Jeon Lee, Ph.D.
Assistant Professor

I was originally trained as a medical device developer and innovator with expertise in signal processing and machine learning (ML) in the Dept. Biomedical Engineering, Yonsei University, South Korea. I worked on several medical device development projects, including cardiac pacemakers, automated external defibrillators, fetal hypoxia detection systems, wearable ECG monitors, etc. These projects required deep understanding of electrical and electronic engineering, biomedical sensors, computational algorithms, and human physiology. I was recognized for my innovation in this work with a Biennial Best Paper Award by the Korean Institute of Electrical Engineers in 2003 and with a Young Investigator Award by the International Federation for Medical and Biological Engineering in 2005 (Lee et al., 2005). One ML paper we published in 2005 has become the world’s second most cited paper in the area of support vector machine (SVM)-based arrhythmia classification (Song et al., 2005).

After obtaining my Ph.D. in 2006, I continued my research in medical device and ML algorithm development as an independent researcher. In 2006, I joined the Korea Institute of Oriental Medicine (KIOM), where I led a team to develop a robotic 3D-pulse analyzer in collaboration with the startup company DaeyoMedi Co. in Korea. Specifically, my team developed novel 3-D pulse pressure sensors, a robotic arm, and signal processing and ML algorithms and performed clinical trials to evaluate the analyzer and build up its clinical database. In 2009, I opened my first lab as an Assistant Professor in Dept. Biomedical Engineering, Daegu Haany University. From 2011 to 2012, after moving to the Dept. Biomedical Engineering, Yonsei University as a Research Professor, I was involved in a project aiming at developing a continuous positive airway pressure (CPAP) device for sleep apnea patients. During this period, I developed a few novel computational algorithms—one of them was patented and transferred to a medical device start-up, MEZOO in Korea (Lee et al., 2012).

In 2013, I started my career in the U.S. as a postdoc trainee at Johns Hopkins School of Medicine under mentorship of Joseph Finkelstein, M.D., Ph.D. I pursued this training path to expand my research to telemedicine for chronic disease patients. We provided telecare solutions including online patient education, home-healthcare devices, and real-time patient monitoring. In anticipation of the revolution in Artificial Intelligence (AI) and Big Data Analysis, I moved in 2015 to the Dept. Computer Science and Electrical Engineering at University of Maryland, Baltimore County, to study ML algorithms. After a short 5-month postdoc, I was promoted to Assistant Research Scientist (parallel to Assistant Professor). During this period, I developed two seminal computational algorithms, called kernel dictionary learning (DL) and online kernel DL (Lee and Kim, 2015; Lee and Kim, 2016). My kernel DL algorithm was applied to brain tumor segmentation in MR images (Lee and Kim, 2015).

In 2016, I joined the White lab in the Dept. Cell Biology at UT Southwestern Medical Center to contribute to high-throughput cancer drug screening projects, in which multiple institutes collaborated to computationally determine the mechanism of action by natural products in killing cancer cells. We integrated gene expression and image data of treated cells to identify novel cancer drug candidates in natural product libraries. Meanwhile, as a part of the AstraZeneca-Sanger Drug Combination DREAM Consortium, I also developed ML models for predicting cancer drug combination responses based on a patient’s genome (Menden et al., 2019). In 2017, I joined the Bioinformatics Core Facility (BICF) in a Senior Research Associate position. I supported a wide range of projects involving from computational analysis of biological, metabolomics, and NGS data (Wang et al., 2018; Henry et al., 2018; Bhave et al., 2020) to DL model development for clinical outcome predictions (Shah et al., 2020; Hallac et al., 2019). I was promoted to Assistant Professor at Lyda Hill Department of Bioinformatics in December, 2020. With research career focused on adopting ML to the full spectrum of the biomedical field, I have been contributing to BICF’s mission of serving UTSW community with reliable and innovative computational solutions.

