Jeong Min Lee, Senior Research Scientist at Meta AI

Welcome!

Hello, I am Jeong Min Lee, a Senior Research Scientist at Meta AI (Applied Research). I obtained Ph.D. in Computer Science from University of Pittsburgh. My research advisor was Dr. Milos Hauskrecht

My research interests are in neural event time-series (sequence) modeling and representation learning for complex high-dimensional sequence data such as clinical event time-series (EHRs) and online user activity.

Updates

2023-07 Work on "Personalized Event Prediction for Electronic Health Records" is published
2023-06 Served RecSys 2023 as a program committee member
2023-03 Served ACL 2023 as a reviewer.
2022-08 Promoted to Senior Research Scientist!
2022-07 Served EMNLP 2022 as a program committee member
2022-06 Served AI in Medicine Journal as a reviewer
2022-04 Obtained Ph.D. degree!
2022-03 Our paper on Residual Mixture of Experts is accepted at AI in Medicine conference (AIME-2022)
2021-11 Apply for 2022 Summer Research intern at Meta AI
2021-10 Our work on Transformers on Session-Based Recommendation is accepted at WiML in NeurIPS 2021
2021-07 Our work on Transformer for Recommendation is accepted at RecSys 2021 conference
2021-06 Our work is nominated for the shortlist for Best Paper in AI in Medicine conference (AIME-2021)
2021-05 Served EMNLP 2021 as a program committee member
2021-03 Served AIME 2021 as a reviewer
2021-03 Served ECML-PKDD 2021 as a program committee member
2021-03 Paper on personalized prediction method accepted at AI in Medicine conference (AIME-2021)
2021-02 Served ACL-IJCNLP 2021 as a reviewer
2021-01 Paper has been published at Journal of Artificial Intelligence in Medicine
2020-12 Started to work at Facebook as Research Scientist
2020-08 Presented our work "Multi-scale Temporal Memory" at AIME-2020 conference
2020-07 Received student scholarship from AIME-2020 conference
2020-07 Paper on Multi-scale Temporal Memory is accepted at AIME-2020 conference (Paper)
2020-05 Started research internship at NVIDIA
2020-03 Passed dissertation proposal
2020-02 Paper on periodicity-based event time-series prediction is accepted at FLAIRS conference
2019-08 Paper on context-based LSTM won the best paper award from Artificial Intelligence in Medicine 2019

Publications

Personalized Event Prediction for Electronic Health Records
Jeong Min Lee and Milos Hauskrecht
Artificial Intelligence in Medicine (Journal)
(Paper) (ScienceDirect)
Neural Event Prediction for Clinical Event Time-Series
Jeong Min Lee
Ph.D. Thesis, University of Pittsburgh 2022
(Thesis) (Slides)
Learning to Adapt Clinical Sequences with Residual Mixture of Experts
Jeong Min Lee and Milos Hauskrecht
20th International Conference on Artificial Intelligence in Medicine (AIME) 2022
(Paper) (Poster) (Code)
Effectiveness of Transformers on Session-Based Recommendation
Sara Rabhi, Ronay Ak, Gabriel de Souza Pereira Moreira, Jeong Min Lee, and Even Oldridge
16th Women in Machine Learning Workshop (WiML) in NeurIPS 2021
(Poster Presentation)
Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation
Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge
15th ACM Conference on Recommender Systems (RecSys) 2021
(Paper) (BibTeX)
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
Jeong Min Lee and Milos Hauskrecht
19th International Conference on Artificial Intelligence in Medicine (AIME) 2021 — Best Paper Nominee
(Paper) (BibTeX) (Code)
Modeling Multivariate Clinical Event Time-series with Recurrent Temporal Mechanisms
Jeong Min Lee and Milos Hauskrecht
rtificial Intelligence in Medicine (Journal)
(Paper) (BibTeX)
MTM: Multi-scale Temporal Memory for Clinical Event Time-Series Prediction
Jeong Min Lee and Milos Hauskrecht
18th International Conference on Artificial Intelligence in Medicine (AIME) 2020
(Paper) (Presentation) (BibTeX)
Clinical Event Time-series Prediction with Periodic Events
Jeong Min Lee and Milos Hauskrecht
The Thirty-Third International FLAIRS Conference 2020
(Paper) (BibTeX)
Recent context-based LSTM for Clinical Event Time-series Prediction
Jeong Min Lee and Milos Hauskrecht
17th Conference on Artificial Intelligence in Medicine (AIME) 2019 — Best Paper Award
(Paper) (BibTeX)
Diagnosis Code Prediction from Electronic Health Records as Multilabel Text Classification: A Survey
Jeong Min Lee and Aldrian Obaja Muis
Neural Network for Natural Language Processing (NLP) at CMU - Fall 2017 Course Project
(Paper)
Modeling Patient Mortality from Clinical Text by Combining Topic Modeling and Ontological Feature Learning with Group Regularization
Jeong Min Lee, Charmgil Hong, and Milos Hauskrecht
Machine Learning for Signal Processing at CMU - Fall 2016 Course Project
(Paper) (Presentation)
Towards Next Generation Health Data Exploration: A Data Cube-based Investigation into Population Statistics for Tobacco
James P. McCusker, Deborah L. McGuinness, Jeong Min Lee, Chavon Thomas, Paul Courtney, Zaria Tatalovich, Noshir S. Contractor, Glen D. Morgan, and Abdul R. Shaikh
In Proceedings of Hawaii International Conference on System Sciences (HICSS) 2013 — Best Paper Award
(Paper) (BibTeX)
Climate Change, Disaster and Sentiment Analysis over Social Media Mining
Jeong Min Lee, James McCusker, and Deborah L. McGuinness
American Geophysical Union (AGU) Fall Meeting 2012, Linked Data for Earth and Space Science Posters Session
(Poster)
Public Health Surveillance Using Global Health Explorer
James P. McCusker, Jeong Min Lee, Thomas Chavon, and Deborah L. McGuinness
In Proceedings of Joint Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM), co-located with the 11th International Semantic Web Conference (ISWC) 2012
(Paper) (BibTeX)

