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Ming-Chun Huang

Associate Professor of Data and Computation at Duke Kunshan University
DKU Faculty

Overview


Huang has a B.S (2007) in Electrical Engineering at Tsing Hua University, Taiwan, an M.S. (2010) in Electrical Engineering at the University of Southern California, and a Ph.D. (2014) in Computer Science at the University of California, Los Angeles. Prior to joining Duke Kunshan University in 2021, he was an Associate Professor at Case Western Reserve University (2014-2021). His research focus is the intersection among Precision Health and Medicine, Internet-of-Things, Machine Learning and Informatics, Motion and Physiological Signal Sensing. He had over 15 years of research experience conducting interdisciplinary scientific projects with researchers from distinct areas (e.g., Biomedical Engineering, Medicine, and Nursing). He had successfully administered past funded projects and productively published over a hundred peer-reviewed publications, 6 invention patents and software copyrights, and won 7 best paper awards/runner-up, 3000+ citations. His research has been reported in hundreds of high-impact media outlets. For the nature of richness and high impact of the research topics he was involved in, his research results in a plethora of new knowledge in aspects ranging from innovative IoT sensing technology, closed-loop AI analytics methodology, optimized clinical decision-making, and just-in-time patient risk assessment.

Current Appointments & Affiliations


Associate Professor of Data and Computation at Duke Kunshan University · 2021 - Present DKU Faculty

Recent Publications


Feasibility and Acceptability of the Sense2Quit App for Improving Smoking Cessation in PWH.

Journal Article AIDS and behavior · February 2025 An estimated 34-47% of PWH in the US report cigarette smoking, three to four times the prevalence observed in the U.S. adult population. Given the dearth of smoking cessation interventions for PWH, our study team used community based participatory feedback ... Full text Cite

Multimodal speech recognition using EEG and audio signals: A novel approach for enhancing ASR systems

Journal Article Smart Health · June 1, 2024 Speech recognition using EEG signals captured during covert (imagined) speech has garnered substantial interest in Brain–Computer Interface (BCI) research. While the concept holds promise, current implementations must improve performance compared to establ ... Full text Cite

Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System

Journal Article ACM Transactions on Sensor Networks · May 11, 2024 As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the ... Full text Cite
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Education, Training & Certifications


University of California, Los Angeles · 2014 Ph.D.