[Email] [Google Scholar] [Twitter] [LinkedIn]

I am an Assistant Professor at Boston University in the Department of Computer Science . I am also an Affiliated Faculty with the Department of Electrical and Computer Engineering and Faculty of Computing & Data Sciences and an academic collaborator with Runway. My research interests are in building safe, interpretable, and robust computer vision systems and also on improving their reasoning capabilities. I obtained my PhD at the University of Texas at Austin and worked at Facebook AI Applied Research and Runway prior to joining BU.

Note to prospective students:

  • If you are currently a MS student at BU and are interested in working with me, please first take either CS599 (Advanced Topics in Computer Vision) or CS542 (Principles of Machine Learning) , or CS541 (Applied achine Learning) . I do not recruit students who haven’t first taken one of these courses. Please do not email me before taking these courses.
  • If you are interested in applying to BU as a PhD student and are interested in working with me, mention my name in your application. It is not necessary to contact me and attach your application.
  • Due to large volumes, I am unable to respond to your emails.

News

Professional Service

Short Bio

Deepti is an Assistant Professor in the Department of Computer Science in Boston University. Her research interests are in Computer Vision and Machine Learning, with a special focus improving the safety, interpretability, and robustness of machine learning systems. Previously she spent over an year as a researcher at Runway and over 5 years at Meta AI Research working on image and video understanding models, fair and inclusive computer vision models, and ML explainability. She obtained her PhD at the University of Texas at Austin in 2017 on perceptual image and video quality assessment for real-world content. She has served as a program chair for NeurIPS 2022 Dataset and Benchmarks track, AIMLsystems’23, and an area chair for several conferences such as IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Winter Conference on Applications of Computer Vision (WACV), Women in Machine Learning (WiML), and Association for the Advancement of Artificial Intelligence (AAAI). She organized several workshops on the topics of responsible and explainable computer vision at top-tier machine learning conferences.