Devinder Kumar

PhD Candidate, UWaterloo & AI scientist in residence @NEXTAI

My research centers around Deep Learning and its application in Computer Vision.

I am a first year PhD student at VIP lab - University of Waterloo and Machine Learning Research Group - University of Guelph where I am supervised by Dr. Alexander Wong (UWaterloo) and Dr. Graham Taylor (UGuelph). Before this, I completed my MASc. in WAVE lab & VIP lab at University of Waterloo, where I worked with Dr. Steven Waslander and Prof. David Clausi.

Previous to joining UWaterloo, I was a research engineer in LIP-6 - UPMC-Sorbonne University, Paris. There I had great time working with Prof. Matthieu Cord and Asst. Prof Nicolas Thome

June 2017: I am giving an invited Keynote Speech at AI-Toronto, 2017 Conference on 21st June, 2017
May 2017: "CLEAR visualization" paper accepted at CVPR Workshop (CVPR-W) on Explainable Computer Vision
Apr 2017: Accepted at DL & RL Summer School, 2017 at MILA, UMontreal with CIFAR scholarship.
Mar 2017: Paper accepted at 14th International Conference on Image Analysis and Recognition (ICIAR, 2017)
Feb 2017: I am joining this year's NextAI program as an AI scientist in residence.
Dec 2016: I will be giving an invited talk at the theory department at Fritz Haber Institute of the Max Planck Society on 08 Dec, 2016
Nov 2016: Paper accepted at 30th Neural Information Processing Systems Machine learning for healthcare Workshop (NIPS MLH, 2016)
June 2016: I will be joining Philips Research (HQ) as a deep learning intern at Eindhoven, NL where I will be working with the data science and medical oncology solutions departments.
May 2016: I gave an invited talk at Gustave Roussy cancer research institute, Paris, France
Nov 2015: Paper accepted at 29th Neural Information Processing Systems (NIPS) Machine Learning for Healthcare Workshop ,2015
Sept 2015: I am doing an internship at Aimetis Corp. for the fall term
May 2015: Paper accepted at IEEE International confernece of Multimedia and Expo workshop (ICME) 2015
Feb 2015: Paper accepted at Field and Service Robotics (FSR) 2015

2017

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

D.Kumar, A. Wong, and G. W. Taylor

Computer Vision and Pattern Recognition Workshop (CVPR-W) Accepted, 2017

2016

Understanding Anatomy Classification Using Visualization

D.Kumar, and V.Menkovski

30th NIPS Machine Learning for Health (NIPS-MLH) Workshop, 2016

2015

Discovery Radiomics via StochasticNet Sequencers for Cancer Detection

M.J.Shafiee, A.G. Chung, D. Kumar,, F. Khalvati, M.A. Haider and A. Wong

29th NIPS Machine Learning for Healthcare (NIPS-MLH) Workshop, 2015

2015

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Recipe recognition with large multimodal food dataset

X.Wang, D.Kumar, N.Thome, M.Cord, and F.Precioso

7th Workshop on Multimediafor Cooking and Eating Activities (CEA) in conjuction with ICME, July 2015.

2015

Taming the North: Multi-Camera Parallel Tracking and Mapping in Snow-Laden Environments

A.Das, D.Kumar, A.E.Bably and S. Waslander

In proceedings Field and Service Robotics (FSR), June, 2015.

2015

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Lung Nodule Classification Using Deep Features in CT Images (Oral)

D. Kumar, A. Wong and D. Clausi

12th Conference on Computer and Robot Vision (CRV), Halifax, NS, June, 2015.

2013

Best Paper Award (3rd Position-Undergrad) in IEEE Region 10 Paper Contest

Constructive Learning for Human-Robot Interaction

A.Singh, S.Karanam, and D.Kumar

IEEE Potentials, Vol. 32, Issue: 4, pp(s): 13-19, Aug, 2013

VISIIR web demo Food recognition system based on the work that I did at LIP-6, Paris.

More projects can be found on my GitHub profile.

UPMC-101 Dataset of 101 food categories (~1000 images per category) that I created from Google image search results.

Under Construction!!