Sang Won Bae

Dr. Bae's research is pioneering the advancement of predicting risky health behaviors through cutting-edge applications of ubiquitous computing and machine learning. Her expertise lies in health monitoring and support systems, with a particular emphasis on vulnerable populations. Her overarching research objective is to establish a foundation for a personalized intervention system that empowers at-risk populations. She achieves this by harnessing the potential of mobile sensing and human-centered AI design strategies, all contributing to positive behavior change in health and safety. She has successfully developed a mobile AI system capable of predicting 30-day hospital readmissions for post-surgical cancer patients. This system's capabilities have been extended to encompass estimating symptom severity during chemotherapy, predicting high-risk alcohol consumption, and predicting marijuana intoxication in young adults. Dr. Bae's groundbreaking work has earned her recognition, including the Best Paper Award in Cancer Informatics, published in ACM, and featured in Forbes News.

Sang Won (Grace) Bae is an assistant professor at the Department of Systems and Enterprises. Prior to joining Stevens, she held the position of systems scientist at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University. Dr. Bae's research interests primarily revolve around personalized and contextualized health intervention systems aimed at reducing healthcare costs and empowering individuals in making informed decisions and fostering behavioral changes. She has received grants and awards from several foundations, including the R21 and U01 grants from the National Institutes of Health. She also holds several leadership roles, including Associate Chair for CHI from 2023 to 2026 and Associate Editor for IMWUT from 2023 to 2027.

2017-2019 Systems Scientist, Special Faculty, Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University
2014-2017 Postdoctoral Associate, Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University
2013-2014 Visiting Scholar, Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University
2010-2013 Research Scientist, Yonsei Center for Cognitive Science
2005-2008 UX Designer/Manager/Leader, Asia Pacific Mobile Phone Group, Wireless Product Division, Samsung Electronics Co., Ltd
2000-2005 UX Designer/Programmer/Data Scientist, SK Group

2019.8 - Present Assistant Professor, School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ
2023 - Present Affiliate Faculty, Stevens Institute for Artificial Intelligence (SIAI)
2024 - Present Affiliate Faculty, Semer Center for Healthcare Innovation

2018-2020 Small Business Innovation Research (SBIR) grant, National Institute on Drug Abuse (NIDA)
2018 IBM Watson AI XPRIZE Round III, Selected 10 Milestone Nominees

Best Paper Honorable Mention Award, ACM CHI Conference on Human Factors in Computing Systems, 2024
Best Paper Award Candidate, IEEE International Conference on Activity and Behavior Computing, 2024
SSE Dean’s Research Incentive Award, Stevens Tech, 2024
Selected for the Inaugural Cohort of the NIDA I4SUD Workshop, Johns Hopkins University, 2023
Outstanding Reviewer, Proceedings of the ACM on Interactive, Mobile, Wearables, and Ubiquitous Technologies (IMWUT), 2023
First Place Research Award, Google Research Tri-State ExploreCSR Workshop, 2021
Best Papers Award 2019 Edition, IMIA Yearbook, Cancer Informatics, 2019
IBM Watson AI XPRIZE Round III, AI Healthcare System, Selected 10 Milestone Nominees, 2018
LATTICE Symposium Selected Top 30 Pre-tenure Track Women Scientists in EECS, USA, 2017

National Institutes of Health (NIH) U01 Grant, 2023-2028
National Institutes of Health (NIH) R21 Grant, 2022
National Institute on Drug Abuse (NIDA) R43 Grant, Small Business Innovation Research (SBIR), 2018
National Science Foundation (NSF), Innovation Corps (I-Corp) Site @Carnegie Mellon University Team, 2018
National Institutes of Health (NIH) R21 Grant, 2017

Apparatus and Method for Supporting Multimedia Service in Mobile Terminal

Bae SW, Chung T, Zhang T, Dey AK, Islam R. (2025). Enhancing Interpretable, Transparent, and UnobtrusiveDetection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and ExplainableAI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study. JMIR AI, Volume 4, Article e52270. DOI: 10.2196/52270, PMID: 39746202, PMCID: PMC11739728.

