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Jay-Yoon Lee Assistant Professor Graduate School of Data Science,   Seoul National University    Email : lee dot jayyoon @ snu dot ac.kr Resume / C.V. / Google scholar / Research Statement

Research Interests

The goal of my research is injecting knowledge/constraints into neural models, primarily for natural language processing (NLP) tasks. I am broadly interested in structured prediction, multi-task learning, logical reasoning, and better representation learning for the aforementioned topics. Currently I am exploring these topics at Graduate School of Data Science (GSDS), Seoul National University (SNU), with SKI-ML lab members. Prior to joining SNU I was a postdoc associate at IESL with Professor Andrew McCallum and during my Ph.D. with my advisor Jaime Carbonell.

News

• 11/30/23 Our members (Jonghyun Song, Hyeryung Son, Mooho Song) won the first prize in the K-Datascience Hackathon!(Team SSSCI)
• 12/06/23 Our paper "Machine Reading Comprehension using Case-based Reasoning" got accepted at EMNLP 2023!
• 11/06/23 Our paper "Parameter-Efficient fine-tuning of InstructBLIP for Visual Reasoning Tasks" got accepted at NeurIPS 2023 workshop!
• 06/01/23 6 graduate students have joined our lab
• 05/03/23 Presenting "Learnable loss function that automatically captures multivariate dependency" at AI frenz
  (youtube link (in Korean)).
• 05/03/23 Our paper "Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation" got accepted at ACL2023!
  
• 03/22/23, 04/12/23 Presented "Exploring the limitations of ChatGPT" at AI frenz (youtube link (in Korean))and at SNU Law forum (a.k.a. 서울대 법과 문화 포럼).
• 03/08/23 Presented at GSDS seminar at SNU on the topic of "Exploring the limitations of ChatGPT" (video).
• 02/24/23 Presented "Automatically capturing label dependencies" at MSU CSE seminar
  (Not recorded, but will give a similar talk soon, so stay tuned!)
• 12/01/22 Going to NeurIPS22 to represent our paper Structured Energy netwrok As a Loss (SEAL)

Education

• Aug 2013 - June 2020,   Ph.D. in Computer Science,
  Computer Science Department,   School of Computer Science,   Carnegie Mellon University
• Sep 2011 - May 2013,   M.S. in Computer Science,
  Language Technology Institute (LTI) & Lane Center for Computational Biology (LCCB),   School of Computer Science,   Carnegie Mellon University
• Aug 2008, B.S. Summa Cum Laude in Electrical Engineering,
  Department of Electrical Engineering and Computer Science,   College of Information Science & Technology, 
  Korea Advanced Institute of Science and Technology (KAIST)

Work Experiences

• Sept 2022 – current, Assistant Professor at Graduate School of Data Science ,
  Seoul National University, Seoul, South Korea
• July 2020 – July 2022, Postdoctoral Associate under Professor Andrew McCallum,
  University of Massachussets, Amherst, MA
• Oct 2015 – July 2020, Research Assistant under Professor Jaime Carbonell,
  Carnegie Mellon University, Pittsburgh, PA
• June 2012 – Oct 2015, Research Assistant under Professor Christos Faloutsos,
  Carnegie Mellon University, Pittsburgh, PA
• Oct 2019 – Jan 2020, Research Intern, Language & Speech , Google AI, New York, NY
• June 2019 – Aug 2019, Research Intern, Deep Learning Group, Microsoft Research, Redmond, WA
• June 2017 – Aug 2017, Research Intern, Information and Data Sciences Group, MSR & Bing, Redmond, WA
• June 2016 – Aug 2016, Research Intern, IRML, Oracle Labs, Boston, MA
• May 2015 – Aug 2015, Research Intern, Yahoo! Labs, Sunnyvale, CA
• Sep 2009 – Jun 2011, Researcher, ZEROIN Corporation, Seoul, Korea
• Jul 2008 – Aug 2009, Associate, NICE Pricing Services, INC., Seoul, Korea
• Oct 2007 – Jan 2008, Intern, AMICUS Wireless Technology, Sunnyvale, CA

Teaching

• Spring 2023, Math and Statistics Foundations for Data Science (link to course schedule)
• Fall 2022, NLP and its recent progress
• Spring 2015, 10-701 Introduction to Machine Learning
Teaching Assistant for Professor Alex J. Smola
• Fall 2014, 15-826 Multimedia Databases and Data Mining
Teaching Assistant for Professor Christos Faloutsos

Course Highlights at CMU

Deep Reinforcement Learning (10-703),  Deep Learning (10-707),  Statistical Machine Learning (10-702),  Convex Optimization (10-725),  Spectral Graph Theory (15-859N),  Intermediate Statistics (10-705),  Multimedia Databases and Data Mining (15-826) [Project Report , Presentation],  Advanced Probability Overview (36-752)

Responsibilities

Reviewer for ICML (2019-2023), NeurIPS (2019-2023, Area chiar 2023), ICLR (2021-2023), ACL (2019-2023),
and EMNLP (2022, Area chiar 2023).

Awards and Honors

• Mar 2003 – Feb 2008, Scholarship in Science & Technology, Korea Student Foundation
• Mar 2005 – Feb 2008, Scholarship in Mathematics, Korea Foundation for Advanced Studies
• Aug 2002, Prize of encouragement at the 5th Nationwide High School Mathematics Competition, Korea University

Other Experiences

• June 2016, top team with $5k prize, , Qualcomm NeuroHackathon, Carnegie Mellon University, PA
• Aug 2016, Best Presenter $3k prize, Global Top Talent Forum, Hyundai Motor Group, San Diego, CA