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 codinginspired algorithms for scalable ML systems.
Casually speaking, my research goal is to answer the following three questions:
Q1. How much data is needed for reliable ML?
Q2. What should I do if I don't have enough data?
Q3. How can we design scalable ML systems?
I also develop new ML algorithms and apply them to realworld applications.
Q4. How can we solve realworld problems using ML?
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: SparseGraph 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
Informationtheoretic Limits of Subspace Clustering
K. Ahn, K. Lee, and C. Suh
IEEE ISIT 2017
PhaseCode: Fast and Efficient Compressive Phase Retrieval based on SparseGraphCodes
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?
Binary Rating Estimation with Graph Side Information
K. Ahn, K. Lee, H. Cha, and C. Suh
NeruIPS 2018
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 scalable ML systems?
UberShuffle: Communicationefficient 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
HighDimensional 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 realworld problems using ML?
Improving Model Robustness via Automatically Incorporating Selfsupervision Tasks
D. Kim, K. Lee, and C. Suh
NeurIPS 2019 Workshop on MetaLearning (MetaLearn 2019)
Largescale 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