Shin J, Ko H, Bae SW, Kim J. (2025). An Exploratory Study on the Impacts of Voice-Based Conversational Agents with Proactive Interactions in the Driving Context. International Journal of Human–Computer Interaction, Volume41, Issue 6, Pages 3708–3723. DOI: 10.1080/10447318.2024.2441530.

Islam MR, Bae SW (2024). FacePsy: An Open-Source Affective Mobile Sensing System – Analyzing Facial Behaviorand Head Gesture for Depression Detection in Naturalistic Settings. Proceedings of the ACM on Human-Computer Interaction (PACMHCI), Volume 8, Article 260, 32 pages. DOI: 10.1145/3676505

Lee H, Kim A, Bae SW, Lee U. (2024). S-ADL: Exploring Smartphone-based Activities of Daily Living to DetectBlood Alcohol Concentration in a Controlled Environment. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Volume 8, Article 1005, 25 pages, DOI: 10.1145/3613904.3642832

Islam MR, Bae SW (2024). PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild. International Conference on Activity and Behavior Computing, DOI: 10.48550/arXiv.2404.14590

Islam MR, Zhang T, Bisen P, Bae SW (2024). MoodPupilar: Predicting Mood Through Smartphone Detected Pupillary Responses in Naturalistic Settings. IEEE International Conference on Wearable and Implantable Body Sensor Networks.

Islam MR, Bae SW (2024). MoodCam: Mood Sensing Through In-the-Wild Facial Affect Data. 21st IEEE International Conference on Ubiquitous Intelligence and Computing.

Zhang T, Chung T, Dey A, Bae SW. (2024). Exploring Algorithmic Explainability: Generating Explainable AIInsights for Personalized Clinical Decision Support Focused on Cannabis Intoxication in Young Adults.Proceedings of the 2024 International Conference on Activity and Behavior Computing (ABC), 01-15. DOI:10.1109/ABC61795.2024.10652070

Bae SW, Suffoletto B, Zhang T, Chung T, Ozolcer M, Islam MR, Dey AK. (2023). Leveraging Mobile Phone Sensors,Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study. JMIR Formative Research, Volume 7, Article e39862. DOI: 10.2196/39862, PMID: 36809294, PMCID: PMC10196900.

Lauvsnes ADF, Hansen TI, Ankill SØ, Bae SW, Gråwe RW, Braund TA, Larsen M, Langaas M. (2023). MobileAssessments of Mood, Cognition, Smartphone-Based Sensor Activity, and Variability in Craving and SubstanceUse in Patients With Substance Use Disorders in Norway: Prospective Observational Feasibility Study. JMIR Formative Research, Volume 7, Article e45254. DOI: 10.2196/45254, PMID: 37351934, PMCID: PMC10337471.

Yan R, Ringwald WR, Vega J, Kehl M, Bae SW, Dey AK, Low CA, Wright AGC, Doryab A. (2022). Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior. FutureGeneration Computer Systems, Volume 132, Pages 266-281, ISSN 0167-739X. DOI: 10.1016/j.future.2022.02.010

Bae SW, Chung T, Islam R, Suffoletto B, Du J, Jang S, Nishiyama Y, Mulukutla R, Dey A. (2021). Mobile Phone Sensor-Based Detection of Subjective Cannabis Intoxication in Young Adults: A Feasibility Study in Real-World Settings. Drug and Alcohol Dependence, Volume 228, Article 108972, ISSN 0376-8716. DOI:10.1016/j.drugalcdep.2021.108972

Yan R, Ringwald W, Julio Vega, Kehl M, Bae S, Dey A, Low C, Wright A, Doryab A. (2022). Exploratory Machine Learning Modeling of Adaptive and Maladaptive Personality Traits from Passively Sensed Behavior, Future Generation Computer Systems, Elsevier

