Ajay Jaiswal's Homepage

Ajay Jaiswal

I am a Ph.D. student at the School of Information, UT Austin. I am fortunate to be advised by Prof. Ying Ding, and Prof. Atlas Wang. I am a member of Visual Informatics Group (VITA) @UT Austin. My current research focuses on Sparse Neural Network Training, Computer Vision, and Medical Imaging.

Prior to joining UT Austin, I completed my Masters in Computer Science from Indian Institute of Technology, Kharagpur. I was advised by Prof. Animesh Mukherjee and Prof. Pawan Goyal at the CNeRG Lab. I have also spent 3 wonderful years as Lead Research Engineer at Advanced Technology Lab, Samsung Electronics, India under the supervision of Dr. Vijay Narayan Tiwari.

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Blog

News

07/2022 One paper accepted to ECCV 2022.
06/2022 Spending Summer 2022 in Adobe Research as Computational Photography/Computer Vision Intern.
05/2022 One paper accepted to ICML 2022 (Spotlight).
01/2022 Awarded Professoional Development Fellowship by School of Information, UT Austin
08/2021 One paper accepted to ML4H 2021.
07/2021 One paper accepted to ICDM 2021.

Selected Publication [Full List]

(A superscript * denotes equal contribution)

Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

Zhiyuan Mao*, Ajay Jaiswal*, Atlas Wang, Stanley Chan

European Conference on Computer Vision (ECCV), 2022

We collect and present two new real-world turbulence datasets along with a physics-inspired transformer model for imaging through atmospheric turbulence.
[Paper] [Code]
[Spotlight] Training Your Sparse Neural Network Better with Any Mask

Ajay Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Ying Ding, Atlas Wang

International Conference on Machine Learning (ICML), 2022

We present a sparse subnetwork training toolkit to imporve the training of subnetworks identified by any static pruning methods (SNIP, GRaSP, LTH, SynFlow, Random).
[Paper] [Code]
Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata

Ajay Jaiswal, Tianhao Li, Cyprian Zander, Yan Han, Justin Rousseau, Yifan Peng, and Ying Ding

IEEE International Conference on Data Mining (ICDM), 2021

We present novel augmentation method based on patient metadata and extend self-supervised contrastive learning framework for cardiopulmonary disease classificaton.
[Paper] [Code]
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification

Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin Rousseau, Yifan Peng, and Ying Ding

Machine Learning for Health (ML4H), 2021

We present a constastive learning pre-training framework for Bio-BERT on Radiology Report to capture factual critical information.
[Paper] [Code]

Teaching & Service

Conference Reviewer of ICME 2022, ICDM 2021, KDD 2022
Teaching Assistant - OS programming and ML/DS Programming [IIT Kharagpur]

Contact

Address

UTA 5.410
1616 Guadalupe St
Austin, TX
United States
78712

Email

Academic
ajayjaiswal [at] utexas.edu

Other Use
ajayjaiswalhi [at] gmail.com

About Me

Born is a small town (Belthara) in Eastern UP, India, I got my hands on a computer first time during my undergraduate freshman year. My journey in computer science began when I was sixteen year old with a fantastic book "Let Us C" by Yashavant Kanetkar. During my career, I have always tried to make best use of resources and smart people around me to learn and grow. I identified my research potential while working with Prof. Animesh Mukherjee in IIT Kharagpur and later spent some wonderful time in Samsung Research after graduating. Currently, with the grace of GOD, I am very previlaged to work and supervised by some extremely brilliant minds in VITA and AI Health @ UT Austin.

Somewhere, something incredible is waiting to be known.

Prof. Carl Sagan

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