Abby Stylianou
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Associate Professor, Department of Computer Science
Fellow, Taylor Geospatial Institute
Fellow, SLU Research Institute

My research lies at the intersection of fine grained visual categorization, deep metric learning and image retrieval, and explainable AI. I am particularly motivated by applications of machine learning and computer vision in social justice and in science. In recent years, the applications I have focused on include building models for hotel-specific image retrieval in order to locate victims of sex trafficking who have been photographed in hotels, learning descriptions of plant phenomics and how they relate 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. I am additionally interested in the development of machine learning benchmarks and competitions to broaden participation in machine learning for science, and in the design of visualization and interpretability tools to better understand machine learning algorithms and make their decisions accessible to non-experts.

I joined Saint Louis University in Fall of 2019 as an Assistant Professor of Computer Science at Saint Louis University, and happily was promoted to Associate Professor starting in Fall 2024. I received my B.A. in Environmental Studies: Geoscience from Washington University in St. Louis. I was then hired as a Research Scientist by Dr. Robert Pless, in the Media and Machines Lab at WashU, where my work focused on tools for the calibration and validation of outdoor imagery. I ended up pursuing my M.S. in Computer Science part time while working as a Research Scientist, before decided to pursue my Ph.D. My 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. I continued working on this project as a Postdoc at George Washington University, working with Dr. Pless, and funded by the National Institute of Justice.

• Looking forward to giving a keynote at the CVPR 2023 Women in Computer Vision Workshop!
• I am honored to have been named to the inaugural class of Taylor Geospatial Institute Fellows.
• I am happy to share that I will once again be serving as Publicity Chair for WACV (after having served in the same capacity for WACV 2020, 2021 and 2023, CVPR 2020, 2021 and 2023, and ICCV 2023).
• The SLU School of Medicine Presidential Research Fund awarded my grant with Dr. Kedar Padhye (SLU Orthopedics) entitled "Machine Learning for Implant Classification in Spinal Revision Surgery"!
• Delighted to be one of the St. Louis Business Journal's 40 Under 40 for 2023!
• Happy to be serving as Area Chair for CVPR 2023!
• I'm extremely honored to have been named one of Google's 2022 Research Scholar Program Recipients, for my project "Using Deep Learning to Uncover Unknown Genotype x Phenotype Relationships in Crops"
• Excited to share "Comparing deep learning approaches for understanding genotype x phenotype interactions in biomass sorghum" in Frontiers in AI, wrapping up years of work funded by the US Department of Energy/ARPA-E.
• Happy to be serving as Area Chair for ECCV 2022!
• The Sorghum-100 Cultivar Identification Comptetition (part of FGVC 9 at CVPR 2022) closed with over 250 competitors!
• We've launched two Kaggle competitions as part of CVPR 2022 FGVC9: the Sorghum-100 Cultivar Identification Comptetition and the Hotel-ID to Combat Human Trafficking 2022 Comptetition
• Check out our work on "Visualizing paired image similarity in transformer networks" from WACV2022, following up on our prior work in visualizing image similarity from WACV2020!
• Ecstatic to be part of the recently funded NSF Biology Integration Institute "New Roots for Restoration" -- my lab will be focused on building low cost platforms and models for in field image based plant phenotyping!
• Our lab received two best paper awards (tied for first place!) at the ICCV 2021 Computer Vision Problems in Plant Phenotyping and Agriculture Workshop -- "What does TERRA-REF’s high resolution, multi sensor plant sensing public domain data offer the computer vision community?" and "Classification and visualization of genotype × phenotype interactions in biomass sorghu"

I am proud to have published with a variety of undergraduate and graduate students, and am always seeking undergraduate and PhD students with strong machine learning and computer vision background to join my lab. However, I receive a huge number of solicitations for positions in my lab and do not have time to respond to them all (I would never do anything else!). If you would like to stick out from the noise, please be sure to include significant details about your background (pluses: prior publications, evidence of very strong Python programming skills and programming for deep learning in particular, applications you've built, etc.) as well as why you are interested in joining my lab in particular. Students seeking to join the CS PhD program at SLU should reach out to me in addition to applying to the Saint Louis University Computer Science PhD program.

• CSCI 4740/5740: Artifical Intelligence
• CSCI 5961: Artifical Intelligence MS Capstone

• CSCI 4830/5830: Computer Vision

• CSCI 4710/5710: Databases
• CSCI 5090: CS Colloquium

• Maternity Leave!

• CSCI 1070: Taming Big Data
• CSCI 4710/5710: Databases

• CSCI 5760: Deep Learning

• CSCI 1070: Taming Big Data

• CSCI 1070: Taming Big Data

• CSCI 1070: Taming Big Data