Bae S, Suffoletto B, Mun E.-Y, Dey A, Ren Y, Chung T (2022). Identifying Links between Drinking Behavior and Travel Pattern to Inform Personalized Digital Alcohol Intervention, Alcoholism-Clinical and Experimental Research, WILEY. 46 51A

Korshakova E, Bae S. (2022). Towards Human-Centric XAI Chatbots in Mental Health for End-User Experience, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Human-Centered Perspectives in Explainable AI, ACM

Roper S, Bae S. (2022). Exploring Students' Flow States Using Facial Behavior Markers in an Online At-Home Learning Environment, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, the Future of Emotion in Human-Computer Interaction, ACM

Demaliaj A, Bae S. (2022). Designing Human-Centered AI Systems, Detecting Emotion and Flow in College Students During the Online Courses, Google Research 2021, Best Research Award

Bae S, Chung T, Islam R, Suffoletto B, Du J, Jang S, Nishiyama Y, Jang S, Mulukutla R, Dey A (2021). Mobile Phone Sensor-Based Detection of Subjective Cannabis Intoxication in Young Adults: A Feasibility Study in Real-World Settings, Drug and Alcohol Dependence, Elsevier.

Chung T, Bae SW, Mun EY, Suffoletto B, Nishiyama Y, Jang S, Dey AK. (2020). Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study. JMIR mHealth and uHealth, Volume 8,Issue 3, Article e16240. DOI: 10.2196/16240, PMID: 32154789, PMCID: PMC7093776

Bae SW, Chung T, Ferreira D, Dey AK, Suffoletto B. (2018). Mobile Phone Sensors and Supervised Machine Learning to Identify Alcohol Use Events in Young Adults: Implications for Just-in-Time Adaptive Interventions. Addictive Behaviors, Volume 83, Pages 42-47, ISSN 0306-4603. DOI: 10.1016/j.addbeh.2017.11.039

Bae SW, Ferreira D, Suffoletto B, Puyana JC, Kurtz R, Chung T, Dey AK. (2017). Detecting Drinking Episodes inYoung Adults Using Smartphone-Based Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 2, Article 5. DOI: 10.1145/3090051, PMID: 35146236, PMCID: PMC8827207

Low CA, Dey AK, Ferreira D, Kamarck T, Sun W, Bae SW, Doryab A. (2017). Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study. Journal of Medical Internet Research, Volume 19,Issue 12, Article e420. DOI: 10.2196/jmir.9046, PMID: 29258977, PMCID: PMC5750420

Bae SW, Dey AK, Low CA. (2016). Using Passively Collected Sedentary Behavior to Predict Hospital Readmission. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’16),Heidelberg, Germany, pp. 616–621. DOI: 10.1145/2971648.2971750

Bae SW, Jang J, Kim J. (2013). Good Samaritans on Social Network Services: Effects of Shared ContextInformation on Social Supports for Strangers. International Journal of Human-Computer Studies, Volume 71, Issue 9, Pages 900-918, ISSN 1071-5819. DOI: 10.1016/j.ijhcs.2013.04.004

Bae, Sangwon, Lee, Haein, Park, Hyejin, Cho, Hanju, Park, Joonah, Kim, Jinwoo. (2012). The Effects of Egocentricand Allocentric Representations on Presence and Perceived Realism: Tested in Stereoscopic 3D Games.Interacting with Computers, Volume 24, Issue 4, Pages 251-264. DOI: 10.1016/j.intcom.2012.04.009.

EM/SYS 624: Informatics for Engineering Management
ISE490: Data Mining and Applied Machine Learning
SYS 515: Systems Engineering Applications to Healthcare
EM 224: Informatics and Software Development
EM 622: Decision Making via Data Analysis Techniques
EM 680: Designing and Managing the Development Enterprise
SYS 800: Special Problems in Systems Engineering