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The Gene and Linda Voiland School of Chemical Engineering and Bioengineering

Faculty & Staff

Xianglong Wang

Xianglong Wang Headshot 2019

Xianglong Wang, Ph.D.
Scholarly Assistant Professor

Computer-Aided Detection and Diagnostics with Machine Learning

Office: 313 Wegner Hall 📞Pending

The Gene and Linda Voiland
School of Chemical Engineering and Bioengineering
1505 Stadium Way, Room 105
P.O. Box 646515
Washington State University
Pullman, WA 99164-6515

Education

    • Ph.D. Biomedical Engineering and Scientific Computing, University of Michigan, Ann Arbor, MI, 2020
    • M.S.E. Biomedical Engineering, University of Michigan, Ann Arbor, MI, 2016
    • B.S.E. Biomedical Engineering, University of Michigan, Ann Arbor, MI, 2015
    • B.S.E. Electrical and Computer Engineering, Shanghai Jiao Tong University, Shanghai, China, 2015

Teaching

Dr. Wang is currently teaching these courses:

  • BIO_ENG 310 Introduction to Transport Processes (Fall 2020)
  • BIO_ENG 410 Bioengineering Capstone Project I (Fall 2020)
  • BIO_ENG 330 Bioinstrumentation (Spring 2021)
  • BIO_ENG 411 Bioengineering Capstone Project II (Spring 2021)
  • CHE 581-003 Data-Driven Methods in Bioengineering (Spring 2021)

Professional Experience

Xianglong Wang joined the Voiland School of Chemical Engineering and Bioengineering in 2020 as a teaching-oriented Scholarly Assistant Professor, after he obtained his Ph.D. degree from University of Michigan—Ann Arbor. He resided in New Orleans from 2017 to 2020 as a visiting scholar of Tulane University. In 2019, he worked at the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) of the U.S. Food and Drug Administration (FDA) as an ORISE Fellow and performed computer-aided detection and diagnostics research. He is an active contributor to the Kubernetes Project and serves as a reviewer for its Python client. He is very enthusiastic to teach at the college level and has served as an teaching assistant for eight courses during his undergraduate and PhD studies.

Research

Dr. Wang’s current research focuses on using machine learning methods to detect lung ultrasound comets, or B-lines. B-lines are line-like artifacts that appear in lung ultrasound images, and are indicators of lung edema. We are currently attempting to use machine learning methods to quantify or segment lung ultrasound comets.  

The research is extremely beneficial to assessing fluid status in end-stage renal disease (ESRD) patients and potentially offers a non-invasive, radiation-free way to accurately quantify the amount of lung edema to guide dialysis.

Dr. Wang is part of the Ultrasound Imaging Research Core at the Ann Arbor VA Healthcare System. Check us out: ➡ AAVA Ultrasound Imaging