"If we knew what it was we were doing, it would not be called research, would it?" -- Albert Einstein.

About

I do research in the intersection of statistics, differential geometry and deep learning. I am a senior machine learning scientist at Butlr, CA. I was an applied Scientist at Amazon Lab126. I was a member of the CVGMI group at University of Florida (UF).

I joined UF–Gainesville in 2013 and finished my PhD in April 2018, where I was advised by Baba Vemuri. After my PhD, I continued my research at UF as a postdoctoral associate for 6 months and then spend 1.5 years at UC Berkely.

Research

See my research agenda for details on my current work. I am always looking for collaborations. If you're interested in working with me, email me.

Publications

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis
Rudrasis Chakraborty, Zihang Meng, Vikas Singh
Neural Information Processing Systems, 2021
paper

A Variational Approximation for Analyzing the Dynamics of Panel data
Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya Ravi and Vikas Singh
37th Conference on Uncertainty in Artificial Intelligence (UAI 2021)
paper | code

Simpler Certified Radius Maximization by Propagating Covariances
Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Oral)
paper | code

Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Association for the Advancement of Artificial Intelligence (AAAI) 2021
paper | code

Flow-based Generative Models for Learning Manifold to Manifold Mappings
Xingjian Zhen, Rudrasis Chakraborty, Lucy Yang, Vikas Singh
Association for the Advancement of Artificial Intelligence (AAAI) 2021
paper

VolterraNet: A higher order convolutionalnetwork with group equivariance forhomogeneous manifolds
Rudrasis Chakraborty, Monami Banerjee, Jose Bouza, Baba C. Vemuri
IEEE Trans. on Pattern Analysis and Machine Intelligence 2020
paper

SurReal: Complex-Valued Learning as PrincipledTransformations on a Scaling and Rotation Manifold
Rudrasis Chakraborty, Yifei Xing and Stella Yu
IEEE Trans. on Neural Network and Learning Systems 2020
paper | code

ManifoldNet: A Deep Neural Network for Manifold-valued Data with Applications
Rudrasis Chakraborty, Jose Bouza, Jonathan Manton and Baba C. Vemuri
IEEE Trans. on Pattern Analysis and Machine Intelligence 2020
paper | code

Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning
Rudrasis Chakraborty, Liu Yang, Soren Hauberg and Baba C. Vemuri
IEEE Trans. on Pattern Analysis and Machine Intelligence 2020
paper

Orthogonal Convolutional Neural Networks
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu
Computer Vision and Pattern Recognition (CVPR) 2020
paper | code

Dilated Convolutional Neural Networks (DCNN) for Sequential Manifold-valued Data in Neuroimaging
Rudrasis Chakraborty, XIngjian Zhen and Vikas Singh
International Conference of Computer Vision (ICCV) 2019
paper | code

Statistical Recurrent Models on Manifold valued data
Rudrasis Chakraborty, Chun-Hao Yang*, Xingjian Zhen*, Monami Banerjee, Derek Archer, David Vaillancourt, Vikas Singh and Baba C. Vemuri
Neural Information Processing Systems (NeurIPS) 2018
paper | code

For the entire publication list see here

Students Mentored

Xingjian Zhen, Steven Kan, Eric Huang, Yifei Xing, Yining Liu, Minxuan (Alice) Duan, Shixuan (Wayne) Li, Amira Dinari (and others)

Teaching

Please see my teaching statement.
F14,15,16 CAP 5416: Computer Vision

Reviewer Services

15- CVPR, ICLR, ICCV, MICCAI, ECCV, NIPS, ACCV, BMVC, ICML, AAAI
16- IEEE TPAMI, IEEE TIP, TSMC-B, TNNLS, IJCV, IEEE TSP, IEEE JBHI