Christopher Kanan CV as of July 3, 2020

Voice: Email: Web:

(405) 714-0735 chriskanan@gmail.com www.chriskanan.com

INTERESTS

Deep Learning; Computer Vision with Natural Language Understanding; Lifelong Machine Learning

EDUCATION PhD MS BS

Computer Science Computer Science Philosophy & Computer Science

University of California San Diego University of Southern California Oklahoma State University

La Jolla, CA Los Angeles, CA Stillwater, OK

2013 2006 2004

POSITIONS HELD

Assistant Professor, Chester F. Carlson Center for Imaging Science Associate Director, Center for Human-aware Artificial Intelligence (CHAI) Affiliate Faculty, Computer Science Department McNair Scholars Advisory Board Member, Division of Diversity and Inclusion Rochester Institute of Technology, Rochester, NY

2015 – Present 2018 – Present 2018 – Present 2018 – Present

Senior AI Scientist Paige, New York, NY

2018 – Present

Visiting Assistant Professor, Computer Science Cornell Tech, New York, NY

2019 – Present

Research Technologist, Maritime and Aerial Perception Group NASA Jet Propulsion Laboratory (JPL), Pasadena, CA

2014 – 2015

Caltech Postdoctoral Scholar

California Institute of Technology, Pasadena, CA 2013 – 2014

Graduate Student Researcher

University of California San Diego, La Jolla, CA 2007 – 2013

Research Intern, Brain Inspired Cognitive Architecture Team HRL Laboratories, Malibu, CA

2005 – 2007

GRANTS & GIFT FUNDING

Total: $3,112,592 PI, RI: Small: Lifelong Multimodal Concept Learning. NSF. Award #1909696. 10/1/2019 – 9/30/2022,

$499,960. PI, Advancing Automation for Texture Analysis. Discontinued Materials, Inc. 08/12/2019 – 03/13/2020,

$41,358. PI, Using Artificial Intelligence on Street View Imagery to Detect Five Key Invasive Plant Species in New

York State. NY Department of Environmental Conservation. 4/1/2019 – 3/31/2022, $124,986 PI, Fast and Efficient Continual Learning without Catastrophic Forgetting. DARPA / US Army Research

Laboratory. 8/1/2018 – 12/31/2020, $205,959. Co-PI, High-fidelity scene modeling and vehicle tracking using hyperspectral video. US Air Force

Materiel Command, FA9550-19-1-0021. 12/1/2018 - 11/30/2021, $598,750 Co-PI, Data-Driven Adaptive Learning for Video Analytics. US Air Force Materiel Command - FA9550-

18-1-0121. 2/22/2018 – 2/21/2021, $352,152. PI, Automation for Texture Analysis. Discontinued Materials, Inc. 06/01/2018 – 05/31/2019, $67,659. Co-PI, MediSphere. PAR Government System Corporation (Prime: DARPA) - 005264-003. 5/1/2016 –

4/30/2019, $826,729. PI, Gift for Advancing Visual Question Answering. Adobe Research. 9/8/2017 – 5/23/2018, $40,000

Co-PI, Brain-Inspired Deep Neural Networks for Incremental Learning. Naval Research Laboratory (NRL). 9/1/2017 – 8/31/2018, $80,295.

PI, GFNet: Gnostic Fields based Low-Shot Learning for Target Detection in Remote Sensing. Intelligent Automation, Inc. (Prime: DoD NGA) – HM047618C0005. 11/1/2017 – 7/31/2018, $33,000.

PI, Periscope Imagery Ship Classification Project. Scientific Systems Company, Inc. (Prime: ONR) –N66604-13-C-1404/NA. 5/9/2016 – 9/25/2016, $16,453.

PI, Deep Learning for Active Vision. Rochester Institute of Technology, College of Science FEAD. 7/1/2017 – 2/1/2018, $5,500.

Co-PI, Data Driven Methods for Event Detection in Eye Tracking Signals. Rochester Institute of Technology, College of Science DRIG. 11/1/2016 – 8/31/2017, $15,000

PI, Perception System for Autonomous Sea Surface Ships. Jet Propulsion Laboratory (Prime: NASA) – HE NNN12AA01C/1541689. 12/18/2015 - 12/14/2016, $64,994.

Co-PI, Object Cueing using Biomimetic Approaches to Visual Information Processing. Scientific Systems Company, Inc. (Prime: NAVAIR) Phase 1 STTR FY2014A – Topic N14A-T008. 09/14/2014 – 04/14/2015, $24,000

Co-PI, Inter-Science of Learning Centers Conference. NSF SMA 1212288, 03/01/2012 – 02/28/2013, $115,797.

IN KIND AWARDS

PI, Detecting Invasive Plant Species from Street View Imagery. Microsoft AI for Earth Grants Program. 04/30/2019 – 04/31/2020, $15,000 in Microsoft Azure Credits.

PI, Visual Question Answering and the Web. Amazon Web Services Research Award. 03/29/2016 – 02/28/2017, $15,000 in Amazon Web Service Credits.

