Hyun-Myung Woo, Ph.D.

Assistant Professor Computational Science Laboratory

Department of Biomedical & Robotics Engineering
Incheon National University

Education

Ph.D. in Electrical & Computer Engineering
Texas A&M University 2022
M.S. in Computer Science
Yonsei University 2010
B.A. of Engineering (Summa Cum Laude)
Yonsei University 2008

Selected Experience

Assistant Professor 2023 - Present
Incheon National University
Research Associate 2022 - 2023
Brookhaven National Laboratory
Research Assistant 2017, 2019-2022
Texas A&M University
Research Engineer 2016 - 2017
LG Electronics

Honors & Awards

Academic Research Award Incheon National University 2024
Excellence in Teaching Award Incheon National University 2024
The Grand Prize IDIS & ETNEWS Paper Contest 2009
Best Paper Award KIIECT Fall Conference 2009
Summa Cum Laude Yonsei University 2008
Academic Excellence Scholarship Yonsei University 2005-2007

Our Team

Computational Science Laboratory

Open Positions

Our Computational Science Laboratory is currently accepting applications for Ph.D., MS, and Undergraduate research positions. These opportunities involve conducting cutting-edge research in mathematical optimization and machine/deep learning to address complex scientific and engineering challenges.

Deep Learning Optimal Experimental Design Bayesian Methods Reinforcement Learning Bioinformatics Computational Materials Science
Contact Us for Opportunities

M.S. Students

Taeyeong Jang

Taeyeong Jang

Comparative Network Analysis Multi-modal Learning

Undergraduate Researchers

Juhyun Yeom

Juhyun Yeom

Deep Learning
Jiho Han

Jiho Han

Medical Imaging Multi-task Learning
Yoonsang Park

Yoonsang Park

Semi-supervised Learning Deep Learning
Chanhoe Kim

Chanhoe Kim

Optimal Computational Screening Campaign Deep Learning

Ph.D. Students

Coming Soon

Research

Computational Science Laboratory

Our research focuses on developing innovative computational methods at the intersection of machine learning, optimization, and scientific discovery. We aim to create intelligent systems that can autonomously make optimal decisions in complex scientific domains.

I

Bayesian Deep Learning and Uncertainty Quantification

Our core research focuses on the principled representation of uncertainty in deep learning models. By treating model parameters as probability distributions, we aim to develop models that are not only accurate but also aware of their own confidence. This is crucial for applications where reliability and trust are paramount.

Accelerating Uncertainty Quantification (UQ) and Optimal Experimental Design (OED)

Development of novel deep learning frameworks to significantly speed up the computationally expensive processes of UQ and OED.

Advanced Bayesian Neural Networks (BNNs)

Exploration and development of sophisticated BNN architectures and inference techniques to capture both aleatoric (data) and epistemic (model) uncertainty.

Efficient and Scalable Bayesian Methods for LLMs

Fine-tuning Large Language Models (LLMs) using innovative Bayesian techniques, such as Bayesian LoRA, to enhance performance while robustly quantifying predictive uncertainty.

Data-Efficient Learning Strategies

Investigating and applying methods like semi-supervised learning to improve the performance of OED in scenarios with limited labeled data.

II

Optimal Decision-Making and Automated Scientific Discovery

We aim to create intelligent systems that can autonomously make optimal decisions in complex, high-dimensional spaces. This involves the integration of deep learning, reinforcement learning, and classical optimization methods to guide scientific campaigns.

Accelerated High-Throughput Virtual Screening (HTVS)

Development of deep learning surrogate models to accelerate the identification of promising candidates within massive molecular libraries. Replacing computationally intensive quantum calculations (e.g., DFT) with highly accurate, rapid predictive models to maximize screening throughput and minimize computational resource allocation.

Optimal Molecular Screening Campaigns

Design of highly efficient molecular screening strategies by integrating mathematical optimization and Bayesian frameworks. Utilization of Multi-objective Bayesian Optimization (MOBO) to navigate competing properties, such as binding affinity and toxicity, automating the decision-making process to identify superior candidates under budgetary constraints.

De Novo Molecular Design with Generative Models

Application of state-of-the-art generative models, including Transformers and Large Language Models (LLMs), for the ground-up design of novel molecules. Focused development of innovative molecular structures optimized for specific applications, such as carbon capture, by efficiently navigating the chemical space to find compounds with desired properties.

