Vishnu Lokhande
Email:lokhande [at] cs [dot] wisc [dot] edu

Hello there! Let's begin with a quote,
"What I cannot create, I do not understand" - Richard Feynman

I am a PhD candidate studying computer vision at the University of Wisconsin-Madison . My interests revolve around constrained and stochastic optimization problems and their applications to Computer Vision. I am very fortunate to be advised by Prof. Vikas Singh .

I am also lucky to have been associated with the following wonderful mentors: Julian Yarkony (Verisk), Sathya Ravi (UIC), Vibhav Vineet (MSR), Rudra Chakraborty (Amazon), Kihyuk Sohn (Google).

Prior to my graduate studies, I received my bachelor's degree in Electrical Engineering from IIT Kanpur where I was advised by Prof. Laxmidhar Behera.

CV (updated Mar'22)  /  Github /  Scholar /  Twitter






News

    [Jun 2022] Attending CVPR in-person in New Orleans. Please reach out to chat about research or brainstorm ideas to collaborate!
    [Jun 2022] A preliminary version of our work on Group Robustness in presence of Partial Labels will now appear at the Workshop on Spurious Correlations, Invariance and Stability at ICML 2022
    [May 2022] Internship with the wonderful Google Brain STAR team! It's in-person in MTV California.
    [Apr 2022] Thankful to the Alzheimer's Association for providing me with a Conference Fellowship to attend AAIC 2022.
    [Apr 2022] Honored to be selected for CVPR 2022's Doctoral Consortium.
    [Mar 2022] Our paper on Handling Nuisance Attributes when Pooling Datasets was accepted at CVPR 2022.
    [Feb 2022] Invited to give a talk at the 17th CSL conference UIUC. [slides]
    [Oct 2021] Participated in 2021 CRCNS Meeting CRCNS Meeting.
    [Jun 2021] Invited to participate in Morgridge Entrepreneurial Bootcamp MEB 2021.
    [May 2021] Proud to be an Outstanding reviewer of CVPR 2021 .
    [May 2021] Our paper on Graph Reparameterization in Bayesian Deep Neural Networks was accepted at UAI 2021.
    [Apr 2021] Our abstract on dataset harmonization algorithms was accepted at AAIC 2021.
    [Apr 2021] Our workshop paper on Diffeomorphism invariant neural networks was accepted at DiffCVML 2021.
    [Feb 2021] Invited to give a talk at my alma mater IIT Kanpur on optimization methods in computer vision QIP IITK.
    [Jan 2021] Internship with Google Research, Sunnyvale at my home (again!) .
    [Dec 2020] Our paper on learning invariant representations was accepted at AAAI 2021.
    [Nov 2020] Honored to be selected as a finalist for Microsoft Research PhD Fellowship 2021.
    [Oct 2020] Proud to be a Top 10% of high-scoring reviewers of NeurIPS 2020.
    [Sep 2020] Invited to give a talk on optimization methods for fairness in vison at Utah Data Science Seminar Series. [slides]
    [Jul 2020] Invited to give a talk on our FairALM project at the Princeton Fairness Reading Group.
    [Jul 2020] Our paper on fairness in computer vision - FairALM was accepted at ECCV 2020. [arXiv]
    [Feb 2020] Internship with Microsoft Research, Redmond at my home.
    [Feb 2020] Our Paper on Hermite Polynomial Activations for semi-supervised learning was accepted at CVPR 2020. [arXiv]
    [Nov 2019] Our paper Column Generation Techniques for Entity Resolution received Oral Acceptance at the AAAI 2020. [arXiv]
    [Oct 2019] Our paper on Active Learning with Importance Sampling was accepted to the NeurIPS 2019 Workshop Machine Learning with Gaurantees. [arXiv]
    [Jun 2019] Our paper on Hermite Polynomial Activations for semi-supervised learning received Best Student Paper Award at MMLS 2019.
    [May 2019] Research Internship at Verisk AI Labs.
    [Feb 2019] Passed PhD Qualifying Exam in Machine Learning with a High Pass (P+) grade.
    [Dec 2018] Our paper on Constrained Deep Learning using Conditional Gradient method received Oral Acceptance at AAAI 2019.
    [Dec 2018] Student Volunteer at NeurIPS 2018.
    [Jul 2018] Attended summer school on Fundametals of Data Analysis.
    [Jun 2018] Our paper on "Constrained Deep Learning using Conditional Gradient method" received Spotlight Presentation at MMLS 2018.
    [Jun 2018] Organized Reading Group on Deep learning Theory and Practice.

Publications

Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets
Vishnu Suresh Lokhande, Rudra Chakraborty, Sathya N. Ravi, Vikas Singh
[arXiv] [Code] [Video]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-22)


Towards Group Robustness in the presence of Partial Group Labels
Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell,
Chen-Yu Lee, Tomas Pfister
[arXiv] [Slides] [Code]
Workshop on Spurious Correlations, Invariance and Stability at ICML 2022

Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks
Jurijs Nazarovs, Ronak R. Mehta, Vishnu Suresh Lokhande, Vikas Singh
[Video] [Code]
Thirty-seventh Conference on Uncertainty in Artificial Intelligence (UAI-21)

Constrained Harmonization Algorithm for Pooling Multi-site Datasets
Vishnu Suresh Lokhande, Akshay Mishra, Kersten Diers, Emrah Duzel, Martin Reuter, Barbara Bendlin, Vikas Singh
[Slides] [Poster] [Video]
2021 Alzheimer's Association International Conference. (AAIC-21)

Learning Invariant Representations using Inverse Contrastive Loss
Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh
[Poster] [Code] [Slides]
Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)

FairALM: An Augmented Lagrangian Method for Training Fair Models with Little Regret
Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N. Ravi, Vikas Singh
[Video] [Slides] [Code]
16th European Conference on Computer Vision (ECCV-20)

Generating Accurate Pseudo-labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations
Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh,
Sathya N. Ravi, Vikas Singh
[Video] [Slides] [Code]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR-20)
Also, Best Student Paper Award at MMLS 2019

Accelerating Column Generation via Flexible Dual Optimal Inequalities with Application to Entity Resolution
Vishnu Suresh Lokhande, Shaofei Wang, Maneesh Singh, Julian Yarkony
[Code] [Poster] [Slides]
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) (Oral Presentation)

Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
Sathya N. Ravi, Tuan Dinh, Vishnu Suresh Lokhande, Vikas Singh
[Code] [Poster]
Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) (Oral Presentation)
Also, Spotlight Presentation at MMLS 2018

Useful Links


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