David Wanik

Associate 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 3510: Business Data Analytics II
• 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
• OPIM 5516: Advanced Deep Learning

Biography

Dr. Wanik is an Associate Professor In-Residence at the University of Connecticut, where he teaches and develops graduate courses in optimization, statistics, cloud computing, data science, and deep learning. His background includes extensive applied research and collaborations with industry partners, bringing real operational, engineering, and IoT datasets into both his research and the classroom. He previously served as the inaugural Center Manager of the Eversource Energy Center at UConn and has spent nearly 15 years working with utility data across projects involving IoT analytics, natural language processing, remote sensing, and storm-outage forecasting. He also serves as Academic Director for Business Data Analytics and advises BittBridge Labs, a student-led experiential learning program in decentralized AI focused on deep learning time-series modeling. Across his teaching and mentorship, Dr. Wanik helps students learn to work with real-world data, build modern analytical workflows, and solve practical challenges faced by industry.

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