Jun 2021 – Sep 2021 · 3 mos · New York City

• Working as a Research Intern at Facebook AI Research on developing novel self-supervised representation learning methods.
• Advised by Yann Lecun, Yubei Chen and Stephane Deny during this internship.

Sep 2020 – Jun 2021 · 9 mos · Mountain View, California, United States

• Continuing my research internship project on comparing human- and machine visual perception as a Student Researcher in the Google Brain team.

Jul 2020 – Sep 2020 · 2 mos · Mountain View, California, United States

• Working as a Research Intern with the Google Brain team on human and machine visual perception. Advised by Gamaleldin Elsayed, Jascha Sohl-Dickstein, Michael Mozer and Jon Shlens during this internship.

Jul 2019 – Sep 2019 · 2 mos · San Diego County, California, United States

• Worked at the Qualcomm AI Research group with Reza Pourreza, Amir Habibian and Taco S. Cohen on generative modeling of videos.

Sep 2018 – Mar 2024 · 5 yrs 6 mos · La Jolla, California

• As a doctoral student advised by Prof. Virginia de Sa, I conduct research on the computational modeling of early visual perception.
• Teaching Assistant for Prof. de Sa's courses COGS 118B - Introduction to Machine Learning II (Fall 2018) and COGS 189 - EEG-based Brain-computer interfaces (Winter 2019).

Aug 2017 – Aug 2018 · 1 yr · Providence, Rhode Island Area

• Worked with Prof. Thomas Serre in the Serre Lab on developing the Horizontal Gated Recurrent Unit, a biologically inspired recurrent unit that complements feedforward neural networks with long-range horizontal connections.
• Collaborated with the Neuroscience and Psychology departments at Brown University on the automation of disease diagnosis using computer vision and deep learning.
• Authored publications of my work at the following venues: (1) NeurIPS 2018, (2) SfN 2018, (3) CCN 2018.

Dec 2016 – Feb 2017 · 2 mos · Bengaluru, Karnataka, India

• Worked on applying Generative Adversarial Networks (GANs) to address Artifacia's computer vision problems.
• Developed GAN models for the following vision problems: (1) Image Inpainting, (2) Text-to-Image synthesis, and (3) Image Super-Resolution.
• Conducted an introductory tutorial session on Generative Adversarial Networks and their applications at the Artifacia AI Meet in January 2017.

Jan 2016 – Mar 2016 · 2 mos · Greater Chennai Area

• Developed the TradeSocio Sentiment Analysis engine that analyzed content posted on foreign exchange forums.
• Trained random forest classifiers on tf-idf features corresponding to the forum posts discussing about specific currency pairs, to classify them as either bullish or bearish pairs based on the posts’ overall sentiment value.

Jun 2015 – Jun 2015 · 0 mo · IIITD&M, Kancheepuram

• During this internship, I worked on developing a fast and efficient compression algorithm, for images. I used sequence mining and unsupervised learning techniques to develop the algorithm.
• This algorithm yields compression ratios that are comparable to that of a few industry standard lossy image compression algorithms including GIF, and JPEG.
• Performed extensive reviews of the state-of-the-art machine learning and computer vision systems and their applications to data compression.