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Members

Open Positions 연구원 모집

Our Computational Science Laboratory is currently accepting applications for Ph.D., M.S., and Undergraduate research positions. These opportunities involve conducting cutting-edge research in deep learning/machine learning and mathematical optimization to address complex scientific and engineering challenges.
Computational Science Laboratory에서는 현재 박사, 석사 및 학부 연구생을 모집하고 있습니다. 우리 연구실은 딥러닝/머신러닝과 수학적 최적화를 활용하여 복잡한 과학 및 공학적 난제를 해결하는 최첨단 연구를 수행하고 있습니다.

M.S. Students 석사 과정

  • Taeyoung Jang 장태영

Undergraduate Researchers 학부 연구생

  • Juho Lee 이주호
  • Yunsang Park 박윤상
  • Chanhoe Kim 김찬회
  • Yunwoo Lee 이윤우
  • Yunseo Yeo 여윤서

Alumni 졸업생

  • Juhyun Yeom (Initial Placement: Graduate Student, Yonsei University) 염주현 (첫 소속기관: 연세대학교 대학원)
  • Jiho Han (Initial Placement: Hanwha Robotics) 한지호 (첫 소속기관: 한화 로보틱스)

Professor

Hyun-Myung Woo, Ph.D.

Assistant Professor
Department of Biomedical & Robotics Engineering
Incheon National University

우현명

조교수
바이오-로봇 시스템 공학과
인천대학교

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

학력

  • 전기 및 컴퓨터 공학 박사 (Ph.D.)
    Texas A&M University, 2022
  • 컴퓨터 과학 석사 (M.S.)
    연세대학교, 2010
  • 공학 학사 (수석 졸업)
    연세대학교, 2008

Selected Experience

  • Assistant Professor
    Incheon National University, 2023 - Present
  • Research Associate
    Brookhaven National Laboratory, 2022 - 2023
  • Senior Researcher
    LG Electronics, 2016 - 2017
  • Embedded Software Engineer
    IDIS, 2010 - 2013

주요 경력

  • 조교수
    인천대학교, 2023 - 현재
  • 연구원
    미국 브룩헤이븐 국립 연구소, 2022 - 2023
  • 주임연구원
    LG 전자, 2016 - 2017
  • 임베디드 소프트웨어 엔지니어
    아이디스, 2010 - 2013

Honors & Awards

  • Outstanding Paper
    KIIS 2025 Fall Conference, 2025
  • Academic Research Award
    Incheon National University, 2024
  • Excellence in Teaching Award
    Incheon National University, 2024
  • Best Paper Award
    KIIECT Fall Conference, 2009
  • The Grand Prize
    IDIS & ETNEWS Paper Contest, 2009
  • Summa Cum Laude
    Yonsei University, 2008
  • Academic Excellence Scholarship
    Yonsei University, 2005 - 2007

수상 경력

  • 우수 논문상
    KIIS 2025 추계 학술대회, 2025
  • 학술 연구상
    인천대학교, 2024
  • 우수 강의상
    인천대학교, 2024
  • 우수 논문상
    KIIECT 추계 학술대회, 2009
  • 최우수상
    아이디스(IDIS) & 전자신문 논문 공모전, 2009
  • 최우등 졸업 (Summa Cum Laude)
    연세대학교, 2008

Invited Talks

  • Accelerating Bayesian Optimal Experimental Design using Deep Learning
    Inha University, Apr 2025
  • Designing and Optimizing High-Throughput Virtual Screening Pipelines
    KIIS Spring Conference (Special Session), Apr 2025
  • Optimal Computational Screening Campaign
    Yonsei University, Oct 2024
  • Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
    Yonsei University, Oct 2023
  • Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
    Sungkyunkwan University, Apr 2023
  • Optimal Decision Making in High-Throughput Virtual Screening Pipelines
    BioSeminar @ Texas A&M University, Apr 2022
  • Optimal Decision Making for Accelerating Scientific Discovery
    CSI Seminar @ Brookhaven National Laboratory, Mar 2022
  • Machine Learning Enabled Adaptive Wireless Power Transmission System
    BioSeminar @ Texas A&M University, Oct 2020
  • Accurate Biological Network Alignment through Iterative Optimal Mapping
    BioSeminar @ Texas A&M University, Oct 2019

