Scholar
Abby Stylianou
Google Scholar ID: mNoB9SgAAAAJ
Associate Professor, Saint Louis University
Citations & Impact
Citations
768
H-index
11
i10-index
11
Publications
20
Co-authors
8
list available
Publications
Resume (English only)
Academic Achievements
- Published 'Comparing Deep Learning Approaches for Understanding Genotype x Phenotype Interactions in Biomass Sorghum' in Frontiers in AI; Named one of Google's 2022 Research Scholar Program Recipients; Selected as one of St. Louis Business Journal's 40 Under 40 for 2023; Keynote speaker at CVPR 2023 Women in Computer Vision Workshop; Part of the inaugural class of Taylor Geospatial Institute Fellows; Served multiple times as Publicity Chair or Area Chair for WACV, CVPR, and ECCV; Launched several Kaggle competitions including the Sorghum-100 Cultivar Identification Competition and the Hotel-ID to Combat Human Trafficking 2022 Competition.
Research Experience
- Worked as a Research Scientist in the Media and Machines Lab at Washington University in St. Louis; Conducted postdoctoral research at George Washington University; Served as a faculty member at Saint Louis University since 2019.
Education
- Joined Saint Louis University in Fall 2019 as an Assistant Professor of Computer Science, promoted to Associate Professor starting in Fall 2024. Received her B.A. in Environmental Studies: Geoscience from Washington University in St. Louis. Hired as a Research Scientist by Dr. Robert Pless in the Media and Machines Lab at WashU, where she focused on tools for the calibration and validation of outdoor imagery. Pursued her M.S. in Computer Science part-time while working as a Research Scientist, before deciding to pursue her Ph.D. Her Ph.D. dissertation focused on large-scale image search approaches to combat human trafficking by recognizing the hotels that victims of human trafficking are photographed. Continued this project as a Postdoc at George Washington University, working with Dr. Pless, funded by the National Institute of Justice.
Background
- Her research interests lie at the intersection of fine-grained visual categorization, deep metric learning, and image retrieval, as well as explainable AI. She is particularly motivated by applications of machine learning and computer vision in social justice and science, such as building models for hotel-specific image retrieval to locate victims of sex trafficking, learning descriptions of plant phenomics and their relation to underlying genetics and environmental factors, and observing how individuals interact with the world around them in outdoor webcam images to support better design of the built environment. Additionally, she is interested in developing machine learning benchmarks and competitions to broaden participation in ML for science, and designing visualization and interpretability tools to better understand ML algorithms and make their decisions accessible to non-experts.
Miscellany
- No personal interests mentioned.
