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I’m Christopher Kanan, a tenured Associate Professor of Computer Science at the University of Rochester, where I lead the Hajim School of Engineering & Applied Sciences’ AI Initiative. I hold secondary appointments in Brain and Cognitive Sciences, The Goergen Institute for Data Science and AI (GIDS-AI), and The Center for Visual Science.

With over 20 years of experience in artificial intelligence, my research focuses on developing deep learning systems that advance the foundational capabilities necessary for artificial general intelligence (AGI). My work spans deep continual learning, multi-modal scene understanding, visual question answering, self-supervised learning, medical computer vision (pathology and radiology), semantic segmentation, object recognition, active vision, object tracking, and more. I also bring a strong background in cognitive science, primate vision, theoretical neuroscience, and eye tracking.

From 2018 to 2022, I served as an executive leader at Paige.AI, where I spearheaded AI research that led to Paige Prostate—the first FDA-cleared AI system in pathology. I played a key role in scaling Paige from a small start-up to a company with over 180 employees and led its patent initiative, earning 60+ granted patents. I continue to contribute as a member of Paige’s Scientific Advisory Board.

Previously, I was a tenured Associate Professor in the Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT). At RIT, I co-founded the Center for Human-aware AI (CHAI), and I served as its Associate Director for four years. I was also a member of RIT’s McNair Scholars Advisory Board, part of RIT’s Division of Diversity and Inclusion. From 2019 – 2022, I was a visiting professor at Cornell Tech in New York City, where I taught a course on Deep Learning for four years to about 100 graduate students annually.

I received my PhD from UC San Diego (UCSD), was a postdoctoral scholar at Caltech, and worked as a scientist at NASA JPL.

See my publications page for links to specific projects, code, and datasets.

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Recent News

2025/01: I’m serving as an Associate Program Chair for the Conference on Lifelong Learning Agents (CoLLAs).
2024/09: Our paper on understanding out-of-distribution generalization was accepted to NeurIPS-2024!
2024/07: Our paper on the foundation model Virchow was published in Nature Medicine!
2024/03: I was elected as a Senior Member of AAAI!
2023/12: My PhD student Robik Shrestha defended his dissertation on VQA and bias robust AI! He is joining Amazon AGI.
2023/09: Won an NSF grant with Dhireesha Kudithipudi and others for $2M!
2023/09: Won a DoE grant with Riccardo Betti and others for $3M to use generative AI to advance nuclear fusion research!
2023/08: Lab alumnus Dr. Ron Kemker won the Harold Brown Award from the USAF!
2022/08: I was honored to be profiled in Quanta Magazine.
2022/07: Our paper on OccamNets was accepted to ECCV-2022 as an Oral!
2022/07: I am now an Associate Professor of Computer Science at the University of Rochester, where I’ll be leading various AI activities!
2022/06: My PhD student Usman Mahmood defended his dissertation on detecting and mitigating bias in AI for radiology!!
2022/06: My PhD student Manoj Acharya defended his dissertation and will be joining Amazon!!
2022/04: My PhD student Tyler Hayes defended her dissertation and will be joining Naver Labs!
2021/09: I am honored to have received an NSF CAREER Award!
2021/09: Led by Cissi Alm, we won an NSF NRT award for $2M to improve graduate student training in AI!
2021/09: Paige Prostate received FDA clearance! I oversaw the AI research activities for the system.
2021/05: I was awarded tenure at RIT and promoted to Associate Professor!
2021/03: With SRI, we won an award from DARPA for Lifelong Learning on Starcraft 2!

