2022–present
Assistant Professor at Singapore Management University
Singapore
Min Hun Lee
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Faculty at Singapore Management University
Research Area
To enhance people's ability to understand and communicate large and complex data, I develop methods that richly integrate the capabilities of both people and machines. I combine methods from visualization, data management, programming languages, and human-computer interaction to enable effective methods for data analysis and communication. My systems have won awards at premier academic venues, and are used by journalists, data enthusiasts, developers, educators, and the data science community.
Positions
2015–2021
Research Assistant at Carnegie Mellon University
Pittsburgh, PA
2017
Research Intern at IBM Research
Cambridge, MA
Education
2015–2021
Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University
Pittsburgh, PA
Advised by Daniel P. Siewiorek
Thesis: “Interactive Hybrid Intelligence Systems for Human-AI/Robot Collaboration”.
2018–2020
M.S. in Machine Learning, Carnegie Mellon University
Pittsburgh, PA
2008–2012
B.S. in Electrical and Computer Engineering, Carnegie Mellon University
Pittsburgh, PA
Notable Awards
2021
Rising Stars in Data Science
The University of Chicago
For my thesis on "Interactive Hybrid Intelligence Systems".
2019
2nd Place
ACM ASSETS Student Research Competition
2019
Integrated Design Award
IJCAI Eldercare Robot Challenge
Publications [Interactive Version]
Conference
C12
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
Min Hun Lee, Chong Jun Chew.
ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW), 2023.
C11
Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM International Conference on Intelligent User Interfaces (IUI), 2022.
C10
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu.
ACM FAccT Conference on Fairness, Accountability, and Transparency (FAccT), 2022.
C9
A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM Conference on Human Factors in Computing Systems (CHI), 2021.
C8
An Exploratory Study on Techniques for Quantitative Assessment of Stroke Rehabilitation Exercises
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM Conference on User Modeling, Adaptation and Personalization (UMAP), 2020.
C7
Interactive Hybrid Approach to Combine Machine and Human Intelligence for Personalized Rehabilitation Assessment
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM Conference on Health, Inference, and Learning (CHIL), 2020.
C6
Towards Personalized Interaction and Corrective Feedback of a Socially Assistive Robot for Post-Stroke Rehabilitation Therapy
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2020.
C5
Intelligent Agent for Assessing and Guiding Rehabilitation Exercises
Min Hun Lee.
International Joint Conference on Artificial Intelligence (IJCAI Doctoral Consortium), 2019.
C4
An Intelligent Decision Support System for Stroke Rehabilitation Assessment
Min Hun Lee.
ACM SIGACCESS Conference on Computers and Accessibility (ASSETS Student Research Competition), 2019.
C3
Learning to Assess the Quality of Stroke Rehabilitation Exercises
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM International Conference on Intelligent User Interfaces (IUI), 2019.
C2
A Technology for Computer-Assisted Stroke Rehabilitation
Min Hun Lee.
ACM International Conference on Intelligent User Interfaces (IUI Student Consortium), 2018.
C1
An Approach for Self-Training Audio Event Detectors using Web Data
Benjamin Elizalde*, Ankit Shah*, Siddharth Dalmia*, Min Hun Lee*, Rohan Badlani, Anurag Kumar, Bhiksha Raj, Ian Lane.
European Signal Processing Conference (EUSIPCO), 2017.
Journal
J4
Design, Development, and Evaluation of an Interactive Personalized Robot to Monitor and Coach Rehabilitation Exercises
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
User Modeling and User-Adapted Interaction, 2023.
J3
A low-cost virtual coach for 2D video-based compensation assessment of upper extremity rehabilitation exercises
Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino.
Journal of NeuroEngineering and Rehabilitation, 2022.
J2
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
International Journal of Social Robotics, 2022.
J1
Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia.
ACM on Human-Computer Interaction. 4 (CSCW2):156, 2020.
Book Chapter
Demo
Workshop
W1
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises
Min Hun Lee, Yi Jing Choy.
International Conference on Learning Representations (ICLR) Time Series Representation Learning for Health, 2023.
Thesis
T1
Interactive Hybrid Intelligence Systems for Human-AI/Robot Collaboration
Min Hun Lee.
PhD thesis.
2021.
Patents
Mentoring
2019–2021
AI Mentoring Program
Carnegie Mellon UniversityI mentored undergraduate students for over a year on finding classes, research projects, collaborators, and advisors, as well as managing conferences.
Mentored:
- Jingwei Wang
2015–2021
Mentor Undergraduate Researchers
Carnegie Mellon UniversityMentor students
Mentored:
- Christine Fang
- Teddy
Talks [Interactive Version]
Interactive Hybrid Intelligence Systems for Improving the Practices of Physical Stroke Rehabilitation
Pitt-CMU Medical Informatics Colloquium, Zoom
Panel Discussion
IJCAI 2021 AI4 Function, Online
Teaching
2022
Introduction to Artificial Intelligence
Singapore Management University
Instructor,
75 students
Prepared and taught lectures. Prepared and graded assignments.
Service
Reviewer for ACM CHI, IEEE VIS, TVCG, EuroVis, PacificVis, Computers & Graphics, HILDA, and DSIA. Program committee for HILDA 2018, HILDA 2019, and HILDA 2020 at SIGMOD. Organizer of DSIA 2018 and DSIA 2019 at VIS. Session chair at CHI 2019, VIS 2019, VIS 2020. Program committee at VIS 2020 and 2021.
References
Daniel P. Siewiorek
Professor of Electrical and Computer Engineering and Computer Science
Carnegie Mellon University
http://www.cs.cmu.edu/~dps/