Dr. Eui Min Jeong of BIMAG has been appointed as an Assistant Professor in the Department of Data Science at Inha University, starting in the second semester of 2025. Congratulations!
BIMAG 소속 정의민 박사가 2025년 2학기부터 인하대학교 데이터사이언스학과 신임교원으로 임용되었습니다. 축하합니다!
On June 27th , 2025, Prof. U Jin Choi from KAIST gave a talk at the IBS Biomedical Mathematics Seminar. The title of his talk was “Simulation-Free Schrodinger Bridges Via Score and Flow Matching (by Tong et al, AISTATS 2024).”
On June 10th-13th, Dr. Shingo Gibo gave a poster presentation at the 2025 Hibernation Biology 2.0, Annual Meeting, which was held at the Okinawa Institute of Science and Technology (OIST) in Japan.
A recent study by BIMAG researchers has been featured in various Korean media outlets. This study proposes a novel method for efficiently and precisely estimating enzyme inhibition constants using only a single inhibitor concentration. The work was co-authored by undergraduate intern Hyeongjun Jang, Dr. Yunmin Song, and corresponding author Prof. Jae Kyoung Kim. The paper, titled “Optimizing enzyme inhibition analysis: precise estimation with a single inhibitor concentration,” was published in Nature Communications in June 2025. [Link to paper]
BIMAG 연구진의 최근 연구가 다양한 한국 언론에 보도되었습니다. 이 연구에서는 효소 저해 상수를 효율적이고 정밀하게 추정할 수 있는 새로운 방법론을 개발했으며, 장형준 학부생 인턴, 송윤민 박사, 책임저자인 김재경 교수가 공동 저자로 참여했습니다. 해당 논문은 2025년 6월 Nature Communications에 “Optimizing enzyme inhibition analysis: precise estimation with a single inhibitor concentration” 라는 제목으로 게재되었습니다. (논문링크)
On May 30th, 2025, Hiroya Nakao from Institute of Science Tokyo gave an online talk at the IBS Biomedical Mathematics Colloquium. The title of his talk was “Koopman operator approach to complex rhythmic systems.”
On May 21st, 2025, Benjamin Lindner from Humboldt University Berlin gave an online talk at the IBS Biomedical Mathematics Colloquium. The title of his talk was “Simplified descriptions of stochastic oscillators.”
We are pleased to announce the selected participants for the BIMAG 2025 Summer Internship. Congratulations to all those who have been selected!
선정된 BIMAG 2025 여름 인턴십 참가자를 발표합니다. 선정되신 모든 분들께 진심으로 축하의 말씀을 전합니다.
Intern
Affiliation
Mentor
Project
Ji Woo Chae
Bachelor of Arts and Sciences, Dartmouth College
Dongju Lim
Quantifying the uncertainty of predicting human circadian phase by using a mathematical model
Heejune So
Dept. of Medicine, University of Galway
Eui Min Jeong
A Machine Learning-Based Development of a Shortened Version of the Pre-Sleep Arousal Scale (PSAS)
Seunghyun Seo
Dept of Computer Science, Johns Hopkins
Dongju Lim
Estimating circadian period from wearable data using a machine learning algorithm.
Dongyeon Yang
School of Transdisciplinary Studies, KAIST
Dongju Lim
Estimating circadian period from wearable data using a machine learning algorithm.
Taiyoon Yeu
Chemical and Biomolecular Engineering, UCLA
Hyeong Jun Jang
Optimizing experimental design for time-dependent inhibition without prior parameter knowledge
Hongji Kim
Biology and HOD, Vanderbilt
Hyeong Jun Jang
Develop standardized range of drug for AUC-based drug screening
Minah Lee
Math, Chungbuk National University
Myna Lim
A Data-Driven Development of a Shortened Version of the Counterfactual Thinking for Negative Events Scale (CTNES)
Dongwoo Won
Dept of Mathematicsl Sciences, KAIST
Kangmin Lee, Gyuyoung Hwang
Developing Transfer Entropy estimation method with Hidden Markov Model, Data-driven inference of phase response curves in oscillator system using physics-informed neural network
Hein Lin Thant
Dept of Bio & Brain engineering, KAIST
Dongju Lim
Addressing bias in mathematical model-based DLMO prediction
Sangho Kil
Dept of Life Science, Seoul National University
Dongju Lim
Revealing the molecular-level mechanism of mood change from circadian phase resetting
Daewon Jeong
Applied mathematics, Pukyong National University
Yun Min Song
Investigating the Impact of Excessive Sleep on Insomnia
Jaehee Seo
Biochemistry, Yonsei University
Kévin SPINICCI
Assessing similarity cell to detect cell-type population in scRNA-seq data
Thuy Trang Nguyen
School of Computing and School of Business and Technology Management, KAIST
Olive Cawiding
Reducing False Positives in Causal Detection via Temporally-Constrained Surrogate Testing
Hyunkyeong Park
Computer Convergence Software, Korea University(Sejong)
Yun Min Song
Optimization of Mathematical Models for Predicting Alertness
Yoon Kim
Pure and Applied Mathematics, Tsinghua University
Yun Min Song
Sleep-Wake Prediction Using Activity Count
Jinyoung Kim
Dept. of Mathematics, POSTECH
Eui Min Jeong
Oscillatory Transcriptional Factor Dynamics Delay Gene Expression Decoding Compared to Sustained Dynamcis and Modulate Cell Fate
Jaehun Jeong
Dept. of Chemistry, Seoul National University
Gyuyoung Hwang
Symmetry in oscillations and its influence on the frequency gap in coupled oscillator systems.
Dasom Lee
Department of Big Data Science, Korea University Sejong Campus
Dongju Lim
Developing the early warning signal detection method based on predictability
BIMAG members participated in the 2025 KSIAM Spring Conference, held at Konkuk University from May 16 to 17, 2025.
BIMAG 멤버들이 2025년 5월 16일부터 17일까지 건국대학교에서 개최된 2025 KSIAM 봄 학술대회에 참여했습니다.
CI Prof. Jae Kyoung Kim, along with Dr. Yun Min Song, PhD student Dongju Lim, master’s student Kangmin Lee, Mina Lim, and Aqsa, and undergraduate researcher Hyeong Jun Jang, presented posters at the conference.
CI 김재경 교수를 비롯해 송윤민 박사, 임동주 박사과정생, 이강민, 임민아, Aqsa 석사과정생, 장형준 학부생 연구원이 포스터 발표를 진행했습니다.
Dr. Hyukpyo Hong, a former member of BIMAG, gave a presentation as a recipient of the Next-Generation Researcher Award.
전 BIMAG 멤버인 홍혁표 박사가 차세대 연구자상 수상강연을 진행했습니다.
Dongju Lim, a PhD student, received the Best Poster Presentation Award. Congratulations!
On May 9, 2025, the research conducted by members of BIMAG was featured in SIAM News. The study, which focused on predicting mood episodes using only sleep and circadian rhythm features, was highlighted in an article titled “Predicting Mood Episodes Based on Sleep and Circadian Rhythm Features.”
2025년 5월 9일, BIMAG 연구진의 연구가 SIAM News에 소개되었습니다. 이 연구는 수면 및 생체 리듬 관련 특성만을 이용해 기분 에피소드를 예측하는 내용을 담고 있으며, “Predicting Mood Episodes Based on Sleep and Circadian Rhythm Features”라는 제목의 기사로 보도되었습니다.