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 3510: Business Data Analytics I
• OPIM 3802: Data and Text Analytics
• OPIM 5603: Statistics in Business Analytics
• OPIM 5641: Business Decision Modeling
• OPIM 5502: Big Data Analytics with Cloud Computing
• OPIM 5512: Data Science Using Python
• OPIM 5509: Introduction to Deep Learning

Biography

Dr. Wanik is an Assistant Professor In-Residence in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. His research interest is at the intersection of natural hazards, insurance, business analytics and remote sensing. He primarily teaches for the MS Business Analytics and Project Management program, is the Academic Director for Business Data Analytics at the Stamford campus, and conducts research in the Eversource Energy Center. He has previous industry experience working with utility, manufacturing and insurance companies.

Recent Publications

1. Lebakula V., Datla V., Wanik D. W., Cosby A. G., 2024: ‘Predicting County-Level Population from VIIRS Nighttime Light Imagery with Deep Learning’, IEEE Sensors Journal, Accepted.
2. Taylor, W., Cerrai, D., Wanik D., Koukoula, M., Anagnostou, E., 2023: ‘Community Power Outage Prediction Modeling for the Eastern United States’, Energy Reports, Accepted.
3. Hughes W., Zhang W., Cerrai D., Bagtzoglou A. C., Wanik D. W., Anagnostou E. N., 2022: “A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation”, Reliability Engineering & System Safety, Volume 225,
4. Chang C. F., Garcia V., Tang C., Vlahos P., Wanik D. W., Yan J., Bash J. O., Astitha M., 2021: “Linking multi-media modeling with machine learning to assess and predict lake chlorophyll-a concentrations”, Journal of Great Lakes Research, Volume 47, Issue 6, 2021, Pages 1656-1670, ISSN 0380-1330, https://doi.org/10.1016/j.jglr.2021.09.011.
5. Hughes W., Zhang , Bagtzoglou A. C., Wanik, D. W., Pensado, O., Yuan, H., Zhang J, 2021: “Resilience Hardening Strategy and Damage Modeling Framework for Overhead Power Distribution Systems”, Reliability Engineering and System Safety. https://doi.org/10.1016/j.ress.2020.107367