Reinforcement Learning for Scientific Policy

Development of reinforcement learning agents capable of learning optimal screening policies within HTVS pipelines, maximizing the efficiency of the discovery process.

III

Machine Learning for Scientific and Engineering Applications

We apply our expertise in deep learning to solve critical problems across various scientific and engineering disciplines, with a focus on creating practical and impactful solutions.

Multimodal Deep Learning for Medical Image Analysis

Fusion of information from diverse medical imaging modalities using advanced deep learning architectures to improve diagnostic accuracy and automated analysis.

Solving Diverse Scientific Challenges

Addressing a wide array of problems in science and engineering by custom-designing and applying appropriate deep learning models, contributing to advancements in multiple fields.

Publications

14 Journal Articles
9 Conference Papers
1 Preprints

Preprints

1
A framework for parametric and predictive uncertainty quantification in the E3SM Land Model: Assessing the impact of site and observable heterogeneity
Jiang, Z., Isenberg, N.M., Subba, T., Woo, H.-M., Serbin, S.P., Urban, N.M., Kuang, C.
Authorea Preprints 2025

Selected Journal Publications

14
Effect of Mechanical Environment Alterations in 3D Stem Cell Culture on the Therapeutic Potential of Extracellular Vesicles
Kang, W.Y., Jung, S., Jeong, H., Woo, H.-M., Kang, M.-H., Bae, H., Cha, J.M.
Biomaterials Research 2025
13
Featured
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
Woo, H.-M., Qian, X., Tan, L., Jha, S., Alexander, F. J., Dougherty, E. R., Yoon, B.-J.
Patterns 2023
12
Featured
Neural Message Passing for Optimal Experimental Design in Complex Uncertain Systems
Chen, Q., Chen, X., Woo, H.-M., Yoon, B.-J.
Engineering Applications of Artificial Intelligence 2023
11
Featured
Optimal High-Throughput Virtual Screening Pipeline for Efficient Selection of Redox-Active Organic Materials
Woo, H.-M.¶, Allam, O.¶, Chen, J., Jang, S. S., Yoon, B.-J.
iScience 2023
10
Featured
AI-Enabled, Implantable, Multichannel Wireless Telemetry for Photodynamic Therapy
Kim, W. S.¶, Khot, M. I.¶, Woo, H.-M.¶, Hong, S., Baek, D.-H., Maisey, T., Daniels, B., Coletta, P., Yoon, B.-J., Jayne, D., Park, S. I.
Nature Communications 2022
9
Featured
Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning
Woo, H.-M., Hong, Y., Kwon, B., Yoon, B.-J.
IEEE Transactions on Signal Processing
8
MONACO: Accurate Biological Network Alignment Through Optimal Neighborhood Matching Between Focal Nodes
Woo, H.-M., Yoon, B.-J.
Bioinformatics 2020
7
A Novel Approach Using an Inductive Loading to Lower the Resonant Frequency of a Mushroom-Shaped High Impedance Surface
Gu, M., Vorobiev, D., Kim, W. S., Chien, H.-T., Woo, H.-M., Hong, S. C., Park, S. I.
Progress In Electromagnetics Research M 2020
6
NAPAbench 2: A Network Synthesis Algorithm for Generating Realistic Protein-Protein Interaction (PPI) Network Families
Woo, H.-M.¶, Jeong, H.¶, Yoon, B.-J.
PLOS ONE 2020
5
Reduced Complexity ML Signal Detection for Spatially Multiplexed Signal Transmission Over MIMO Systems With Two Transmit Antennas
Woo, H.-M., Kim, J., Yi, J.-H., Cho, Y.-S.
IEEE Transactions on Vehicular Technology 2010
4
A Computationally Efficient ML Signal Detection Technique for MIMO Systems With Two Spatial Streams
Woo, H.-M., Kim, J., Yi, J.-H., Choi, S. Y., Cho, Y.-S.
SK Telecom Review 2009
3
A Computationally Efficient Search Space for QRM-MLD Signal Detection
Hur, H., Woo, H.-M., Yang, W.-Y., Bahng, S. J., Park, Y.-O., Kim, J.
IEICE Transactions on Communications 2009
2
A Novel Soft Output Generation Method for Spatially Multiplexed MIMO Systems
Hur, H., Woo, H.-M., Bahng, S. J., Park, Y.-O., Kim, J.
KICS Journal 2008
1
An Improved Search Space for QRM-MLD Signal Detection
Hur, H., Woo, H.-M., Yang, W.-Y., Bahng, S. J., Park, Y.-O., Kim, J.
KICS Journal 2008

