Ph.D. student specializing in Machine Learning and Computer Engineering.
My research interests include machine learning, speech and natural language processing, statistical modeling, reinforcement learning, adversarial learning, systems security and vulnerability analysis of deep learning models.
I received my B.Sc. in Electrical Engineering from University of Massachusetts, Amherst and B.A in Physics from Mount Holyoke College in 2015 and 2014, respectively. Prior to pursuing my research interests in academia, I worked as an Applications Engineer at Global Foundries New York. I’m a recipient of Charles Lee Powell Foundation Fellowship.
Shehzeen Hussain*, Paarth Neekhara*, Malhar Jere, Farinaz Koushanfar, Julian McAuley
Winter Conference on Applications in Computer Vision (WACV) 2021
[ * Equal Contribution ]
[ paper, video examples ]
Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, Farinaz Koshanfar
Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing 2019 (EMNLP)
[ paper, code ]
Nyan Lynn Aung, Woong Jae Chung, Lokesh Subramany, Shehzeen Hussain, Pavan Samudrala, Haiyong Gao, Xueli Hao, Yen-Jen Chen, Juan-Manuel Gomez
Metrology, Inspection, and Process Control for Microlithography 2016 SPIE
[ paper ]
June 2020 - September 2020
Menlo Park, California
I was a member of the Text-to-Speech Synthesis team within Applied AI Speech. I developed deep neural network models for multi-speaker and multi-style controllable speech synthesis. I also designed end-to-end pipeline for joint training of speaker encoder model with text-to-speech synthesis (Tacotron2) model. Additionally, I developed a voice cloning toolkit for synthesizing speech of unseen speakers from a few reference audio samples.
July 2019 - October 2019
Santa Clara, California
Graduate Machine Learning Intern
I was a part of the Non-Volatile Memory Systems Group, working on reinforcement learning for memory and SSD applications.
June 2018 - September 2018
San Diego, California
Deep Learning R&D Intern
I was a part of Qualcomm Research optimizing power and performance management on Qualcomm chipsets using reinforcement learning with deep neural networks. I was advised by Shankar Sadasivam, Manu Rastogi, Guillaume Sautière and Rajeev Jain.
July 2015 - August 2017
Malta, New York
Among other responsibilities, I was a member of the applications team and assisted in design of algorithms to model advanced wafer level corrections.
[ paper ]