HONORS RIT College of Science Distinguished Scholarship Award

IEEE Senior Member Scholarship for Cornell Uni. Faculty Leadership and Professional Development Program RIT College of Science Rising Star Award TDLC Junior Investigator Award University of California President’s Dissertation Year Fellowship San Diego Diversity Fellowship NSF Integrative Graduate Education and Research Traineeship Eugene Cota-Robles Fellowship Oklahoma State University Continuing Student Scholarship Oklahoma State University Regents’ Scholarship

2019 2018 2017 2016 2013 2012 2010 2007 2007 2002 2002

BEST PAPER AWARDS

Hayes, T.L., Kanan, C. (2020) Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. CVPR Workshop on Continual Learning in Computer Vision (CLVISION).

REFEREED PUBLICATIONS

Total: 47 Journals: 18 Refereed Conferences: 22 Refereed Workshops: 7 2020

ECCV Hayes, T., Kafle, K., Shrestha, R., Acharya, M., Kanan, C. (2020) REMIND Your Deep Neural Network to Prevent Catastrophic Forgetting. In: European Conference on Computer Vision (ECCV). [27% accept rate]

ACL Shrestha, R., Kafle, K., Kanan, C. (2020) A negative case analysis of visual grounding methods for VQA. In: Annual Conference of the Association for Computational Linguistics (ACL). [23% accept rate]

CLVISION / CVPRW

Roady, R., Hayes, T.L., Vaidya, H., Kanan, C. (2020) Stream-51: Streaming Classification and Novelty Detection from Videos. CVPR Workshop on Continual Learning in Computer Vision (CLVISION).

CLVISION / CVPRW

Hayes, T.L., Kanan, C. (2020) Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. CVPR Workshop on Continual Learning in Computer Vision (CLVISION). [Best Paper Award; Oral]

Modern Pathology

Raciti, P., Sue, J., Ceballos, R., Godrich, R., Kunz, J., Kapur, S., Reuter, V.E., Grady, L., Kanan, C., Klimstra, D., Fuchs, T. (2020) Novel Artificial Intelligence System Increases the Detection of Prostate Cancer in Whole Slide Images of Core Needle Biopsies. Modern Pathology. doi: 10.1038/s41379-020-0551-y

TGRS Rangnekar, A., Mokashi, N., Ientilucci, E., Kanan, C., Hoffman, M.J. (2020) AeroRIT: A New Scene for Hyperspectral Image Analysis. IEEE Transactions on Geoscience and Remote Sensing (TGRS). doi: 10.1109/TGRS.2020.2987199

WACV Kafle, K., Shrestha, R., Cohen, S., Price, B., Kanan, C. (2020) Answering Questions about Data Visualizations using Efficient Bimodal Fusion. In: IEEE Winter Applications of Computer Vision Conference (WACV). [34% accept rate]

Scientific Reports

Kothari, R., Yang, Z., Kanan, C., Bailey, R., Pelz, J., Diaz, G. (2020) Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities. Scientific Reports. doi: 10.1038/s41598-020-59251-5

2019

Frontiers in AI

Kafle, K., Shrestha, R., Kanan, C. (2019) Challenges and prospects in vision and language research. Frontiers in Artificial Intelligence. doi: 10.3389/frai.2019.00028

NeurIPSW Seedat, N., Kanan, C. (2019) Towards calibrated and scalable uncertainty representations for neural networks. In: NeurIPS-2019 Workshop on Bayesian Deep Learning.

ICCVW Chaudhary, A.K., Kothari, R., Acharya, M., Dangi, S., Nair, N., Bailey, R., Kanan, C., Diaz, G., Pelz, J.B. (2019) RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking. The 2019 OpenEDS Workshop at ICCV-2019: Eye Tracking for VR and AR. [Winner of the Facebook eye semantic segmentation challenge]

CVPR Shrestha, R., Kafle, K., Kanan, C. (2019) Answer Them All! Toward Universal Visual Question Answering Models. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [25% accept rate]

NAACL Acharya, M., Jariwala, K., Kanan, C. (2019) VQD: Visual Query Detection in Natural Scenes. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). [23% accept rate]

Neural Networks

Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S. (2019) Continual lifelong learning with neural networks: A review. Neural Networks. doi: 10.1016/j.neunet.2019.01.012

ICRA Hayes, T., Cahill, N., Kanan, C. (2019). Memory Efficient Experience Replay for Streaming Learning. In: IEEE International Conference on Robotics and Automation (ICRA).

AAAI Acharya, M., Kafle, K., Kanan, C. (2019) TallyQA: Answering Complex Counting Questions. In: AAAI. [16% accept rate]

2018

PLOS ONE Birmingham, E., Svärd, J., Kanan, C., Fischer, H. (2018) Exploring Emotional Expression Recognition in Aging Adults using the Moving Window Technique. PLOS ONE. doi:10.1371/journal.pone.0205341

CVPRW Hayes, T., Kemker, R., Cahill, N., Kanan, C. (2018) New Metrics and Experimental Paradigms for Continual Learning. In: Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision (CVPRW).

CVPR Kafle, K., Cohen, S., Price, B., Kanan, C. (2018) DVQA: Understanding Data Visualizations via Question Answering. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [30% accept rate]

CogSci Binaee, K., Starynska, A., Pelz, J., Kanan, C., Diaz, G. (2018) Characterizing the Temporal Dynamics of Information in Visually Guided Predictive Control Using LSTM Recurrent Neural Networks. In: Proc. 40th Annual Conference of the Cognitive Science Society (CogSci).

