Welcome!

Hi! I am a Ph.D. candidate in the department of Electrical and Computer Engineering (ECE) at University of Wisconsin-Madison, advised by Prof. Dimitris Papailiopoulos and Prof. Kangwook Lee. I received my M.S. in 2018 from Seoul National University, where I was advised by Prof. Jungwoo Lee and learned about communication systems. I also received my B.S. in ECE from Seoul National University. I am a recipient of the Korean Government Scholarship Program for Study Overseas.

I am open to research collaboration and internship / full time opportunities.

Research Interest

I am interested in machine learning, with the focus on finding efficient, robust and scalable machine learning algorithm. Recently, I’ve been interested in understanding the algorithmic capabilities of large language models and ways to improve performance through data, prompting, inference-time methods, and verifiers. Previously, I have worked on neural network pruning.

News

• (Feb. 2025) Our paper on Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges is on Arxiv!
• (May. 2024) Our paper on Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks is accepted at ICML 2024!
• (Jan. 2024) Our paper on Teaching Arithmetic to Small Transformers is accepted at ICLR 2024! - Paper | Poster
• (Aug. 2023) Gave a Short Talk at the Simons Institute LLM workshop! - Video | Slides
• (Jun. 2022) Our paper on Super Seeds: extreme model compression by trading off storage with computation is accepted at ICML 2022 UpML Workshop (Spotlight)! - Paper | Poster

Selected Publications

• Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges, ICLR 2025 Workshop (spotlight)
  N. Lee, Z. Cai, A. Schwarzschild, K. Lee, D. Papailiopoulos
  

• Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks, ICML 2024
  J. Park, J. Park, Z. Xiong, N. Lee, J. Cho, S. Oymak, K. Lee, D. Papailiopoulos
  

• Teaching Arithmetic to Small Transformers, ICLR 2024
  N. Lee, K. Sreenivasan, J. D. Lee, K. Lee, D. Papailiopoulos
  

• Super Seeds: extreme model compression by trading off storage with computation, ICML 2022 Workshop (spotlight)
  N. Lee, S. Rajput, J. Sohn, H. Wang, A. Nagle, E. Xing, K. Lee, D. Papailiopoulos
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• On the Design of Tailored Neural Networks for Energy Harvesting Broadcast Channels: A Reinforcement Learning Approach, IEEE Access 2020
  H. Kim, J. Kim, W. Shin, H. Yang, N. Lee, S. Kim, J. Lee.

• Rate Maximization with Reinforcement Learning for Time-Varying Energy Harvesting Broadcast Channels, IEEE Globecom2019
  H.Kim, W. Shin, H. Yang, N. Lee, J. Lee.

• Analog network coding using differential and double-differential modulation with relay selection, ICT Express 2019
  S. Heo, C. Kim, N. Lee, J. Lee. –>

Teaching Experience

Graduate Teaching Assistant

• (UW-Madison CS/ECE 561) Probability and Information Theory in Machine Learning, 2022 Fall
• (UW-Madison CS/ECE/ME 532) Matrix Methods in Machine Learning, 2021 Fall
• (SNU) Information Theory, 2019-1
• (SNU) Introduction to Communications, 2018-2

Undergraduate Teaching Assistant

• Foundation of Physics 1, 2015-1, 2016-1,2017-1
• Foundation of Physics 2, 2015-2, 2016-2, 2017-2

Talks

• (02.24.2025) Talk at the FLaNN Seminars
• (09.11.2023) Talk at the IFDS Ideas Forum

• (08.18.2023) Short Talk at the Simons Institute LLM Workshop - Video

  Slides

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