ramchandran
  IEEE ISIT 2017
  

• The MDS Queue: Analysing the Latency Performance of Erasure Codes
  K. Lee, N. Shah, L. Huang, and K. Ramchandran
  IEEE Transactions on Information Theory May 2017
  

• On Scheduling Redundant Requests With Cancellation Overheads
  K. Lee, R. Pedarsani, and K. Ramchandran
  IEEE/ACM Transactions on Networking April 2017
  

• When Do Redundant Requests Reduce Latency?
  N. Shah, K. Lee, and K. Ramchandran
  IEEE Transactions on Communications February 2016
  

• A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation
  K. Lee, L. Yan, A. Parekh, and K. Ramchandran
  IEEE MASCOTS 2013
  

Q4. How can we solve real-world problems using ML?

• Large-scale and Interpretable Collaborative Filtering for Educational Data
  K. Lee, J. Chung, and C. Suh
  KDD Workshop on Advancing Education with Data 2017
  

• Machine Learning Approaches for Learning Analytics: Collaborative Filtering or Regression With Experts?
  K. Lee, J. Chung, Y. Cha, and C. Suh
  NIPS Workshop on Machine Learning for Education 2016