TGRS Kemker, R., Luu, R., Kanan, C. (2018) Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing (TGRS). doi:10.1109/TGRS.2018.2833808

ICLR Kemker, R., Kanan, C. (2018) FearNet: Brain-Inspired Model for Incremental Learning. In: International Conference on Learning Representations (ICLR). [34% accept rate]

AAAI Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C. (2018) Measuring Catastrophic Forgetting in Neural Networks. In: AAAI. [24.6% accept rate; Oral presentation]

ISPRS J. Photo. Re. Sens.

Kemker, R., Salvaggio, C., Kanan, C. (2018) Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning. ISPRS Journal of Photogrammetry and Remote Sensing. doi: 10.1016/j.isprsjprs.2018.04.014

2017

ICCV Kafle, K., Kanan, C. (2017) An Analysis of Visual Question Answering Algorithms. In: International Conference on Computer Vision (ICCV). [29% accept rate]

CVIU Kafle, K., Kanan, C. (2017) Visual Question Answering: Datasets, Algorithms, and Future Challenges. J. Computer Vision and Image Understanding (CVIU). doi:10.1016/j.cviu.2017.06.005

IROS Kumra, S., Kanan, C. (2017) Robotic Grasp Detection using Deep Convolutional Neural Networks. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

AIPR Hezaveh, M.M., Kanan, C., Salvaggio, C. (2017) Roof damage assessment using deep learning. IEEE Applied Pattern Recognition Workshop (AIPR).

ICRC Graham, D., Langroudi, S., Kanan, C., Kudithipudi, D. (2017) Convolutional Drift Networks for Spatio-Temporal Processing. In: IEEE International Conference on Rebooting Computing (ICRC).

Remote Sensing

Kleynhans, T., Montanaro, M., Gerace, A., Kanan, C. (2017) Predicting Top-of-Atmosphere Thermal Radiance using MERRA-2 Atmospheric Data with Deep Learning. Remote Sensing, 9(11), 1133; doi:10.3390/rs9111133

INLG Kafle, K., Yousefhussien, M., Kanan, C. (2017) Data Augmentation for Visual Question Answering. In. International Natural Language Generation conference (INLG).

TGRS Kemker, R., Kanan, C. (2017) Self-Taught Feature Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 55(5): 2693 – 2705.

2016

CVPR Kafle, K., Kanan, C. (2016) Answer-Type Prediction for Visual Question Answering. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [30% accept rate]

WACV Yousefhussien, M., Browning, N.A., Kanan, C. (2016) Online Tracking using Saliency. In: Proc. IEEE Winter Applications of Computer Vision Conference (WACV). [34% accept rate]

2015

CogSci Wang, P., Cottrell, G., Kanan, C. (2015) Modeling the Object Recognition Pathway: A Deep Hierarchical Model Using Gnostic Fields. In: Proc. 36th Annual Conference of the Cognitive Science Society (CogSci).

PBVS Zhang, M.M., Choi, J., Daniilidis, K., Wolf, M.T., Kanan, C. (2015) VAIS: A Dataset for Recognizing Maritime Imagery in the Visible and Infrared Spectrums. In: Proc of the 11th IEEE Workshop on Perception Beyond the Visible Spectrum (PBVS).

Vision Research

Kanan, C., Bseiso, D., Ray, N., Hsiao, J., & Cottrell, G. (2015) Humans Have Idiosyncratic and Task-specific Scanpaths for Judging Faces. Vision Research. doi:10.1016/j.visres.2015.01.013

2014

WACV Kanan, C. (2014) Fine-Grained Object Recognition with Gnostic Fields. IEEE Winter Applications of Computer Vision Conference (WACV). doi:10.1109/WACV.2014.6836122

ETRA Kanan, C., Ray, N., Bseiso, D., Hsiao, J., Cottrell, G. (2014) Predicting an Observer’s Task Using Multi-Fixation Pattern Analysis. ACM Symposium on Eye Tracking Research and Applications (ETRA). doi: 10.1145/2578153.2578208

BICA Khosla, D., Huber, D.J., Kanan, C. (2014) A Neuromorphic System for Visual Object Recognition. Biologically Inspired Cognitive Architectures, 8: 33-45.

2013

Mach. Vis. Kanan, C. (2013) Active Object Recognition with a Space-Variant Retina. ISRN Machine Vision, 2013: 138057. doi:10.1155/2013/138057

PLOS ONE Kanan, C. (2013) Recognizing Sights, Smells, and Sounds With Gnostic Fields. PLOS ONE, 8(1): e54088. doi:10.1371/journal.pone.0054088

2012

Child Dev. Birmingham, E., Meixner, T., Iarocci, G., Kanan, C., Smilek, D., Tanaka, J. (2012) The Moving Window Technique: A Window into Age-Related Changes in Attention to Facial Expressions of Emotion. Child Development, 84: 1407-1424. doi:10.1111/cdev.12039

PLOS ONE Kanan, C., Cottrell, G. W. (2012) Color-to-Grayscale: Does the Method Matter in Image Recognition? PLOS ONE, 7(1): e29740. doi:10.1371/journal.pone.0029740

2010

CVPR Kanan, C., Cottrell, G. W. (2010) Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472-2479. [26.4% Accept Rate]

ISVC Kanan, C., Flores, A., Cottrell, G. (2010) Color Constancy Algorithms for Object and Face Recognition. Lecture Notes in Computer Science, 6453 (ISVC): 199-210.

