Safwan Wshah

Associate Professor, Department of Computer Science

Safwan.Wshah@uvm.edu
(802) 656-8086
Associate Professor Safwan Wshah
Safwan.Wshah@uvm.edu
+18026568086

Innovation E417
82 University Place
Burlington, VT 05405
United States
Vermont Artificial Intelligence Laboratory (VaiL)
Alma mater(s)
  • Ph.D., Computer Science and Engineering - SUNY Buffalo
Affiliated Department(s)

Department of. Computer Science

BIO

Wshah joins UVM CEMS from PARC (Palo Alto Research Center)- A Xerox company, where he worked as a research scientist in the fields of machine learning/deep learning, computer vision, and image/ video processing. Dr. Wshah holds a Ph.D. in Computer Science and Engineering from SUNY-Buffalo. He received nineteen issued U.S. patents and has authored 25 conference and journal publications. He is currently investigating machine learning algorithms to be applied in Energy, Transportation and Healthcare fields.

Courses

  • CS 3540 - Machine Learning
  • CS 6990 - Deep Learning

Area(s) of expertise

Machine Learning, Image & Video Processing, Deep Learning, Pattern Recognition, Computer Vision, Document Imaging & Digital Signal Processing.

Bio

Wshah joins UVM CEMS from PARC (Palo Alto Research Center)- A Xerox company, where he worked as a research scientist in the fields of machine learning/deep learning, computer vision, and image/ video processing. Dr. Wshah holds a Ph.D. in Computer Science and Engineering from SUNY-Buffalo. He received nineteen issued U.S. patents and has authored 25 conference and journal publications. He is currently investigating machine learning algorithms to be applied in Energy, Transportation and Healthcare fields.

Courses

  • CS 3540 - Machine Learning
  • CS 6990 - Deep Learning

Areas of Expertise

Machine Learning, Image & Video Processing, Deep Learning, Pattern Recognition, Computer Vision, Document Imaging & Digital Signal Processing.

Publications

Selected Publications:

  • S. Wshah, N. Chaube, C. Skalka and M. Price, Machine Learning Methods for
    Post Traumatic Stress Disorder Patient Prediction, to be Submitted to Journal
    of Biomedical and Health Informatics. In press, 2019.
  • S. Hahn, J. Allison, R. Watts, S. Wshah, Automatic Deep Learning Based
    Cervical Spinal Fracture Diagnosis, submitted to IEEE International Symposium
    on Biomedical Imaging (ISBI), 2019.
  • S. Hamshaw, D. Denu, M. Holthuijzen, S. Wshah, and D. Rizzo, Automating the
    Classification of Hysteresis in Event Concentration-Discharge Relationships,
    poster submitted to SedHyd conference, 2019.
  • Applying Deep Learning to Event Concentration-Discharge Hysteresis Patterns
    to Reveal Differences in Sediment Dynamics across Contrasting Watersheds, SD
    Hamshaw, D Denu, MM Dewoolkar, M Holthuijzen, S Wshah, D Rizzo, AGU Fall
    Meeting, 2018.
  • L. Bonnell, B. Littenberg, S. Wshah, G. Rose, Automated identification of
    unhealthy drinking using routinely collected data: A machine learning
    approach, accepted poster to APHA 2018.