CV

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Education

• Ph.D in Computer Science, University of Massachusetts Amherst, 2028 (expected) — Passed Portfolio with Distinction
• M.S. in Computer Science, University of Massachusetts Amherst, 05/2026
• B.S. in Computer Science, National Tsing Hua University, 06/2022

Experience

• 09/2023 - present: Research assistant, University of Massachusetts Amherst, Advanced Human Health Analytics Lab.
  • Advised by Prof. Sunghoon Ivan Lee
  • Developed a wearable-based digital biomarker for monitoring upper-limb stroke recovery, enabling a 60% reduction in required clinical trial sample size and supporting remote recovery monitoring. Manuscript under review at Science Translational Medicine.
  • Applied ML models integrating baseline clinical and kinematic data to reduce 3-month outcome prediction error by 31–52%, enabling earlier identification of at-risk patients and personalized rehabilitation planning. Manuscript in preparation for PLOS ONE.
  • Engineered a wrist-worn digital biomarker to quantify compensation movements; achieved a 0.67 Spearman’s correlation with compensation scores, providing a validated metric for objective, long-term monitoring. Manuscript in preparation for Neurorehabilitation and Neural Repair.
• 06/2025 - 09/2025: Research Mentor, University of Massachusetts Amherst
  • Supervised undergraduate research on predictive modeling of stroke risk using electronic health records (EHR).
• 09/2020 - 03/2023: Research Assistant, National Tsing Hua University, Human-Centered Machine Intelligence Lab.
  • Advised by Prof. Po-Chih Kuo
  • Demonstrated that deep learning models can infer demographic attributes from chest X-rays, identifying critical fairness risks in clinical AI. Published in The Lancet Digital Health.
  • Proposed a novel image augmentation method that reduced error disparities across demographic groups from 22% to 5% in chest X-ray anomaly detection. Published in eBiomedicine.
  • Developed a multimodal diagnostic model for COPD classification (AUC=0.76) using model-fusion ML techniques to integrate clinical data and chest X-ray. Published in International Journal of Medical Informatics.
• 01/2026 - 06/2026: Teaching Assistant, University of Massachusetts Amherst
  • COMPSCI 160 Object-Oriented Programming: Hosted lab and review sections, graded assignments, labs, and quizzes.
• 09/2021 - 02/2022: Teaching Assistant, National Tsing Hua University
  • Discrete Mathematics: Designed and graded assignments and exams.
  • Introduction to Machine Learning: Designed assignments, labs, and exams.

Awards

• College Student Research Creative Awards, Ministry of Science and Technology (MOST), Taiwan, 06/2023
• Undergraduate Research Fellowship, Ministry of Science and Technology (MOST), Taiwan, 09/2022
• Second Runner-up, SG Healthcare AI Datathon & EXPO, 12/2021

Publications

Wearable-based digital biomarker provides a valid alternative to traditional clinical measures for post-stroke upper-limb motor recovery

Wang, R., Lang, C. E., Stoykov, M. E., Bonato, P., & Lee, S. I. (2025). Wearable-based digital biomarker provides a valid alternative to traditional clinical measures for post-stroke upper-limb motor recovery. medRxiv, 2025-01.

Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images.

Wang, R., Kuo, P. C., Chen, L. C., Seastedt, K. P., Gichoya, J. W., & Celi, L. A. (2024). Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images. EBioMedicine, 102.

Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study

Wang, R., Chen, L. C., Moukheiber, L., Seastedt, K. P., Moukheiber, M., Moukheiber, D., ... & Celi, L. A. (2023). Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study. International Journal of Medical Informatics, 178, 105211.

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L. C., ...Wang, R., ... & Zhang, H. (in alphabetical order) (2022). AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4(6), e406-e414.

Eliminating race-related shortcuts in deep neural networks for chest X-ray analysis

Wang, R., Chen, L.C., Lin, P.C., Wawira, J., Celi, L., & Kuo, P.C. (2022). Eliminating race-related shortcuts in deep neural networks for chest X-ray analysis. AAAI 2022 Workshop: Trustworthy AI for Healthcare.

Service

• Peer reviewer: ICLR 2026 Workshop DATA-FM
• Peer reviewer: EMBC 2026 - Abstracts

Skills

• Programming: C++, Python (NumPy, SciPy, PyTorch, TensorFlow, scikit-learn, Matplotlib), Git
• Research: Machine/Deep Learning, Digital Health, Signal Processing, Statistical Analysis
• Business (NSF I-Corps): Customer discovery, Market analysis, Business model design
• Languages: English (fluent), Mandarin Chinese (native)