2009

Vis. Cog. Kanan, C., Tong, M. H., Zhang, L., Cottrell, G. W. (2009) SUN: Top-down Saliency Using Natural Statistics. Visual Cognition, 17:979-1003.

PATENTS

Khosla, D., Kanan, C., Huber, D., Chelian, S., Srinivasa, N. (2012) Visual Attention and Object Recognition System. U.S. Patent No. 8,165,407. Washington, DC: U.S.

ABSTRACTS & POSTERS WITHOUT PROCEEDINGS

Mahmood, U., Apte, A., Kanan, C., Bates, D., Corrias, G., Mannelli, L., Oh, J., Erdi, Y., Deasy, J., Dave, A. (2020) From CT Scans to 3D Prints: Feasibility of 3D Printing CT Radiomic Phantoms for Standardization and Validation of Quantitative CT Measurements. Joint AAPM/COMP Meeting.

Hayes, T.L., Kafle, K., Shrestha, R., Acharya, M., Kanan, C. (2020) REMIND Your Neural Network to Prevent Catastrophic Forgetting. CVPR Women in Computer Vision Workshop (WiCV).

Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., van den Hengel, A. (2020) On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. CVPR-2020 Visual Question Answering and Dialog Workshop.

Dogdas, B., Kanan, C., Raciti, P., Tian, S.K., Brookman-May, S., Wetherhold, L., Smith, A., Rooney, O., McCarthy, S., Alvarez, J.D., Lopez-Gitlitz, G., Casson, A., Godrich, R., Kunz, J., Ceballos, R., Leibowitz, C., Grady, L., Fuchs, T.J. (2020) Computational pathological identification of prostate cancer following neoadjuvant treatment. American Society of Clinical Oncology (ASCO).

Kanan, C., Sue, J., Grady, L., Fuchs, T., Chandarlapaty, S., Reis-Filho, J.S., Salles, P., da Silva, L., Ferreira, C., Pereira, E. (2020) Independent validation of Paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow. American Society of Clinical Oncology (ASCO).

Chaudhary, A., Kothari, R., Acharya, M., Dangi, S., Nair, N., Bailey, R., Kanan, C., Diaz, G., Pelz, J. (2019) Robust, real-time Semantic Segmentation of the Eye for Gaze Tracking. Frameless.

Lipitz, M., Kanan, C. (2019) Vid2Cartoon: Turning Videos into Episodes of the Simpsons with Neural Animation Style Transfer. IEEE Western NY Signal Processing Workshop.

Hayes, T., Kanan, C. (2019) Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. IEEE Western NY Signal Processing Workshop.

Shrestha, R., Kafle, K., Kanan, C. (2019) Answer Them All! Toward Universal Visual Question Answering Models. IEEE Western NY Signal Processing Workshop.

Munechika, D., Roady, R.P., Kanan, C. (2019) Incremental Open Set Recognition: Exploring Novel Input Detection in Incremental Learning. IEEE Western NY Signal Processing Workshop.

Acharya, M., Flores, A., Cwitkowitz, F., Megraw, L., Tyler, C., Kanan, C. (2019) Know thy Enemy: Invasive Species Detection in High-Resolution Imagery. IEEE Western NY Signal Processing Workshop.

Kafle, K., Shrestha, R., Price, B., Cohen, S., Kanan, C. (2019) Answering Questions about Data Visualizations using Efficient Bimodal Fusion. CVPR-2019 Visual Question Answering Workshop.

Shrestha, R., Kafle, K., Kanan, C. (2019) Answer Them All! Toward Universal Visual Question Answering Models. CVPR-2019 Visual Question Answering Workshop.

Kafle, K., Shrestha, R., Price, B., Cohen, S., Kanan, C. (2019) Answering Questions about Data Visualizations using Efficient Bimodal Fusion. CVPR-2019 Workshop on Language and Vision.

Shrestha, R., Kafle, K., Kanan, C. (2019) Answer Them All! Toward Universal Visual Question Answering Models. CVPR-2019 Workshop on Language and Vision.

Acharya, M., Kafle, K., Kanan, C. (2018) TallyQA: Answering Complex Counting Questions. Workshop on Shortcomings in Vision and Language (SiVL) @ NAACL-2019.

Acharya, M., Kafle, K., Kanan, C. (2018) TallyQA: Answering Complex Counting Questions. RCQA-19 Workshop @ AAAI-2019.

Acharya, M., Kafle, K., Kanan, C. (2018) TallyQA: Answering Complex Counting Questions. NeurIPS-2018 ViGIL Workshop.

Kafle, K., Cohen, S., Price, B., Kanan, C. (2018) DVQA: Understanding Data Visualizations via Question Answering. NeurIPS-2018 ViGIL Workshop.

Hayes, T., Cahill, N., Kanan, C. (2018) Memory Efficient Experience Replay for Streaming Learning. IEEE Western NY Signal Processing Workshop.

Roady, R., Kemker, R., Gonzalez, A., Kanan, C. (2018) Evaluating Bounded Classification Methods for Deep Neural Networks. IEEE Western NY Signal Processing Workshop.

