David Wanik

Assistant Professor in-Residence

Operations and Information Management


Education

  • PhD –University of Connecticut – Environmental Engineering
  • MS – University of Connecticut – Environmental Engineering
  • BS – University of Connecticut – Environmental Science

Areas of Expertise

  • Data science, natural hazards, remote sensing, IoT

Courses

  • OPIM 5603: Statistics in Business Analytics
  • OPIM 5641: Business Decision Modeling
  • OPIM 5512: Data Science Using Python
  • OPIM 5509: Introduction to Deep Learning

Biography

Dr. Wanik is an Assistant Professor In-Residence at the University of Connecticut – Department of Operations and Information Management. His research interest is at the intersection of natural hazards, business analytics and remote sensing. He primarily teaches for the MS Business Analytics and Project Management program and is the Academic Director for Business Data Analytics at the Stamford campus. He is also appointed as a Research Fellow at the Mississippi State University Social Science Research Center, where he leads research on global coastal population dynamics and remote sensing topics. He has previous industry experience working with utility, manufacturing and insurance companies.

Recent Publications

  1. Walsh, T., Wanik D.W., Anagnostou E.N., Mellor J., 2020: “Estimated Time to Restoration of Hurricane Sandy in a Future Climate”. Sustainability 2020, 12(16), 6502. https://doi.org/10.3390/su12166502
  2. Watson P., Cerrai D., Wanik D. W., Anagnostou E. N., 2020: “A Weather-Related Power Outage Model with a Growing Domain: Structure, Performance, and Generalizability”, The Journal of Engineering. Accepted – May 28, 2020.
  3. Alpay B. A., Wanik D. W., Watson P., Liang G., Anagnostou E. N., 2020: “Dynamic Modeling of Power Outages Caused by Thunderstorms”, Forecasting, 2(2), 151-162; https://doi.org/10.3390/forecast2020008
  4. Yang F., Wanik D. W., Cerrai D., Bhuiyan M. A. E., Anagnostou E., 2020: “Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration”, Sustainability 12 (4), 1525. https://doi.org/10.3390/su12041525.
  5. Cerrai D., Wanik D. W., A.E. Bhuiyan, Zhang X., Yang J., Frediani M., Anagnostou E. N., 2019: “The Predictability of Power Outages from a New Representation of Weather and Vegetation Impacts in Non-Parametric Modeling”, IEEE Access. DOI: 10.1109/ACCESS.2019.2902558.
Contact Information
Emaildave.wanik@uconn.edu
Mailing AddressOne University Place, Stamford, CT 06901
Office Location3.87
CampusStamford
Linkhttps://scholar.google.com/citations?user=xyW8xncAAAAJ&hl=en
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