Jeongkyu Lee
(he/him/his)
Teaching Professor
Research interests
- Computer vision
- Medical image segmentation
- Deep learning
Education
- PhD in Computer Science and Engineering, University of Texas at Arlington
- MS in Computer Science, Sogang University
- BS in Mathematics, Sungkyunkwan University
Biography
Jeongkyu Lee is a teaching professor at the Khoury College of Computer Sciences at Northeastern University, based in Silicon Valley. With a research background that bridges deep learning, robotics, and big data, Lee brings both technical expertise and educational innovation to the classroom. His courses in big data, NoSQL, Python, and statistics reflect his commitment to preparing students for emerging data-driven fields.
Before joining Northeastern, Lee worked as a database administrator at companies such as Hana Bank and IBM. His academic journey and professional career have been defined by a focus on real-world computing solutions across data engineering, computer vision, and intelligent systems.
He leads the “Anyone Python” project — an informal, global online learning series focused on basic Python and computer science fundamentals. The course has served hundreds of learners across countries through an accessible, stress-free teaching model.
Outside of work, Lee enjoys traveling by train, finding inspiration and calm through his journeys.
Projects
Recent publications
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XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation
Citation: Byunghyun Ko, Anning Tian, Jeongkyu Lee. (2025). XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation CoRR, abs/2508.06258. https://doi.org/10.48550/arXiv.2508.06258 -
An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
Citation: Mengyuan Liu, Jeongkyu Lee. (2025). An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan CoRR, abs/2507.08690. https://doi.org/10.48550/arXiv.2507.08690 -
YOLO-KAN: Exploring the Adaptability of Kolmogorov-Arnold Networks in the YOLO Model
Citation: Xiaofeng Zhao, Jeongkyu Lee, Tehmina Amjad. (2025). YOLO-KAN: Exploring the Adaptability of Kolmogorov-Arnold Networks in the YOLO Model CAI, 1-6. https://doi.org/10.1109/CAI64502.2025.00144 -
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan
Citation: Mengyuan Liu, Yixiao Chen, Anning Tian, Xinmeng Wu, Mozhi Shen, Tianchou Gong, Jeongkyu Lee. (2025). Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan CoRR, abs/2504.04066. https://doi.org/10.48550/arXiv.2504.04066 -
SQLearn: Automated SQL Statement Assessment using Structure-based Analysis
Citation: Sumukhi Ganesan, Tianchou Gong, Jeongkyu Lee. (2024). SQLearn: Automated SQL Statement Assessment using Structure-based Analysis SIGCSE (2), 1644-1645. https://doi.org/10.1145/3626253.3635607 -
MRI Segmentation of Musculoskeletal Components Using U-Net: Preliminary Results
Citation: Divit Vasu, Seungmoon Song, Hans Kainz, Jeongkyu Lee. (2024). MRI Segmentation of Musculoskeletal Components Using U-Net: Preliminary Results ICBBB, 30-35. https://doi.org/10.1145/3640900.3640902 -
Detection of Northern Corn Leaf Blight Disease in Real Environment Using Optimized YOLOv3
Citation: Brian Song, Jeongkyu Lee. (2022). Detection of Northern Corn Leaf Blight Disease in Real Environment Using Optimized YOLOv3 CCWC, 475-480. https://doi.org/10.1109/CCWC54503.2022.9720782 -
An Event Detection Platform to Detect Gender Using Deep Learning
Citation: Abdulrahman Aldhaheri, Khaled Almgren and Jeongkyu Lee, “An Event Detection Platform to Detect Gender Using Deep Learning,” Proc. of the 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 360-364, October 28–31, 2020 -
Topological Data Analysis for Classification of Heart Disease Data
Citation: Fatima Ali Aljanobi, and Jeongkyu Lee, “Topological Data Analysis for Classification of Heart Disease Data,” Proc. of 2021 IEEE International Conference on Big Data and Smart Computing (BigComp 2021), January 17–20, 2021. -
Miniature Humanoid Upgrade for Material Handling Tasks in Humanoid Challenge
Citation: Kiwon Sohn, Jeongkyu Lee, and Kevin Huang. “Miniature Humanoid Upgrade for Material Handling Tasks in Humanoid Challenge,” Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition. Volume 4: Dynamics, Vibration, and Control. Salt Lake City, Utah, USA. November 11–14, 2019 -
Mapping Areas using Computer Vision Algorithms and Drones
Citation: Bashar Alhafni, Saulo Fernando Guedes, Lays Cavalcante Ribeiro, Juhyun Park, Jeongkyu Lee. (2019). Mapping Areas using Computer Vision Algorithms and Drones CoRR, abs/1901.00211. http://arxiv.org/abs/1901.00211 -
AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines
Citation: Almgren, K., Krishna, M., Aljanobi, F., and Jeongkyu Lee, “AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines,” Entropy, 20(12), 982, 2019. -
H2Hadoop: Improving Hadoop Performance Using the Metadata of Related Jobs
Citation: Hamoud Alshammari, Jeongkyu Lee, and Hassan Bajwa, “H2Hadoop: Improving Hadoop Performance Using the Metadata of Related Jobs,” IEEE Trans. Cloud Computing 6(4): 1031-1040, 2018.
