Hello, I am Jeong Min Lee, a Senior Research Scientist at Instagram. 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.


  • 2024-06 Serve NeurIPS 2024 as an Area Chair
  • 2024-05 Serve RecSys 2024 as a program committee member
  • 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


  • 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) 2021Best 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) 2019Best 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
  • 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) 2013Best 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
  • 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

  • Instagram Menlo Park CA, Aug 2023 - Present
    Research Scientist / Instagram Reels - Recommendations Core Ranking
  • Meta Menlo Park CA, Dec 2020 - Aug 2023
    Research Scientist / AI Applied Research Group (Modern Recommendation Systems)
  • 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



  • 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


  • 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

  • Global Health Explorer
    Semantic web tool that can be used to conduct public health surveillance using Twitter

  • Data Cube Browser
    Data exploration tool in Javascript using D3 and jQuery that makes it simple to explore data expressed as RDF Data Cubes.
  • 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


Jeong Min Lee
1 Hacker Way
Menlo Park, CA 94025

(firstname: jeongmin)