Shrestha, R., Kafle, K., Kanan, C. (2018) Comparative Analysis of VQA algorithms on Natural and Synthetic Datasets. IEEE Western NY Signal Processing Workshop.

Cwitkowitz, F., Flores, A., Kanan, C. (2018) Convolutional Neural Network Architectures for Very High Resolution Imagery. IEEE Western NY Signal Processing Workshop. [Best Poster Award Winner]

Kothari, R., Yang, Z., Pelz, J., Bailey, R., Kanan, C., Diaz, G. (2018) Gaze in wild: A dataset for studying Vestibular-Ocular coordination in naturalistic tasks. IEEE Western NY Signal Processing Workshop.

Kafle, K., Cohen, S., Price, B., Kanan, C. (2018) DVQA: Understanding Data Visualizations via Question Answering. IEEE Western NY Signal Processing Workshop.

Acharya, M., Kafle, K., Kanan, C. (2018) TallyQA: Answering Complex Counting Questions. IEEE Western NY Signal Processing Workshop.

Kafle., K., Cohen, S., Price, B., Kanan, C. (2018) DVQA: Understanding Data Visualizations via Question Answering. CVPR-2018 Workshop on Language and Vision.

Hayes, T., Kemker, R., Cahill, N., Kanan, C. (2018) New metrics and experimental paradigms for continual learning. CVPR-2018 Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision Workshop.

Kapisthalam, S., Kanan, C., Fedorovskaya, E. (2017) Analyzing eye movements using multi-fixation pattern analysis with deep learning. 12th Annual Women in Machine Learning Workshop at NeurIPS-2017. Long Beach, CA.

Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C. (2017) Measuring Catastrophic Forgetting in Neural Networks. IEEE Western NY Signal Processing Workshop. [Best Poster Award Winner]

Kafle, K., Kanan, C. (2017) An analysis of visual question answering algorithms. IEEE Western NY Signal Processing Workshop.

Kafle, K., Kanan, C. (2017) An analysis of visual question answering algorithms. ICCV-2017: Second workshop on closing the loop between vision and language. Venice, Italy.

Kafle, K., Kanan, C. (2017) An analysis of visual question answering algorithms. CVPR-2017 Workshop on Language and Vision. Honolulu, HI.

Kafle, K., Kanan, C. (2017) An analysis of visual question answering algorithms. CVPR-2017 Workshop on Visual Question Answering. Honolulu, HI.

Diaz, G., Bailey, R., Kanan, C., Lipson, M., Pelz, J., Kothari, R. (2017) Data-driven Gaze Event Classification for the Analysis of Eye and Head Coordination By Natural Task. ECEM-2017.

Binaee, K., Starynska, A., Kothari, R., Kanan, C., Pelz, J., Diaz, G. (2017) Modeling hand-eye movements in a virtual ball catching setup using a deep recurrent neural networks. Vision Sciences Society (VSS-2017).

Kothari, R., Binaee, K., Bailey, R., Kanan, C., Diaz, G., Pelz, J. (2017) Gaze-in-world movement classification for unconstrained head motion during natural tasks. Vision Sciences Society (VSS-2017).

Kafle, K., Kanan, C. (2016) Answer-Type Prediction for Visual Question Answering. CVPR-2016 Visual Question Answering Workshop.

Kanan, C., Kafle, K. (2016) Answer-Type Prediction for Visual Question Answering. Vision Sciences Society Annual Meeting (VSS 2016).

Kanan, C., Bseiso, D., Ray, N., Hsiao, J., Cottrell, G. (2014) Predicting an Observer’s Task Using Multi-Fixation Pattern Analysis. 21st Joint Symposium on Neural Computation. UC Irvine.

Kanan, C. (2013) Image, Sound, and Odor Classification with Gnostic Fields. Society for Neuroscience (SFN 2013).

Chukoskie, L., Kanan, C., Albrecht, K., Wiles, J., Townsend, J. (2013) Comparing Saccade Sequences in Typical and Autistic Children. Society for Neuroscience (SFN 2013).

Kanan, C. (2013) Recognizing Sights, Smells, and Sounds with Gnostic Fields. 17th International Conference on Cognitive and Neural Systems (ICCNS). Boston University.

Kanan, C. (2013) Recognizing Sights, Smells, and Sounds with Gnostic Fields. 20th Joint Symposium on Neural Computation. Caltech.

Kanan, C. (2013) Recognizing Sights, Smells, and Sounds with Gnostic Fields. Jacobs Research Expo 2013. [Semi-Finalist in Best Poster Competition]

Kanan, C., Cottrell, G. W. (2012) A Neural Network Model of the Primate Visuo-Motor System. Computational and Systems Neuroscience (COSYNE 2012).

Chukoskie, L., Miller, M., Kanan, C., Dorai, M., Townsend, J., Trauner, D. (2012) Did you see that change? A study of dyspraxia, eye movement, and visual perception in autism. International Meeting for Autism Research (IMFAR-2012).

Kanan, C. (2011). A Training Program in Grantsmanship. NSF Science of Learning Center 2011 PI Meeting.

Kanan, C., Chukoskie, L., Sejnowski, T. (2011) Shifting from a Stimulus-driven to a Task-driven Saccadic Policy. 18th Joint Symposium on Neural Computation.

