Saurabh Sharma

About me

I'm a PhD student at the University of California Santa Barbara in beautiful Santa Barbara. My background is in Computer Science, and my interests lie in Computer Vision and more recently Graph Machine Learning. My research focuses on dataset imbalance, distribution shifts, and learning with limited data. I'm advised at UCSB by Ambuj Singh. Prior to this, I was in Bernt Schiele's Computer Vision and Machine Learning group at MPI Informatics, Saarbruecken, where I got my MSc in Computer Science. I also interned with Arjun Jain's 3D Vision group at IIT Bombay in the Summer of 2018.

News

[02/2023] Our work on Learning Prototype Classifiers for Long-Tailed Recognition is available as a preprint on ArXiv.
[01/2023] I TAed the Introduction to Computer Vision course offered in Winter 23 at UCSB.
[11/2022] I attended the Neural Information Processing Systems Conference (NeurIPS), 2022 in New Orleans, Louisiana.
[09/2022] I finished my Applied Scientist internship at Amazon Science, San Diego.
[04/2022] I TAed the Introduction to Computational Science course offered in Spring 22 at UCSB.
[01/2022] I TAed the Machine Learning course offered in Winter 22 at UCSB.
[10/2021] I TAed the Capstone course for Data Science offered in Fall 21 at UCSB.
[08/2021] I attended the Virtual International Conference on Knowledge Discovery and Data Mining conference (KDD), 2021.
[04/2021] I attended the Virtual Siam International Conference on Data Mining (SDM), 2021.
[10/2020] I started my PhD in Computer Science at the University of California Santa Barbara, Santa Barbara, USA.
[10/2020] I got my MSc in Computer Science from Saarland University, Saarbruecken, Germany.
[09/2020] I presented our work on Long-Tailed Recognition using Class-Balanced Experts at the Virtual German Conference on Pattern Recognition (GCPR) 2020.
[09/2020] Our code for Long-Tailed Recognition using Class-Balanced Experts is released.
[08/2020] Our work on Long-Tailed Recognition using Class-Balanced Experts is accepted to the German Conference on Pattern Recognition (GCPR) 2020.
[10/2019] We presented our work on Monocular 3d human pose estimation by generation and ordinal ranking at the International Conference on Computer Vision (ICCV), 2019 in Seoul, South Korea.
[07/2019] Our work Monocular 3d human pose estimation by generation and ordinal ranking is accepted to the International Conference on Computer Vision (ICCV), 2019.
[06/2019] We presented our work f-vaegan-d2: A feature generating framework for any-shot learning at the Computer Vision and Pattern Recognition (CVPR) conference, 2019 in Long Beach, California, USA.
[03/2019] Our work f-vaegan-d2: A feature generating framework for any-shot learning is accepted to the Computer Vision and Pattern Recognition (CVPR) conference, 2019.

Publications