Older News

2020/09: Our paper analyzing metrics and bias in VQA-CP was accepted to NeurIPS-2020!
2020/08: My PhD student Ryne Roady defended his dissertation on open set recognition!
2020/07: Our REMIND paper for streaming CNN training was accepted to ECCV-2020!
2020/06: Our paper on deep streaming linear discriminant analysis won best paper award at CLVISION!
2020/06: Gave a talk at CVPR-2020 on improving continual learning.
2020/06: Gave a talk at CVPR-2020 on Paige’s cancer detection technology.
2020/05: Paper accepted to ACL examining methods for VQA with changing priors.
2020/03: My PhD student Kushal Kafle defended his dissertation and will be joining Adobe Research!!
2019/10: Paper accepted to WACV-2020 that achieves super-human performance on chart question answering!
2019/09: Helped RIT win the Facebook OpenEDS Semantic Segmentation Challenge!
2019/06: Won an NSF grant for multi-modal streaming learning!
2019/06: Won a grant with Christy Tyler to detect invasive plant species!
2019/04: Gave an invited talk at SUNY Buffalo on memory replay and the hippocampus
2019/02: Our paper on universal VQA models was accepted to CVPR-2019!
2019/02: Our paper on Visual Query Detection was accepted to NAACL-2019!
2019/01: Our paper on memory efficient experience replay for streaming learning was accepted to ICRA-2019!
2019/01: Became a Visiting Professor at Cornell Tech and will be teaching Deep Learning. I’ll be at RIT 1-2 days per week.
2018/12: I am co-organizing the NAACL-2019 Workshop on Shortcomings in Vision and Language (SiVL).
2018/11: Joining Paige as a Senior AI Scientist to lead a group working on improving detection and treatment of cancer.
2018/10: Won a $206K DARPA/ARL award for lifelong learning as PI!
2018/10: My lab’s TallyQA paper was accepted to AAAI-2019. It is the largest VQA counting dataset.
2018/09: Joined RIT’s McNair Scholars Advisory Board to help organize training programs for underrepresented students.
2018/08: Panelist discussing Artificial General Intelligence on NPR Member Station WXXI’s Connections with Evan Dawson.
2018/06: Our web-app for Visual Question Answering was relaunched: http://www.askimage.org/
2018/06: Released EarthMapper software toolbox for low-shot semantic segmentation.
2018/05: Appointed Associate Director of RIT’s Center for Human-aware AI (CHAI).
2018/04: Ron Kemker became the first person to defend his PhD in my lab! Congratulations!!
2018/03: Two papers on semantic segmentation for remote sensing accepted in TGRS and P&RS.
2018/03: Check out EarthMapper, our toolbox for low-shot semantic segmentation.
2018/02: Won a $352K grant from USAF Materiel Command as Co-PI (Andreas Savakis is PI)!
2018/02: Our DVQA paper that does VQA for data visualizations was accepted to CVPR-2018!
2018/01: Our FearNet paper for lifelong learning was accepted to ICLR-2018!
2018/01: Teaching IMGS 682 – Image Processing and Computer Vision.
2017/11: Adobe Research has given my lab a gift of $22,000. Thank you Adobe Research!
2017/11: Won a $33K NGA Phase 1 SBIR with our collaborators at Intelligent Automation, Inc. to do target detection.
2017/11: My lab’s paper on measuring catastrophic forgetting was accepted to AAAI-2018!
2017/10: I was an invited speaker at the ICCV-2017 Workshop on Closing the Loop between Vision and Language.
2017/08: Our new VQA dataset, TDIUC, is now publicly available.
2017/08: Our RIT-18 dataset for multispectral semantic segmentation is now publicly available.
2017/08: Teaching the course Deep Learning for Vision.
2017/07: My paper with Dhireesha Kudithipudi’s lab on video classification was accepted to ICRC-2017.
2017/07: Our paper analyzing VQA algorithms using TDIUC was accepted to ICCV-2017.
2017/06: My first robotics paper, which is on using deep learning for detecting good grasps, was accepted to IROS-2017.
2017/06: My lab’s critical review paper on the state of VQA was accepted by the journal CVIU.
2017/05: I am co-organizing the ICCV-2017 workshop on Mutual Benefits of Cognitive and Computer Vision (MBCC).
2017/02: I’m an area chair for ICIP-2017.
2017/01: Teaching IMGS 682 – Image Processing and Computer Vision.
2016/12: Our paper on self-taught deep learning models for semantic segmentation of hyperspectral images was accepted to IEEE TGRS.
2016/08: Teaching IMGS 789 – Deep Learning for Vision.
2016/06: Our paper on Answer-Type Prediction for Visual Question Answering appeared at CVPR-2016.
2016/05: I won the RIT College of Science Rising Star Award.
2016/03: Our paper on using gnostic fields and saliency for tracking appeared at WACV-2016.
2016/01: Started teaching IMGS 682 – Image Processing and Computer Vision.
2015/08: Started as an assistant professor at RIT. My lab: http://klab.cis.rit.edu/
2015/07: The VAIS dataset for ship classification is now available for download.
2015/07: Our paper on combining hierarchical ICA with Gnostic Fields appeared at COGSCI-2015.
2015/06: Our paper on the VAIS dataset for ship classification appeared at the CVPR-2015 Perception Beyond the Visible Spectrum workshop.
2015/01: Our paper on the Multi-Fixation Pattern Analysis, a new method for analyzing eye movement data, appeared in Vision Research.

AI Leadership & Service

I lead the AI Initiative at the University of Rochester’s Hajim School of Engineering & Applied Sciences and serve on the University AI Governance Council, helping shape AI strategy and policy across the university. My leadership includes serving as an Associate Program Chair for the Conference on Lifelong Learning Agents (CoLLAs) and as an Area Chair for top AI conferences such as NeurIPS and ICIP, as well as helping to organize a variety of workshops attached to premier AI conferences. I have advised the US government on AI topics, especially continual learning, including contributing to national defense initiatives. I’m on the Data Science Advisory Board for Ampersand Capital Partners.

I have actively trained and mentored students from underrepresented backgrounds through programs such as NSF LSAMP and McNair, helping them succeed in research and pursue graduate studies. My efforts include supervising undergraduate research and mentoring high school students from low-income communities, many of whom have gone on to prestigious universities.

Biography

I grew up in rural Oklahoma, where my fascination with artificial intelligence began in 1996 when I developed “bots” for online multiplayer games in high school. I earned a dual degree in philosophy and computer science at Oklahoma State University, followed by an MS in computer science at the University of Southern California (USC), where I specialized in AI and neuroscience under Michael Arbib, a pioneer in computational neuroscience.

I completed my PhD in computer science at the University of California, San Diego (UCSD) as part of Gary Cottrell’s research group, one of the few labs at the time still working on neural networks when much of the AI community had (foolishly) abandoned them as a dead end. Afterward, I became a Postdoctoral Scholar at Caltech, followed by working at NASA’s Jet Propulsion Laboratory (JPL) in the Maritime and Aerial Perception group as a Research Technologist working on deep learning to create vision systems for autonomous maritime vessels.

In 2015, I joined the Chester F. Carlson Center for Imaging Science at RIT, earning tenure in 2021. As one of the first employees at Paige in 2018, I led AI research to build computational pathology systems for cancer diagnosis, contributing to the first FDA-cleared AI system in pathology. Currently, I am an Associate Professor of Computer Science at the University of Rochester. I am a recipient of the NSF CAREER award and I’m a Senior Member in both AAAI and IEEE.