Vidita Gawade
Title: Assistant Professor
Department: Business and Quantitative Analytics
Campus: New York City
Area(s) of Expertise: Quality Monitoring, Artificial Intelligence, Smart Manufacturing, Teaching Effectiveness
Education Credentials: Ph.D.
Joined New York Tech: 2023
Vidita Gawade obtained her Ph.D. in Industrial and Systems Engineering at Rutgers University in 2023 and her M.Eng. in Operations Research and Information Engineering at Cornell University in 2018. She was a data engineer at JPMorgan Chase & Co. between her Ph.D. and M.Eng. studies.
Her teaching interests include business and quantitative analytics. Her research interests include quality monitoring, state-of-the-art domain-based artificial intelligence in smart manufacturing and data-driven solutions in emerging business applications, and preparing students for Industry 4.0 workforce. She has spoken at various conferences including INFORMS, IISE (Institute of Industrial & Systems Engineers), NAMRC (North American Manufacturing Research Conference), and HFES (Human Factors and Ergonomics Society).
Selected Publications
- Gawade, V., Chen, M. (2025). “Explaining multimodal CNN-DNN model predictions for quality monitoring of porosity in laser metal deposition”. Knowledge-Based Systems. In press. DOI: 10.1016/j.knosys.2025.113095.
- Regal, E., Gawade, V., Guo, WG. (2024). “Physics-informed loss functions with explainable AI to predict emission in powder bed fusion.” International Manufacturing Science and Engineering Conference. 88117. V002T07A010. DOI: 10.1115/MSEC2024-125898.
- Saleh, S., Gawade, V., Bifulco, C., Guo, WG. (2024). “The impact of investment in technology and learning techniques in work design and ergonomics laboratory for diverse and inclusive interest of ISE students in human factors and ergonomics.” IISE Annual Conference Proceedings.
- Guo, WG., Gawade, V., Zhang, B. Guo, Y., (2023). “Explainable AI for layer-wise emission prediction in laser fusion”. CIRP Annals. 72, 1, 437-440. DOI: 10.1016/j.cirp.2023.03.009.
- McGowan, E., Gawade, V. and Guo, WG., (2022). “A physics-informed convolutional neural network with custom loss functions for porosity prediction in laser metal deposition”. Sensors, 22, 2, p.494. DOI: 10.3390/s22020494.
- Gawade, V., Tian, Q. and Guo, WG., (2022). “Simulation-based analysis of train speed for single-track railway scheduling”. IISE Annual Conference Proceedings.
- Gawade, V., Bifulco, C. and Guo, WG., (2022). “Lessons learned to effectively teach and evaluate undergraduate engineers in work design and ergonomics laboratory from a world before, during, and after COVID-19. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 66, 1, 756-760. DOI: 10.1177/1071181322661505.
- Gawade, V., Singh, V., Guo, WG., (2022). Leveraging simulated and empirical data-driven insight to supervised-learning for porosity prediction in laser metal deposition. Journal of Manufacturing Systems, 62, 875–885. DOI: 10.1016/j.jmsy.2021.07.013.
Professional Honors and Awards
- 2022 WORMS Doctoral Student Colloquium Award
- 2022 IISE QSR Best Student Paper Competition Finalist
- 2022 NAMRC NSF Travel Award
Courses Taught at New York Tech
- Business and Quantitative Analytics Courses