Unaiza Ahsan
Education
- Ph.D. in Computer Science, Georgia Institute of Technology, Atlanta, GA
- M.Engg. in Computer and Information Systems, NED University of Engineering and Technology, Karachi, Pakistan
- B.E. in Telecommunications, NED University of Engineering and Technology, Karachi, Pakistan
Research Interests
- Video recognition
- Semi-supervised and transfer learning
- Complex event recognition
- Few-shot learning
- Action recognition
- Multi-modal recommendations
Biography
Dr. Unaiza Ahsan is a computer scientist and researcher interested broadly in Machine Learning, Computer Vision, and Recommendation Systems. She completed her Ph.D. at Georgia Institute of Technology (2012–2019) as a Graduate Research and Teaching Assistant, where her research on complex activity recognition leveraged mid-level representations to improve video recognition accuracy without requiring large labeled datasets; during her studies, she was awarded the Schlumberger Faculty for the Future Fellowship, which fully funded her Ph.D.
Her professional experience includes serving as Lead Data Scientist at The Home Depot in Atlanta, working on recommendation algorithms using computer vision and NLP techniques. As a Graduate Research Assistant, she also worked with the Data Science for Social Good program, developing web applications addressing real-world challenges, from event recognition in images to visually compatible home décor recommendations.
Selected Publications
- Wang, Y., Ahsan, U., Li, H., and Hagen, M. “A Comprehensive Review of Modern Object Segmentation Approaches.” Foundations and Trends in Computer Graphics and Vision, 13(2-3), 2022.
- Ahsan, U., Wang, Y., Guo, A., Tynes Jr., K. D., Xu, T., Afshar, E., and Cui, X. “Visually Compatible Home Decor Recommendations Using Object Detection and Product Matching.” CSCI, 2021.
- Al Jadda, K., Ahsan, U., and Qu, H. “Complementary Item Recommendations Based on Multi-Modal Embeddings.” U.S. Patent Application 17/011,543, filed 2021.
- Ahsan, U., Madhok, R., and Essa, I. “Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition.” IEEE WACV, 2019.
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