Kanan, C., Cottrell, G. W. (2011) Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. Jacobs Research Expo 2011. [Semi-Finalist in Best Poster Competition]

Kanan, C., Cottrell, G. W. (2010) Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. Society for Neuroscience (SFN 2010).

Cottrell, G., Kanan, C. (2010) Robust Object and Face Recognition Using a Biologically Plausible Model. Vision Sciences Society Annual Meeting (VSS 2010).

Kanan, C., Cottrell, G. W. (2009) Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. NSF Science of Learning Center 2009 PI Meeting.

Tong, M.H., Kanan, C., Zhang, L., Cottrell, G. (2009) Task-driven Saliency Using Natural Statistics. Vision Sciences Society Annual Meeting (VSS 2009).

Tong, M.H., Kanan, C., Zhang, L., Cottrell, G.W. (2009) Task-driven Saliency Using Natural Statistics (SUN). MIT Scene Understanding Symposium.

Tong, M. H., Kanan, C., Zhang, L., Cottrell, G. W. (2009) Task-driven saliency using natural statistics (SUN). Computational and Systems Neuroscience (COSYNE 2009).

Kanan, C., Tong, M. H., Zhang, L., Cottrell, G. W. (2008) SUN: Top-down saliency using natural statistics. NSF Science of Learning Center 2008 PI Meeting.

INVITED & CONTRIBUTED TALKS

Kanan, C. (2020) Rethinking continual learning: How to define success. CVPR Workshop on continual learning in computer vision (CLVISION). Seattle, WA (Virtual).

Kanan, C. (2020) Clinical grade AI for computational pathology. CVPR Workshop on Medical Computer Vision (MCVW). Seattle, WA (Virtual).

Kanan, C., Sue, J. (2019) What is Clinical-Grade AI? Digital Pathology & AI Congress. London, United Kingdom.

Kanan, C. (2019) What’s Wrong with Deep Lifelong Learning and Visually Grounded Language Models and How to Fix Them. NASA Jet Propulsion Laboratory. Pasadena, CA.

Kanan, C. (2019) Incremental Learning in Deep Neural Networks using Memory Replay. SUNY Buffalo.

Kanan, C. (2019) Deep Neural Networks for Lifelong Learning and Goal-Driven Computer Vision. Cornell Tech. New York, NY.

Cooper, E., Kanan, C., & Issa, E. (2018) How do we maximize insights gained about vision across animal models, computational models, and humans? Conference on Cognitive Computational Neuroscience.

Kanan, C. (2018) Making Deep Learning More Versatile. NVIDIA.

Kanan, C. (2017) Making Computer Vision More Flexible. Memorial Sloan Kettering Cancer Center, New York City. December, 2017.

Kanan, C. (2017) What does solving VQA mean? ICCV-2017: Second workshop on closing the loop between vision and language. Venice, Italy. October, 2017.

Kanan, C. (2017) An Analysis of Visual Question Answering Algorithms. Center for Nonlinear Studies, Los Alamos National Laboratory.

Kanan, C. (2016) An Analysis of Visual Question Answering Algorithms. Department of Computer Science, University of Rochester.

Kanan, C. (2016) An Analysis of Visual Question Answering Algorithms. Western NY Image and Signal Processing Workshop (WNYISPW-2016).

Kanan, C. (2016) Deep Learning: Overview & Applications. Kodak Alaris.

Kanan, C. (2015) Gnostic Fields for Image Recognition, Active Vision, and Tracking. Xerox PARC.

Kanan, C. (2014) Gnostic Fields for Object Classification. NASA Jet Propulsion Laboratory.

Kanan, C. (2014) Image Recognition and Active Vision in Humans and Machines. Rochester Institute of Technology.

Kanan, C. (2012) Recognizing Sights, Smells, and Sounds with Gnostic Fields. 25th Meeting of the Perceptual Expertise Network, Austin, TX.

Kanan, C. (2011) Recognizing Objects, Faces, and Flowers using Fixations. Vision Sciences Society Annual Meeting (VSS 2011), Naples, FL.

Kanan, C. (2010) Image Recognition Using Fixations. The 2010 Inter-Science of Learning Conference, Boston, MA.

Kanan, C. (2010) Recognizing Objects Using Fixations. Kavli Institute for Brain and Mind Symposium, San Diego, CA.

Kanan, C. (2009) SUN: Top-down saliency using natural statistics. The 2009 Inter-Science of Learning Conference, Seattle, WA.

OTHER PUBLICATIONS

Kanan, C. (2012) Turing: Beyond the original concept. Nature, 483: 275.

POSTDOCTORAL ADVISING

Ashish Gupta, PhD Project: Media Anti-Forensics Outcome: Research Associate, Case Western University

2017 – 2019

PHD ADVISING

Kushal Kafle, PhD, Imaging Science, RIT Project: Algorithms for Visual Question Answering Outcome: Research Scientist, Adobe Research

2015 – 2020

Ronald Kemker, PhD, Imaging Science, RIT Projects: Lifelong Learning; Low Shot Semantic Segmentation with Deep Learning Outcome: Branch Chief, US Air Force

2015 – 2018

Tyler Hayes, PhD (in progress), Imaging Science, RIT Project: Continual Machine Learning

2017 – Present

Usman Mahmood, PhD (in progress), Imaging Science, RIT Project: Deep Learning for Medical Imaging

2017 – Present

Robik Shrestha, PhD (in progress), Imaging Science, RIT Project: Visuo-Linguistic Concept Learning

2017 – Present

Ryne Roady, PhD (in progress), Imaging Science, RIT Project: Lifelong Learning

2017 – Present

Manoj Acharya, PhD (in progress), Imaging Science, RIT Project: Grounded Image Understanding & Open-Ended Visual Counting

2017 – Present

Aneesh Rangnekar, PhD (in progress), Imaging Science, RIT Project: Deep Learning for Tracking and Remote Sensing Co-advised with Matt Hoffman

2017 – Present

MS THESES & CAPSTONES ADVISED Underline denotes co-authored a paper together.

