Dr. Abby Stylianou is an Assistant Professor of Computer Science at Saint Louis University, and a Fellow of the Taylor Geospatial Institute. In recent years, Dr. Stylianou's research has focused on building citizen science data collection applications and global scale image search tools, specifically to support the National Center for Missing and Exploited Children’s investigations of child sexual abuse and human trafficking. Dr. Stylianou is generally excited about applications of computer vision and machine learning that have the potential to benefit society in some way. Beyond the work to help in investigations of child sexual abuse and human trafficking, she has worked on projects including developing systems for making measurements of the natural environment in time-lapse imagery to understand climate change, observing how individuals interact with the world around them in outdoor webcam images to support better design of the built environment, and developing new vision and machine learning algorithms and systems for agriculture and plant breeding to develop more sustainable, more resilient, and healthier crops. Dr. Stylianou has been lucky enough to serve in recent years as Social Media Chair for CVPR, ICCV and WACV, as well as an Area Chair for CVPR, ECCV and WACV.

Dr. Angel Chang is an Assistant Professor at Simon Fraser University and a Canada CIFAR AI Chair. Her research connects language to visual and 3D representations, and grounds language for embodied agents in indoor environments. She has worked on synthesizing 3D scenes and shapes from natural language, as well as localizing objects in 3D.  Her work has been recognized by awards such as the SGP dataset award for ShapeNet and ScanNet, and a TUM-IAS Hans Fischer Fellowship.

Dr. Devi Parikh is a Research Director in Generative AI at Meta, and an Associate Professor in the School of Interactive Computing at Georgia Tech Before this, she was a Director in the Fundamental AI Research (FAIR) lab at Meta. From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. From 2009 to 2012, she was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), an academic computer science institute affiliated with University of Chicago. She has held visiting positions at Cornell University, University of Texas at Austin, Microsoft Research, MIT, Carnegie Mellon University, and Facebook AI Research. She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. She received her B.S. in Electrical and Computer Engineering from Rowan University in 2005. Her research interests are in computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity. She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, an Office of Naval Research (ONR) Young Investigator Program (YIP) award, an Army Research Office (ARO) Young Investigator Program (YIP) award, a Sigma Xi Young Faculty Award at Georgia Tech, an Allen Distinguished Investigator Award in Artificial Intelligence from the Paul G. Allen Family Foundation, four Google Faculty Research Awards, an Amazon Academic Research Award, a Lockheed Martin Inspirational Young Faculty Award at Georgia Tech, an Outstanding New Assistant Professor award from the College of Engineering at Virginia Tech, a Rowan University Medal of Excellence for Alumni Achievement, Rowan University’s 40 under 40 recognition, a Forbes’ list of 20 “Incredible Women Advancing A.I. Research” recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).

Dr. Judy Hoffman is an Assistant Professor in the School of Interactive Computing at Georgia Tech, a member of the Machine Learning Center, and a Diversity and Inclusion Fellow. Her research lies at the intersection of computer vision and machine learning with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness. She has received numerous awards including NSF CAREER, Google Research Scholar Award (2022), Samsung AI Researcher of the Year Award (2021), NVIDIA female leader in computer vision award (2020), AIMiner top 100 most influential scholars in Machine Learning (2020), MIT EECS Rising Star in 2015, and the NSF Graduate Fellowship. In addition to her research, she co-founded and continues to advise for Women in Computer Vision, an organization which provides mentorship and travel support for early-career women in the computer vision community. Prior to joining Georgia Tech, she was a Research Scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016 after which she completed Postdocs at Stanford University (2017) and UC Berkeley (2018).

Dr. Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin and a Research Director in Facebook AI Research (FAIR).  Her research in computer vision and machine learning focuses on video, visual recognition, and action for perception or embodied AI.  Before joining UT-Austin in 2007, she received her Ph.D. at MIT.  She is an IEEE Fellow, AAAI Fellow, Sloan Fellow, a Microsoft Research New Faculty Fellow, and a recipient of NSF CAREER and ONR Young Investigator awards, the PAMI Young Researcher Award in 2013, the 2013 Computers and Thought Award from the International Joint Conference on Artificial Intelligence (IJCAI), the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013.  She was inducted into the UT Academy of Distinguished Teachers in 2017.  She and her collaborators have been recognized with several Best Paper awards in computer vision, including a 2011 Marr Prize and a 2017 Helmholtz Prize (test of time award).  She served for six years as an Associate Editor-in-Chief for the Transactions on Pattern Analysis and Machine Intelligence (PAMI) and for ten years as an Editorial Board member for the International Journal of Computer Vision (IJCV).  She also served as a Program Chair of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 and a Program Chair of Neural Information Processing Systems (NeurIPS) 2018, and will serve as a Program Chair of the IEEE International Conference on Computer Vision (ICCV) 2023.

In the overlap of computer vision and machine learning, Dr. Ilke Demir's research focuses on generative models for digitizing the real world, deep fake detection and generation techniques, analysis and synthesis approaches in geospatial machine learning, and computational geometry for synthesis and fabrication. Currently, she is a Senior Staff Research Scientist at Intel Corporation.

Dr. Demir earned her Ph.D. and M.S. in Computer Science from Purdue University advised by Prof. Daniel Aliaga, and her B.S. in Computer Engineering from Middle East Technical University with a minor in Electrical Engineering. Her Ph.D. dissertation conceives geometric and topological shape processing approaches for reconstruction, modeling, and synthesis; which pioneered the area of proceduralization. Afterwards, Dr. Demir joined Facebook as a Postdoctoral Research Scientist working with Prof. Ramesh Raskar from MIT, where their team developed the breakthrough innovation on generative street addresses. Her research further included deep learning approaches for human behavior understanding in next generation virtual reality headsets, geospatial machine learning for map creation, and 3D reconstruction at scale.

At the intersection of art and science, Dr. Demir contributed to several animated feature and VR/AR short films in Pixar Animation Studios and Intel Studios, respectively. She established the research foundations of the worldÕs largest volumetric capture studio at Intel, bridging the gap between the creative process and AI approaches. Prior to joining Intel, she had a brief startup experience (with a successful acquisition), and she was also a visiting scholar at UCLA.

In addition to her publications in top-tier venues, she has organized workshops, competitions, and courses in deep learning, computer vision, and graphics such as DeepGlobe, SkelNetOn, WiCV, SUMO, DLGC, EarthVision, and OpenEDS, to name a few. Dr. Demir received numerous awards and honors such as Jack Dangermond Award, Bilsland Dissertation Fellowship, and IEEE Industry Distinguished Lecturer, in addition to her best paper/poster/reviewer awards. Her scientific articles received significant attention from researchers and media outlets around the world, such as The Independent, VentureBeat, MIT Tech Review, and Liberation. She has been invited to present over 50 talks and panels worldwide, on the wide range of topics that her work spans.

Dr. Demir has been actively involved in women in science organisms, always being an advocate for women and underrepresented minorities.

ACM involvement:
- ACM SIGGRAPH member since 2011
- Reviewer for ACM SIGGRAPH, TOG
- ACM SIGGRAPH course contributor and BoF organizer in 2016, 2017, 2018

More information: http://ilkedemir.github.io