Graduate School

Yonsei University (1999), Engineering

Graduate School

Yonsei University (2006), Engineering

Undergraduate

(2016), Biomedical Engineering

Graduate School

(2016), Biomedical Engineering

Graduate School

(2016), Biomedical Engineering

• Computational methods for multi-modal and multi-omics data analysis
• Deep Learning model for medical image analysis and clinical outcome prediction
• Machine Learning based biomarker discovery and outcome prediction
• Single cell RNA-seq analysis and related computational algorithms

Featured Publications

Functional characterization of 67 endocytic accessory proteins using multiparametric quantitative analysis of CCP dynamics.

Bhave M, Mino RE, Wang X, Lee J, Grossman HM, Lakoduk AM, Danuser G, Schmid SL, Mettlen M, Proc Natl Acad Sci U S A 2020 Nov

Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation.

Shah N, Farhat A, Tweed J, Wang Z, Lee J, McBeth R, Skinner M, Tian F, Thiagarajan R, Raman L, J Clin Med 2020 Aug 9 9

Identifying Ear Abnormality from 2D Photographs Using Convolutional Neural Networks.

Hallac RR, Lee J, Pressler M, Seaward JR, Kane AA, Sci Rep 2019 Dec 9 1 18198

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.

Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, Tang EKY, Garnett MJ, Veroli GYD, Fawell S, Stolovitzky G, Guinney J, Dry JR, Saez-Rodriguez J, Nat Commun 2019 06 10 1 2674

A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra.

Henry GH, Malewska A, Joseph DB, Malladi VS, Lee J, Torrealba J, Mauck RJ, Gahan JC, Raj GV, Roehrborn CG, Hon GC, MacConmara MP, Reese JC, Hutchinson RC, Vezina CM, Strand DW Cell Rep 2018 Dec 25 12 3530-3542.e5

restrains pancreatic neoplasia formation.

Wang SC, Nassour I, Xiao S, Zhang S, Luo X, Lee J, Li L, Sun X, Nguyen LH, Chuang JC, Peng L, Daigle S, Shen J, Zhu H Gut 2018 Oct

Truncated APC Mutation Induces Asef Activated Golgi Fragmentation.

Kim SB, Zhang L, Yoon J, Lee J, Min J, Li W, Grishin NV, Moon YA, Wright WE, Shay JW Mol. Cell. Biol. 2018 Jun

Normative Values of Short-Term Heart Rate Variability Parameters in Koreans and Their Clinical Value for the Prediction of Mortality.

Lee CH, Lee JH, Son JW, Kim U, Park JS, Lee J, Shin DG Heart Lung Circ 2018 May 27 5 576-587

Online kernel dictionary learning on a budget

Jeon Lee, Seung-Jun Kim 50th Asilomar Conference on Signals, Systems and Computers 2016

Brain tumor image segmentation using kernel dictionary learning.

Jeon LeeSeung-Jun KimRong ChenHerskovits EH, Annu Int Conf IEEE Eng Med Biol Soc 2015 Aug 2015 658-61

 Featured Books

Tonometric Vascular Function Assessment. In Biomedical Engineering

Jeon Lee and Ki Chang Nam (2009). InTech

• Silver medal, RSNA Intracranial Hemorrhage Detection
  Kaggle computational challenge (2019)
• Silver medal, SIIM-ACR Pneumothorax Segmentation
  Kaggle computational challenge (2019)
• Lyda Hill Award, U-HACK MED 2018
  UTSW biomedical hackathon competition (2018)
• Contribution Award
  The Institute of Electronics and Information Engineers, Korea (2012)
• Best Paper Presenter
  BME2008 Hong Kong (2008)
• Best Researcher Award
  Korea Institute of Oriental Medicine (2008)
• Young Investigator Award
  International Federation for Medical and Biological Engineering (2005)
• Biennial Best Paper Award
  Korean Institute of Electrical Engineers (2003)

• Senior Member, Institute of Electrical and Electronics Engineers (IEEE) (2015)
• Member, IEEE Engineering in Medicine and Biology Society (2012)