research

papers

* in alphabetical order

Anoop Kunchukuttan, Maulik Shah, Pradyot Prakash, Pushpak Bhattacharyya, “Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT.” IJCNLP, 2017
* Stanley Bak, Sergiy Bogomolov, Thomas A. Henzinger, Taylor T. Johnson, Pradyot Prakash, “Scalable Static Hybridization Methods for Analysis of Nonlinear Systems.” HSCC 2016

Breaking Robust Adversarial Classification (retrieve here)

Spring 2018

Under Prof. Dimitris Papailiopoulos, UW-Madison

Robust Manifold Defense is state-of-the-art adversarial classification algorithm which works by projecting on the space of GANs
Developed the first algorithm to break the classifier & brought down the classification accuracy by 35% (more work in progress)

Fuzzy Iterative Machine Teaching (retrieve here)

Spring 2018

Under Prof. Jerry Zhu, UW-Madison

Studied the inverse problem of ML–Machine Teaching–where the aim is to learn a target parameter vector in minimum steps
Derived robust bounds for the minimum steps needed to converge under noisy and missing data settings for different losses

Autoencoders & Generative Adversarial Modeling

Spring 2018

Under Prof. Rebecca Willett, UW-Madison

Implemented autoencoders and generative adversarial nets for MRI image denoising
Worked on a unique approach to invert a neural network using Neumann series in operator space

GPU profiling of Deep learning frameworks (retrieve here)

Fall 2017

Under Prof. Aditya Akella, UW-Madison

Analyzed deep learning libraries using their intermediate representations (with ONNX) and profiled their GPU performances

Effect of Segmentation and Encoding on Machine Translation

2016 - 2017

Under Prof. Pushpak Bhattacharyya, IIT Bombay

Improved transliteration and translation with use of pivot-based modeling, byte-pair encoding and orthographic syllabification
Paper published at IJCNLP 2017 (retrieve here)

Statistical Shape Analysis of Images (retrieve here and here)

2016 - 2017

Under Prof. Suyash Awate, IIT Bombay

Created a similarity measure between images and their contours alongside a novel objective function
Used that to segment MRI images (with a shape prior) by performing Riemannian PCA on high dimensional unit spheres