Zhongchao Qian, MS, Imaging Science, RIT Thesis: Exploring fixed-output matrices

2018 – 2020

Navneet Sinha, MS, Computer Science, RIT Project: Goal Driven Detection in Natural Scenes Outcome: Software Development Engineer, Amazon

2017 – 2018

Deepak Sharma, MS, Computer Science, RIT Project: Efficient processing of high-resolution imagery Outcome: Software Engineer, iRobot

2016 – 2019

Karan Jariwala, MS, Computer Science, RIT Project: Advancing Visual Query Detection Outcome: Software Development Engineer, Amazon

2017 – 2018

Tania Kleynhans, MS, Imaging Science, RIT Project: Deep Learning for Predicting Top-of-Atmosphere Thermal Radiance Outcome: Associate Scientist, Rochester Institute of Technology

2016 – 2017

SENIOR PROJECTS SUPERVISED

Adam Casson, BS, Imaging Science, RIT Project: Creating a New Dataset and Algorithms for Video Question Answering

2016 – 2017

Bijia Chen, BS, Imaging Science, RIT Project: Active Vision for Visual Question Answering

2016 – 2017

INTERN & NON-THESIS PROJECTS SUPERVISED Underline denotes co-authored a paper together.

Total Interns and Non-Thesis Students Supervised: 43

Rochester Institute of Technology Streaming Open Set Classification, 2019 Student: David Munechika (high school) New Datasets for Streaming Learning, 2019 Students: Hitesh Ulhas Vaidya (MS) Improving Fine-Tuning for Low-Shot Learning, 2018 – 2019 Students: Sophia Kotok (PhD) Deep Learning Investigations, 2018 Students: Terrell Byrd (high school) Continual Learning for Visual Question Answering, 2018 Students: Michael Geraci (high school) Visual Query Detection, 2017 – 2018 Students: Aditya Kunwar (PhD), Kshitij Bichave (MS) Deep Learning for Ecology, 2017 – 2018 Students: Frank Cwitkowitz (MS)

Lifelong Machine Learning in Deep Neural Networks, 2017 – Present Students: Marc McClure (BS), Angelina Abitino (BS), Ayesha Gonzalez (BS)

Testing Invariance in Deep Convolutional Neural Networks, 2017 Student: Anjali Jogeshwar (MS) Deep Learning for Chess, 2017 Student: Samuel Ragusa (BS) Remote Sensing using Neural Networks, 2017 Students: Nate Brunacini (high school), Ryan Luu (high school) Deep Reinforcement Learning for Control, 2016 – 2019 Student: Rodney Sanchez (BS)

Accelerating Visual Question Answering, 2015 – 2018 Students: Justin Namba (BS), Ramesh Nair (MS), Utkarsh Deshmukh (MS), Aravindh Kuppusamy (MS)

Predicting which Images are Interesting, 2015 Students: Arjun Raj Rajanna (MS)

Deep Learning for Tracking, 2015 – 2017 Students: Mohammed Yousefhussien (PhD)

Paige.AI, Inc. Detecting Breast Cancer Mutations to Guide Treatment using Deep Learning, 2020 Interns: Clement Grisi (MS)

Cornell Tech Calibrated Uncertainty Representations in Neural Networks, 2019 Students: Nabeel Seedat (MS)

Deep SMOTE for Biased Datasets, 2019 Students: Anilkumar Vadali (MS), Emmanuel Cruz (MS), Manuel Viejo (MS)

University of Rochester Advancing multi-label and multi-task classification, 2020 Students: Yipeng Zhang (BS)

Predicting Diabetic Retinopathy Severity using Machine Learning, 2018 Students: Ricky Su (BS), Yihe Yang (MS), Xuexun Xiao (MS)

NASA’s Jet Propulsion Laboratory Evaluation of tracking algorithms in aerial imagery, 2015 Intern: Maya Rau-Murthy (BS)

Smooth Pursuit - Vehicle Tracking in Aerial Video, 2015 Interns: Victor Kwak (BS), Homam Chamas (BS), Samuel Munoz (BS), Emelie Oiknine (BS)

Multi-Modal Object Recognition: Transfer Learning from RGB to IR Imagery, 2014 Interns: Mabel Zhang (PhD), Jean Choi (BS)

Segmenting Land, Sky, and Sea in Maritime Imagery, 2014 Intern: Juan Diego Palomino (BS)

University of California at San Diego (UCSD) Eye tracking analysis using Multi-Fixation Pattern Analysis, 2012 – 2013 Interns: Dina Bseiso (BS), Nicholas Ray (BS)

Scene classification using active vision, 2012 – 2013 Intern: Felix Schüler (BS)

Mobile Eye Tracking, 2008 – 2009 Intern: Jonathan Wosen (high school)

TEACHING CS 5787 @ Cornell (Cornell Tech Campus in NYC) – Deep Learning Sp2019, Sp2020 • Graduate Level. Covers feed-forward neural networks, CNNs, RNNs, transfer learning,

regularization, GANs, etc. This large course (110 students in 2020) is a mix of theory and applications. It also covers issues in AI fairness, ethics, and deployment.