Selected Conference Papers

9
AI-Enabled High-Throughput Wireless Telemetry for Effective Photodynamic Therapy
Kim, W.S., Woo, H.-M., Khot, M.I., Hong, S., Jayne, D.G., Yoon, B.-J., Park, S.I.
55th Asilomar Conference on Signals, Systems, and Computers 2021
8
Network-Based RNA Structural Alignment Through Optimal Local Neighborhood Matching
Woo, H.-M., Yoon, B.-J.
Asilomar Conference on Signals, Systems, and Computers 2020
7
Machine Learning Enabled Adaptive Wireless Power Transmission System for Neuroscience Study
Hyun-Myung Woo, Woo Seok Kim, Sungcheol Hong, Vivekanand Jeevakumar, Clay M. Smithhart, Theodore J. Price, Byung-Jun Yoon, and Sung Il Park
Asilomar Conference on Signals, Systems, and Computers 2020
6
Comprehensive Updates in Network Synthesis Models to Create An Improved Benchmark for Network Alignment Algorithms
Hyun-Myung Woo, Hyundoo Jeong, and Byung-Jun Yoon
ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB) 2018
5
Reduced complexity ML signal detection for spatially multiplexed signal transmission over MIMO systems with two transmit antennas
Hyun-Myung Woo and Jaekwon Kim
KIIECT Fall Conference 2009
4
Reduced complexity ML signal detection for spatially multiplexed signal transmission over MIMO systems with two transmit antennas
Hyun-Myung Woo and Jaekwon Kim
KIISE Gangwon Branch Conference 2009
3
Reduced complexity ML signal detection for spatially multiplexed signal transmission over MIMO systems with two transmit antennas
Hyun-Myung Woo, Jaekwon Kim, J. H. Yi, and Yong-Soo Cho
JCCI 2009
2
A Comparative Studies of Channel Shortening Techniques for OFDM System
Hyun-Myung Woo and Jaekwon Kim
KIIECT Summer Conference 2008
1
An Improved Search Space for QRM-MLD Signal Detection
Hoon Hur, Hyun-Myung Woo, W. Y. Yang, S. J. Bahng, Y. O. Park, and Jaekwon Kim
JCCI 2008

Research Projects

Current Projects

Alchemist 2025 - Present

Development of Oral Wearable/Implantable Neural Stimulator for Sleep Apnea Rehabilitation Treatment

Korea Institute for Advancement of Technology (KIAT)
First Research 2023 - Present

Reinforcement Learning-based Optimal Decision Making in High-Throughput Virtual Screening (HTVS) Pipelines

The National Research Foundation of Korea (NRF)
Internal 2025 - Present

Deep Learning-Driven Multi-Objective Uncertainty Quantification for Accelerated Optimal Experimental Design

Incheon National University

Previous Projects

2024-2025
Knowledge Discovery using Machine Learning for the Objective-based Optimal Experimental Design

Incheon National University (Internal Research Project)

2023-2024
Accelerating the Objective-based Optimal Experimental Design using Machine Learning

Incheon National University (Internal Research Project)

Talks

8
Accelerating Bayesian Optimal Experimental Design using Deep Learning
Inha University Apr, 29, 2025
7
Designing and Optimizing High-Throughput Virtual Screening Pipelines: A General Framework for Maximizing Computational Efficiency
KIIS Spring Conference - special session, Kumoh National Institute of Technology Apr, 26, 2025
6
Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
Yonsei University Oct, 30, 2023
5
Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
Sungkyunkwan University Apr, 24, 2023
4
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
BioSeminar @ Texas A&M University Apr, 29, 2022
3
Optimal decision making for accelerating scientific discovery
Computational Science Initiative Seminar @ Brookhaven National Laboratory Mar, 25, 2022
2
Machine Learning Enabled Adaptive Wireless Power Transmission System for Neuroscience Study
BioSeminar @ Texas A&M University Oct, 23, 2020
1
Accurate biological network alignment through an iterative optimal mapping between neighborhood sets of focal nodes
BioSeminar @ Texas A&M University Oct, 4, 2019

Let's Get In Touch!

Please contact me if you may have any questions! :D