초청 강연

  • Accelerating Bayesian Optimal Experimental Design using Deep Learning
    인하대학교, 2025년 4월
  • Designing and Optimizing High-Throughput Virtual Screening Pipelines
    한국지능시스템학회(KIIS) 춘계 학술대회 특별세션, 2025년 4월
  • Optimal Computational Screening Campaign
    연세대학교, 2024년 10월
  • Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
    연세대학교, 2023년 10월
  • Accelerating Objective-Driven Optimal Experimental Design using Machine Learning
    성균관대학교, 2023년 4월
  • Optimal Decision Making in High-Throughput Virtual Screening Pipelines
    텍사스 A&M 대학교 (BioSeminar), 2022년 4월
  • Optimal Decision Making for Accelerating Scientific Discovery
    브룩헤이븐 국립 연구소 (CSI Seminar), 2022년 3월
  • Machine Learning Enabled Adaptive Wireless Power Transmission System
    텍사스 A&M 대학교 (BioSeminar), 2020년 10월
  • Accurate Biological Network Alignment through Iterative Optimal Mapping
    텍사스 A&M 대학교 (BioSeminar), 2019년 10월

Teaching

  • Deep Learning (4-1)
  • Image Signal Processing (4-2)
  • Digital Signal Processing (3-2)
  • Probability and Statistics (3-1)
  • Electromagnetics (2-2)
  • Calculus I (1-1)

강의

  • 딥러닝 (4-1)
  • 영상신호처리 (4-2)
  • 디지털신호처리 (3-2)
  • 확률과 통계 (3-1)
  • 전자기학 (2-2)
  • 대학수학 I (1-1)

Publications

18 Journal Publications 저널 논문  •  11 Conference Papers 학회 논문  •  2 Under Review 심사 중

Journal Publications 저널 논문

  • [18] Adaptive Sequence-to-Sequence Learning for Long-Term PMSM Temperature Prediction
    Jun Young Lee, Hyun-Myung Woo, Hyundoo Jeong
    AIP Advances 16, no. 4, 2026
  • [17] 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.
    Journal of Advances in Modeling Earth Systems, 2026
  • [16] Integrating Patient-Derived Organoids and Personalized Immunotherapy for Precision Pancreatic Cancer Management
    Wonmin Lee¶, Hyun-Myung Woo¶, Ja Hyang Cho and Man S. Kim
    Fronteirs in Oncology, 2025
  • [15] PixelCut: A Unified Solution for Zero-Configuration 16S rRNA Trimming via Computer Vision
    Doingin Kim, Woo Jin Kim, Hyun-Myung Woo¶, Hyundoo Jeong¶
    Current Issues in Molecular Biology, 2025
  • [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] 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] 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] 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] 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] 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

Conference Papers 학술대회 논문

  • [11] Enabling machine learning-assisted discovery of polyamines for solid-state CO₂ capture
    A N M Nafiz Abeer, Junhe Chen, Alif Bin Abdul Qayyum, Zhihao Feng, Hyun-Myung Woo, Seung Soon Jang, and Byung-Jun Yoon
    NeurIPS 2025 Workshop on Tackling Climate Change with Machine Learning, 2025
  • [10] Accelerated discovery of high-performance polyamines for solid-state direct CO₂ capture via efficient simulations and Bayesian optimization
    Junhe Chen, A N M Nafiz Abeer, Alif Bin Abdul Qayyum, Zhihao Feng, Hyun-Myung Woo, Byung-Jun Yoon, and Seung Soon Jang
    NeurIPS 2025 Workshop on AI for Accelerated Materials Design (AI4Mat), 2025
  • [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

Overview

The Computational Science Laboratory (CSL) investigates foundational AI algorithms and their application to complex physical domains. Under the core framework of AI4X (AI for Scientific Discovery, Optimization, Robotics, and Healthcare), our research focuses on autonomous optimal decision-making, biomolecular structure analysis, and the design of intelligent architectures for cyber-physical and digital healthcare systems.
계산과학연구실(Computational Science Laboratory, CSL)은 인공지능과 복잡계 물리 도메인의 융합을 통한 핵심 알고리즘 및 응용 시스템을 연구합니다. AI4X (AI for Scientific Discovery, Optimization, Robotics, and Healthcare) 방법론을 기반으로 최적 의사결정 추론, 분자 및 생물학적 구조 해석, 그리고 사이버-물리 시스템 및 디지털 헬스케어를 아우르는 지능형 통합 아키텍처를 설계합니다.

Research Areas

1. AI for Science & Engineering
Molecular Design, Bioinformatics & Material Discovery

Developing domain-specific artificial intelligence methodologies to address complex challenges in physics, chemistry, and biology.

  • Drug Discovery & Design
    Constructing generative AI and Graph Neural Network (GNN)-based in-silico pipelines for de novo compound design, target protein binding affinity prediction, drug repositioning, and early-stage toxicity screening.
  • Carbon Capture Material Design
    Utilizing physics-informed deep surrogate models to navigate thermodynamic state spaces, aiming to discover novel metal-organic frameworks (MOFs) and porous polymers with optimized carbon dioxide adsorption and selectivity.
  • Bioinformatics & Structural Biology
    Developing network alignment algorithms and secondary structure prediction models for RNA and proteins to elucidate the interaction mechanisms of biological complex systems.
  • Metamaterial Design via Physics-Informed ML
    Employing Physics-Informed Neural Networks (PINNs) with deep Lorentz layers to bypass traditional FDTD/FEM numerical bottlenecks, enabling the inverse design of functional metamaterials constrained by physical causality.