IMGS 684/789 @ RIT – Deep Learning for Vision Fa2016, Fa2017, Fa2018 • Graduate Level. Covers basic and advanced topics in deep learning, including CNNs, RNNs, transfer

learning, regularization, etc. It devotes considerable time to training students on how to prepare papers for top AI publication venues, e.g., NeurIPS and CVPR.

IMGS 682 @ RIT – Image Processing and Computer Vision Sp2016, Sp2017, Sp2018 • Graduate Level. Covers the basics of supervised machine learning (nearest neighbor, linear

classifiers, SVM, neural networks, etc.), unsupervised learning (PCA, k-means, mean shift, etc.) image processing, and computer vision (convolutional neural networks, image recognition, object detection, homographies, image stitching, target tracking, activity recognition in video, segmentation, semantic segmentation, etc.).

JOURNAL & CONFERENCE REVIEWING

Cerebral Cortex Journal of Vision (JoV) Neural Info. Processing Systems (NeurIPS) PLoS ONE Visual Cognition Optics and Lasers in Engineering Physical Review Letters Biological Cybernetics Neural Networks VOCVLC Workshop ECCV SiVL Workshop Int. Conf. on Computer Vision (ICCV) EMNLP Euro. Conf. on Computer Vision (ECCV) Pattern Recognition

Computer Vision and Pattern Recognition (CVPR) Int. Conf. Robotics and Automation (ICRA) Journal of Machine Learning Research (JMLR) IEEE Trans Pat. Analysis Machine Intelligence (TPAMI) IET Image Processing Cognitive Science Society (CogSci) Journal of Imaging Science and Technology IEEE Winter Applications of Computer Vision (WACV) Eye Tracking Research and Applications (ETRA) AAAI CVPR Workshop on EgoCentric Vision IEEE Transactions on Image Processing (TIP) Nature Medicine Association for Computational Linguistics (ACL) CVPR Workshop on Continual Learning in CV

PROFESSIONAL SERVICE

NSF Review Panelist, 2020 Reviewer, Poland National Science Centre, 2020 NSF Review Panelist, 2019 AAAS Reviewer for KSA International Collaboration Grants, 2019 Organizing Committee, IEEE Western NY Image and Signal Processing Workshop (WNYISPW), 2019 Organizing Committee, Workshop on the Shortcomings in Vision and Language (SiVL) @ NAACL-2019. Organizing Committee, IEEE Western NY Image and Signal Processing Workshop (WNYISPW), 2018 Reviewer, Veni Grant for Netherlands Organisation for Scientific Research (NWO), 2018 NSF Review Panelist, 2018 Organizing Committee, IEEE Western NY Image and Signal Processing Workshop (WNYISPW), 2017 Organizing Committee, ICCV Workshop on Mutual Benefits of Cognitive and Computer Vision, 2017

Area Chair, IEEE International Conference on Image Processing (ICIP), 2017 Organizing Committee, IEEE Western NY Image and Signal Processing Workshop (WNYISPW), 2016 General Chair, Fifth NSF Inter-Science of Learning Center Conference (iSLC), 2012 Executive Committee Member, NSF Temporal Dynamics of Learning Center, 2009 – 2012 Organizer, NSF Temporal Dynamics of Learning Center Small Grants Program Workshop Chair, NSF Inter-Science of Learning Center Conferences, 2011 Workshop Chair, NSF Inter-Science of Learning Center Conferences, 2010 Fellow & Trainee Chair, NSF Temporal Dynamics of Learning Center, 2009 – 2012 Workshop Chair, NSF Inter-Science of Learning Center Conferences, 2009

PROMOTION OF DIVERSITY & INCLUSION

NSF LSAMP/McNair Scholars Programs at RIT 2015 – Present Helped revise curriculum and training for underrepresented students to enhance their research opportunities and skills in order to help them get into PhD programs and excel once admitted. Supervising two disadvantaged undergraduate students from underrepresented groups in machine learning projects. Since 2018, serving as a member of the program’s advisory board.

California Forum for Diversity in Graduate Education 2009, 2010, 2011, 2012, 2013 Invited to speak with underrepresented minorities attending California colleges about how to get

accepted into and succeed in graduate school.

Preuss School Internship Supervisor 2008 – 2009 Supervised and mentored research projects conducted by three students from the Preuss School, a

charter school devoted to preparing low-income students for college. All three students went to college, and one received a PhD from Stanford University in 2019.

Going for the Goal 2005 – 2006 Mentored English as a second language (ESL) students at Camino Nuevo, a middle school in downtown

Los Angeles. Encouraged them to attend college by alleviating their misconceptions and anxieties.

University of Southern California Parkside Area Government 2004 – 2006 Created a student program called “Small World” aimed at breaking cultural stereotypes. “Small World:

Afghanistan” was awarded Best Diversity Program of October 2005. CITIZENSHIP United States of America