Biography

I am a graduate student in Machine Learning at EPFL, Switzerland, where I am advised by Martin Jaggi. Currently, I am doing my master thesis on second-order methods for deep learning, advised by Dan Alistarh at IST Austria. Before EPFL, I completed my undergraduate studies in Computer Science at IIT Roorkee.

Previously, I have interned at Facebook AI Research (FAIR), working with Michael Auli and Angela Fan on non-compositional representations for text. Thanks to the Honda Y-E-S Award, I got the wonderful opportunity to work with Marco Cuturi at Kyoto University, where I learned about Optimal Transport.

In particular, my research focuses on the areas of representation learning, robust-continual learning and resource-efficient learning. Check out my research statement for more details.

• Our Context Mover’s Distance paper is accepted at AISTATS 2020.
• Presented our work on Model Fusion via Optimal Transport at the OTML workshop in NeurIPS (2019).
• Received travel award & selected amongst top 50% reviewers, NeurIPS (2019).
• Our paper on Context Mover’s Distance & Barycenters is accepted at ICLR DeepGenStruct workshop (2019).
• Preprint of my internship work at FAIR is out, GLOSS: Generative Latent Optimization of Sentence Representations (2019).
• Preprint for our recent paper on Wasserstein is all you need (2018).
• Student grant to attend ML4P workshop in Oxford (2018).
• Excited to join Facebook AI Research (FAIR) as an intern this fall (2018).
• Selected for The Alan Turing Institute’s, data study group (2017).
• Offered fulltime Research Fellow position at Microsoft Research India (2017).
• Received Honda Y-E-S (Young Engineers and Scientists) Award (2016).
• Awarded Google Venkat Panchapakesan Scholarship and invited visit to Google YouTube and Mountain View offices (2016).