Sang Won Bae

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Assistant Professor

Stevens Institute of Technology

United States

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About

Sang Won Bae is an Assistant Professor at Stevens Institute of Technology in the United States. His research areas include voice-based conversational agents, mobile assessments of mood and cognition, and the application of machine learning in understanding behavioral health. Recent articles authored by Professor Bae explore topics such as the detection of acute marijuana intoxication, depression detection using mobile sensing systems, and predicting binge-drinking events through mobile phone sensors.

Recent Grants

Grant: Close

Smartphone sensors to detect shifts toward healthy behavior during alcohol treatment

Open Date: 2022-09-01

Close Date: 2023-08-01

Grant: Close

Real-time prediction of marijuana use & effects of use on cognition in the natural environment

Open Date: 2017-04-01

Close Date: 2019-02-28

Articles (8)

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2020

Collaborators (4)

Anind K. Dey

Dean and Professor

University of Washington

UNITED STATES

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Taylor A. Braund

Postdoctoral Fellow

Black Dog Institute

AUSTRALIA

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Runze Yan

Assistant Professor

Emory University

UNITED STATES

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Mette Langaas

Professor

Norwegian University of Science and Technology

NORWAY

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Sang Won Bae

Research Interests

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About

Recent Grants

Articles (8)

An Exploratory Study on the Impacts of Voice-Based Conversational Agents with Proactive Interactions in the Driving Context

FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic Settings

Mobile Assessments of Mood, Cognition, Smartphone-Based Sensor Activity, and Variability in Craving and Substance Use in Patients With Substance Use Disorders in Norway: Prospective Observational Feasibility Study

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

Enhancing Interpretable, Transparent and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions (Preprint)

Exploratory machine learning modeling of adaptive and maladaptive personality traits from passively sensed behavior

Mobile phone sensor-based detection of subjective cannabis intoxication in young adults: A feasibility study in real-world settings

Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study

Collaborators (4)

Anind K. Dey

Taylor A. Braund

Runze Yan

Mette Langaas

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