Dr. Sahil Garg (Vice President, Machine Learning Research at Morgan Stanley)

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Research Interests

Machine Learning (low resource, robustness, interpretability, semi-supervision, information theory, nonparametric, representation learning).

Natural Language Processing (relation extraction, dialog modeling, text clustering, text search).

Deep Learning (continual learning, optimization algorithms).

Investment Banking (Machine Learning, NLP, Deep Learning).

Computational Psychiatry (early diagnosis, therapeutic dialogues).

Neuroscience (autoencoding, neurogenesis).

Robotics (information gathering, spatio temporal modeling, reinforcement learning).

Network Science (generative models, phase transition, link prediction).

Employment and Education

I am diving into the world of investment banking, with full enthusiam, continuing my passion for problem-driven research, striving to tackle mission-critical problems in the facinating world of finance.

Previously, I explored the field of computational psychiatry, as a postdoctoral fellow in Icahn school of medicine at Mount Sinai, under the mentorship of Dr. Cheryl Corcoran and Dr. Guillermo Cecchi.

I was advised by Prof. Aram Galstyan for my PhD thesis (USC), "Hashcode Representations of Natural Language for Relation Extraction".

I have active collaborations with IBM Research (NY), MIT, NYU, and U. of Montreal.

In the past life, I had the privilege of learning under the guidance of Prof. Nora Ayanian (USC), Prof. Amarjeet Singh (IIIT Delhi), and Prof. Fabio Ramos (U. of Sydney).

"Selected" Publications

Modeling Dialogues with Hashcode Representations: A Nonparametric Approach. Sahil Garg*, Irina Rish, Guillermo Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan. AAAI-20. PDF.

PhD Thesis. Sahil Garg. USC. PDF.

Nearly-Unsupervised Hashcode Representations for Relation Extraction. Sahil Garg, Aram Galstyan, Greg Ver Steeg and Guillermo Cecchi. EMNLP-19. PDF, Code.

Kernelized Hashcode Representations for Relation Extraction. Sahil Garg*, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo Cecchi, Shuyang Gao. AAAI-19. PDF, Code.

Stochastic Learning of Nonstationary Kernels for Natural Language Modeling. Sahil Garg*, Greg Ver Steeg, Aram Galstyan. 2017. PDF.

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. Sahil Garg*, Irina Rish, Guillermo Cecchi, Aurelie Lozano. IJCAI-17. PDF, Code.

Extracting Biopathway Interactions using Semantic Parsing of Biomedical Text. Sahil Garg*, Aram Galstyan, Ulf Hermjakob, Daniel Marcu. AAAI-16. PDF, Code.

Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots. Sahil Garg*, Nora Ayanian. RSS-14. PDF.

Learning Nonstationary Space-Time Models for Environmental Monitoring. Sahil Garg*, Amarjeet Singh, and Fabio Ramos. AAAI-12. PDF, Code.

CV and Other Links

My curriculum vitae are here (last updated in July 2020).

See Google Scholar for an extended list of the papers.

This site is text-positive and defiantly retro (hand-crafted HTML 1.0).


sahil.garg.cs at gmail.com