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 forecasting 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 School of Systems and Enterprises. Prior to joining SSE, 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.

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
2000-2005 UX Designer/Programmer/Data analyst, SK Corp.

2019.8-current Assistant Professor, Director, Human-Centered Interaction Lab, School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ

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 Research Award, Human-Centered AI System, Google Tri-State ExploreCSR Research, 2021
Best Paper Selected for the 2019 Edition of the IMIA Yearbook, Section 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 S, Suffoletto B, Zhang T, Chung T, Ozolcer M, Islam R, Dey A (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", JMIR Formative Research, 2023

Lauvsnes A, Hansen T, Ankill S, Bae S, W Gråwe R, Braund T, Larsen M, Langaas M (2023). "Mobile assessments of mood, executive functioning, and sensor-based smartphone activity, explain variability in substance use craving and relapse in patients with clinical substance use disorders – a pilot study", JMIR Formative Research, 2023

Bae S, Suffoletto B, Zhang T, Chung T, Islam M, Dey A. (2022). "Using Mobile Phone Sensors to Predict Same-Day Heavy Drinking Events: A Feasibility Study", JMIR mHealth and uHealth, JMIR Publications Inc (Preprint).

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 (June 28, 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. (May 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. (May 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. (April 23, 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 (Sept 3, 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 S, Suffoletto B, Du J, Jang S, Nishiyama Y, Jang S, Dey A (2020). "Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study.” JMIR Mhealth Uhealth. 2020; 8(3):e16240. Open access PMCID: PMC7093776

Bae S, Chung T, Ferreira D, Dey AK, Suffoletto B. (Aug 1, 2018). "Mobile Phone Sensors and Supervised Machine Learning to Identify Alcohol Use Events in Young Adults: Implications for Just-In-Time Adaptive Interventions", Rachel L. Tomko, Erin A. McClure, Addictive behaviors, Elsevier. 83 42-47.

Bae S, Ferreira D, Suffoletto B, Puyana J, Kurtz R, Chung T, Dey A. (Jun 30, 2017). "Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, ACM. 1 (2), 1-36.

CA Low, AK Dey, D Ferreira, T Kamarck, W Sun, S Bae, A Doryab. (Dec 19, 2017). "Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study", G Eysenbach, Journal of Medical Internet Research., JMIR Publications Inc. 19 (12)

Bae S, Dey A, Low C. (Sep 12, 2016). "Using Passively Collected Sedentary Behavior to Predict Hospital Readmission", Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM. 616-621.

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