1. AI for Science & Engineering
분자 설계 및 과학적 발견

물리, 화학, 생물학 도메인의 복잡한 난제를 해결하기 위한 도메인 특화 인공지능 방법론 연구.

  • 신약 개발 및 설계 (Drug Discovery & Design)
    생성형 AI 및 그래프 신경망(GNN) 기반의 인실리코(In-silico) 파이프라인 구축. 신규 화합물 설계(De novo design), 표적 단백질 결합 친화도 예측, 신약 재창출(Repositioning) 및 독성(Toxicity) 스크리닝을 수행합니다.
  • 탄소 포집 소재 설계 (Carbon Capture Material Design)
    물리 지식 기반 딥러닝 대리 모델(Surrogate Model)을 활용한 열역학적 소재 설계 공간 탐색. 이산화탄소 포집 효율과 선택성이 최적화된 다공성 고분자 및 금속-유기 골격체(MOF) 발굴 방법론을 연구합니다.
  • 생물정보학 및 구조 생물학 (Bioinformatics & Structural Biology)
    생물학적 복잡계의 상호작용 메커니즘을 규명하기 위한 네트워크 정렬(Network alignment) 알고리즘 및 RNA/단백질 2차 구조 예측(Secondary structure prediction) 모델링을 수행합니다.
  • 물리 정보 기반 메타물질 설계 (PINN for Metamaterials)
    물리 정보 신경망(PINN)과 심층 로렌츠 계층(Deep Lorentz layers)을 활용하여 수치해석(FEM, FDTD) 병목을 해소하고, 물리 법칙이 반영된 기능성 메타물질 역설계(Inverse design)를 수행합니다.

2. AI for Methodology & Optimization
Foundational Inference and Optimization

Designing foundational Bayesian inference and optimization algorithms to ensure the reliability and performance of applied domains.

  • HTVS Pipeline & Policy Optimization
    Integrating Reinforcement Learning (RL) into High-Throughput Virtual Screening (HTVS) pipelines to formulate optimal search policies that minimize the computational cost of chemical space exploration.
  • UQ, OED & Bayesian Optimization
    Combining Bayesian Optimization (BO) with Uncertainty Quantification (UQ) to evaluate predictive confidence, maximizing information gain and establishing optimal experimental designs (OED) for efficient search loops.

2. AI for Methodology & Optimization
추론 및 최적화 방법론

응용 도메인의 성능과 신뢰성을 확보하기 위한 베이지안 추론 및 최적화 기반 알고리즘 설계.

  • HTVS 파이프라인 및 정책 최적화 (Policy Optimization)
    고효율 가상 스크리닝(HTVS) 파이프라인에 강화학습(RL)을 통합하여, 방대한 화학 공간(Chemical space) 탐색 비용을 최소화하는 최적 탐색 정책(Policy) 모델링.
  • 불확실성 정량화 및 베이지안 최적화 (UQ, OED & BO)
    베이지안 최적화(BO)와 불확실성 정량화(UQ)를 결합한 예측 신뢰도 평가 모델링. 정보 획득량을 극대화하는 최적 실험계획(OED)을 통해 모델 탐색 루프의 효율성을 향상시킵니다.

3. AI for Healthcare & Medical Systems
Clinical Diagnosis and Digital Healthcare

Fusing heterogeneous clinical data to develop data-driven healthcare systems that improve the accuracy of disease diagnosis and prognosis prediction.

  • Multimodal Medical Imaging Analysis
    Fusing and registering multi-modal medical images (e.g., MRI, CT) using high-resolution vision modeling to extract clinical biomarkers and detect micro-lesions.
  • Digital Phenotyping for Developmental Disorders
    Applying deep-learning-based time-series analysis to audio and video data to track infant micro-behavioral and speech patterns, computing auxiliary diagnostic indices for developmental disorders.
  • WiFi Sensing & Healthcare Semantic Captioning
    Analyzing ambient Radio Frequency (RF) signals and vision data to perform non-contact monitoring of human activities and physiological signals (e.g., respiration, sleep apnea). By translating these inputs into contextual semantic language, we develop privacy-preserving, intelligent healthcare monitoring systems.

3. AI for Healthcare & Medical Systems
임상 진단 및 디지털 헬스케어

이질적인 임상 데이터를 융합하여 질병 진단 및 예후 예측 모델의 정확도를 향상시키는 데이터 기반 헬스케어 시스템.

