I teach machines to see like people and interact with people. As modern machines struggle to fully conceptualize the visual world, my research bootstraps machine learning using frameworks from behavioral and social sciences.
Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering. His research lies at the intersection of computer vision and human computer interaction. This research has received best paper, outstanding paper, and orals at CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV, and has been reported by Science, Forbes, the Wall Street Journal, and PBS NOVA. His research has been supported by Google, Amazon, Cisco, Toyota Research Institute, NSF, ONR, and Yahoo. He holds a bachelor's degree in Electrical & Computer Engineering and in Computer Science from Cornell University, a master's degree in Computer Science from Stanford University and a Ph.D. in Computer Science from Stanford University.
RECENT PAPER HIGHLIGHTS [Oct 2023] Our paper on Explanations and human-AI decision making get awarded a best paper honorable mention at CSCW 2023 [Oct 2023] Two of our papers will appear as orals at NeurIPS 2023: datacomp and quilt. [Mar 2023] Our CVPR 2023 paper was recognized as a CVPR Highlight awarded to top 2.5% of submissions. [Nov 2022] Our work won the Madrona Prize for the most commercializable research project. [Nov 2022] Our work at PNAS 2022 has been covered by the press at Science and TechXplore. [Sep 2022] Our large scale AI deployment interacting and learning from real human interactions online appears at PNAS 2022. [Oct 2021] Our ACL 2021 paper will receive an outstanding paper award (top 6 papers at the conference). RECENT TALKS [Oct 2023] I am giving two keynote talks at ICCV workshops: Scene Graphs and Graph Representation Learning and On Closing The Loop Between [Jun 2023] I will be giving one of the keynote talks at the New Frontiers in Vision and Language Reasoning workshop at CVPR 2023. [Oct 2021] I am speaking at the Compositionality in Computer Vision workshop at ICCV 2021. [Jul 2021] I am speaking at the workshop on Humans in the learning loop at ICML 2021. [Apr 2021] I have successfully defended my Ph.D. at Stanford University and graduated with a distinction in teaching for instructing 5 courses. RECENT WORKSHOPS [Jun 2024] Synthetic Data for Computer Vision at CVPR 2024 [Oct 2023] International Challenge on Compositional and Multimodal Perception at ICCV 2023 [Jul 2023] Artificial Intelligence and Human-Computer Interaction at ICML 2023 [Oct 2022] Compositionality in Computer Vision workshop at ECCV 2022. [Oct 2021] Compositionality in Computer Vision workshop at ICCV 2021. [Aug 2020] International Challenge on Compositional and Multimodal Perception workshop at ECCV 2020. [Jun 2020] Compositionality in Computer Vision workshop at CVPR 2020. [Jun 2020] ActivityNet Challenge on Dense Captioning Events in Videos at CVPR 2020. [Oct 2019] Scene Graph Representation and Learning. [Oct 2019] Co-organizer and a guest editor for an IEEE TPAMI special issue on Graphs in Vision and Pattern Analysis. Pre-prints & working papers
ACademic Publications
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Ph.D. @ Stanford University, 2021
Co-advised by Fei-Fei Li and Michael Bernstein. Curriculum Vitae [2023] Google scholar Research statement [2021] Teaching statement [2021] Diversity statement [2021] CONTACT
ranjay [at] cs [dot] washington [dot] edu Bill & Melinda Gates Center Room 304 3800 E Stevens Way NE, Seattle, WA 98195 TEACHING
University of Washington: CSE 599H: AI vs IA [2023] CSE 493G1: Deep learning [2023] [2024] CSE 455: Computer Vision [2024] CSE 576: Advanced Computer Vision [2025] Stanford University: CS231N: Convolutional Neural Networks for Visual Recognition [2020] [2021] CS131 Computer Vision: Foundations and Applications [2019] [2018] [2017] - [Link to all assignments] - [Link to the crowdsourced class notes book] Research GroupPhD students
Cheng-Yu Hsieh
(2022-) Jieyu Zhang
(2023-) Masters and undergraduate students
Long term collaborating PhD students
Yushi Hu with Noah Smith
Arijit Ray with Kate Saenko
Former masters students
- Sho Arora - Ines Chami - Apoorva Dornadula - Oliver Groth - Mayank Kumar - Mona Gandhi - Donsuk Lee Former undergraduate students - Stephanie Chen - Vincent Chen - Shubhang Desai - Omer Gul - Kenji Hata - Jerry Hong - Khaled Jedoui - Pranav Khadpe - Joshua Kravitz - Michelle Lam - Jihyeon Janel Lee - Austin Narcomey - Junwon Park Former PhD mentees - Done He - Jingwei Ji - Siddharth Karamcheti Selected TalksVenue: CVPR 2020 - Computer Vision and Pattern Recognition
Title: Compositionally in Computer Vision [slides][video][workshop] Venue: CVPR 2020 - Computer Vision and Pattern Recognition
Title: Dense Captioning Events in Videos [slides][video][workshop] Venue: ECCV 2016 - European Conference on Computer Vision
Title: Visual Relationship Detection with Language Priors [pdf][project][slides][poster][video] Venue: CHI 2016 - Conference on Human Factors in Computer Systems
Title: Embracing Error to Enable Rapid Crowdsourcing [pdf][slides] MISCELLANEOUS
Trailer for a documentary
Venue: PBS NOVA Title: Can we build a brain? Year: 2018 Complete documentary
Venue: PBS NOVA Title: Can we build a brain? Year: 2018 |