Work Experience

Meta (Prev. Facebook) Menlo Park CA, Dec 2020 - Present
Research Scientist / AI Applied Research Group
NVIDIA Remote, May 2020 - August 2020
Research Intern / Self-attention (transformer) networks for multivariate sequence prediction
Facebook Seattle WA, May 2019 - July 2019
Machine Learning PhD Intern / Pages Sciences team (Project: contents embedding, personalized page recommendation / representation learning)
Facebook Seattle WA, May 2018 - Aug 2018
Machine Learning PhD Intern / Pages Sciences team (Project: sequence models for page recommendation / LSTM and RNN)
Clinical Translational Science Institute, University of Pittsburgh Pittburgh PA, May 2016 - Aug 2016
Data Science and Analysis Intern
Daeyang Luke Hospital & Baobab Health Trust Malawi, Feb 2013 - Jun 2013
Software Engineer
Tetherless World Constellation, Rensselaer Polytechnic Institute Troy NY, May 2012 - Aug 2012
Research Intern

Open Source

Transformers4Rec: a library for sequential and session-based recommendation on Transformer
https://github.com/NVIDIA-Merlin/Transformers4Rec

Tutorial/Notes

Introduction to MIMIC-3 Database
MIMIC-3 is a comprehensive public EHR dataset. The slide I gave presentation at my lab meeting in 2016
Brief note on links to basic NLP methods for clinical notes in MIMIC-3 Database
Tutorial on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Architectures
Lecture slide at CS3750 Advanced Machine Learning Course - 2018 Fall
Summary on Word Embedding, Convolutional Neural Network (CNN), and Memory Networks
Comprehensive exam slide material - 2018 Spring
Tutorial on Word Embedding Methods: Skipgram and Continuous Bag-of-Words (CBOW)
Machine Learning Group talk - 2017 Spring

Teaching

TA for Introduction to Machine Learning (CS1675) 2019 Spring
TA for Programming Languages for Web Applications (CS 1520) 2020 Spring, 2019 Fall, 2018 Spring, 2017 Fall, 2016 Spring & Fall, 2015 Spring
TA for Introduction to Systems Software (CS449) 2018 Fall
TA for Algorithm Implementation (CS1501) 2018 Fall
TA for Introduction to Programming with Python (CS8) 2015 Fall

Projects

Discrete Event Prediction and Modeling in Time Series Data
- Develop time series models that can represent and learn behaviors of complex event time series in Electronic Health records.
- Built end-to-end data pipeline that extracts features from raw data sources, trains models on GPU, tunes hyperparameters, and conducts evaluation and visualizes result.
- Models and the pipeline are built with PyTorch, Python, and bash.
Clinical Knowledge Modeling using Medical Textbooks
- Developed a machine learning model that learns to quantify the similarity of clinical concepts such as disease, medication and lab test from various knowledge sources including medical textbooks, websites, and knowledge graphs.
- Embedding method (Skip-gram) was used and the aim of the project is to research the potential of embedded distance measures for feature selection of classifying electronic health record data.
- The online text scrapping and parsing scripts were built with Beautiful Soup library on Python.
- Using Biomedical Annotating API (http://bioportal.bioontology.org), free texts in textbooks and websites were transformed into ontological concepts. Word embeddings were trained by Gensim library.
Modeling Patient Mortality from Unstructured Text Data
- Developed a machine learning model that predicts patient mortality from clinical note data. Latent Dirichlet Allocation and sparse group regularization were used for feature learning and SVM was used for classification.

Previous Projects

Electronic Medical Record System Project in Africa
Patient registration and billing module, a part of EMR, for Daeyang Luke Hospital in Malawi, East Africa
Detail
Global Health Explorer
Semantic web tool that can be used to conduct public health surveillance using Twitter
Detail
Data Cube Browser
Data exploration tool in Javascript using D3 and jQuery that makes it simple to explore data expressed as RDF Data Cubes.
Detail
Empowering community health worker (CHW) through connecting mobile health (mHealth) and electronic medical record system (EMR)
Independent Research, Advisor: Daiyon Joh
Toward Next Generation of Global Disaster Response and Coordination System (GDRCS)
Class Project - X Informatics
Detail

Contact

Jeong Min Lee
1 Hacker Way
Menlo Park, CA 94025

firstname@fb.com
(firstname: jeongmin)