  • 멀티모달 메디컬 이미징 분석 (Multimodal Medical Imaging Analysis)
    MRI, CT 등 다중 모달리티 의료 영상의 융합 및 정합(Registration)을 통한 미세 병변 검출 및 고해상도 임상 바이오마커 추출 비전 모델링.
  • 발달 장애를 위한 디지털 표현형 (Digital Phenotyping)
    음성 및 영상 시계열 데이터의 딥러닝 분석을 통해 영유아의 미세 행동 및 발화 패턴을 추적하고, 일상 환경 내 발달 장애 조기 진단 보조 지표를 산출하는 기술.
  • 와이파이 센싱 및 헬스케어 비전 캡셔닝 (WiFi Sensing & Healthcare Captioning)
    무선 주파수(RF) 신호와 비전 데이터를 융합 분석하여 환자의 생체 활동(호흡, 수면 무호흡 등)과 일상 행동을 비접촉 방식으로 모니터링합니다. 수집된 다중 모달 정보를 시맨틱 언어로 변환하는 캡셔닝 모델링을 통해 프라이버시를 보호하는 지능형 디지털 헬스케어 시스템을 구현합니다.

4. AI for Robotics & Cyber-Physical Systems
Physical Systems and Interaction Control

Cognitive modeling and decoding of multi-modal signals generated during physical robot operations and human-robot interactions.

  • Telepresence & Sensory Decoding
    Decoding haptic and kinematic signals from remote robot control within a latent space to provide seamless physical sensory feedback to operators.
  • Vision for Robotics
    Enhancing the physical spatial awareness of robots through advanced vision recognition algorithms, enabling robust object detection and environmental navigation.
  • HRI & BCI Deep Learning Models
    Developing meta-learning architectures and lightweight CNNs for the real-time classification of non-linear biological signals (e.g., EEG), enabling rapid personalization for robust HRI and BCI.

4. AI for Robotics & Cyber-Physical Systems
물리 시스템 및 상호작용 제어

물리적 환경 내 로봇 및 인간-로봇 상호작용 과정에서 발생하는 다중 모달 신호의 인지 모델링 및 디코딩 기술.

  • 원격 조작 제어 및 감각 디코딩 (Telepresence & Sensory Decoding)
    원격 로봇 제어 시 발생하는 햅틱 및 운동학적 신호를 잠재 공간(Latent space)에서 디코딩하여, 작업자에게 정밀한 물리적 감각 피드백을 전달하는 딥러닝 알고리즘.
  • 로보틱스 비전 (Vision for Robotics)
    로봇의 물리적 공간 인지 능력을 향상시키기 위한 고도화된 비전 인식 알고리즘 연구를 통해, 정밀한 객체 탐지 및 주행 환경 인지 모델을 개발합니다.
  • HRI 및 BCI 딥러닝 모델 (생체 신호 기반 메타러닝)
    비선형적이고 노이즈가 강한 생체 신호(EEG 등)의 실시간 의도 분류를 위한 메타러닝 및 경량화 CNN 아키텍처 기반 초고속 개인화(Personalization) 기술.

Research Grants

Current Projects

  • Deep Learning-Driven Multi-Objective Uncertainty Quantification for Accelerated Optimal Experimental Design
    Internal Research Grant, Incheon National University, 2025 - Present

Completed Projects

  • Development of Oral Wearable/Implantable Neural Stimulator for Sleep Apnea Rehabilitation Treatment
    Alchemist Project, Korea Institute for Advancement of Technology (KIAT), 2025 - 2025
  • Reinforcement Learning-based Optimal Decision Making in High-Throughput Virtual Screening (HTVS) Pipelines
    First Research, The National Research Foundation of Korea (NRF), 2023 - 2026
  • Knowledge Discovery using Machine Learning for the Objective-based Optimal Experimental Design
    Internal Research Grant, Incheon National University, 2024 - 2025
  • Accelerating the Objective-based Optimal Experimental Design using Machine Learning
    Internal Research Grant, Incheon National University, 2023 - 2024

수행 중인 과제 (Current Projects)

  • 가속화된 최적 실험 설계를 위한 딥러닝 기반 다목적 불확실성 정량화
    교내연구, 인천대학교, 2025 - 현재

완료된 과제 (Completed Projects)

  • 수면 무호흡 재활 치료를 위한 구강 착용/이식형 신경 자극기 개발
    알키미스트 프로젝트, 한국산업기술진흥원(KIAT), 2025 - 2025
  • 고효율 가상 탐색(HTVS) 파이프라인에서의 강화학습 기반 최적 의사 결정
    생애첫연구, 한국연구재단(NRF), 2023 - 2026
  • 목적 기반 최적 실험 설계를 위한 머신러닝 활용 지식 발견
    교내연구, 인천대학교, 2024 - 2025
  • 머신러닝을 이용한 목적 기반 최적 실험 설계 가속화
    교내연구, 인천대학교, 2023 - 2024