Jupinder Parmar

Bio

I graduated with a B.S in Mathematics and M.S in Computer Science from Stanford University where I concentrated my studies within probability and machine learning. I was fortunate to be advised by Amir Dembo for my undergraduate degree and Christopher Ré for my graduate degree. At Stanford, I spent my time exploring a variety of areas with the goal of learning how to levarage my understanding of random phenomena and artificial intelligience to develop exciting ideas and products that will revolutionize our world. I believe that these two concepts can not only guide us to a deeper understanding of the problems that challenge our lives, but also give way to robust, innovative solutions.

I now work as a Deep Learning Applied Scientist at NVIDIA on the foundation model training team. I primarily focus on developing new large language models (LLMs) and augmenting their abilities via continued training.

// In the past, I've:

Worked with Tatsu Hashimoto on improving group robusntess of machine learning models. Current models achieve high average performance but can incur high error on certain groups of rare and atypical examples. We developed a new framework which allows for models to obtain better worst-group performance on tasks.

Worked as a teaching assistant for CS 234: Reinforcement Learning where I had the wonderful opportunity to help introduce the exciting field of reinforcement learning to students. Teaching has always been a passion of mine and it was gratifying to help aid in the educational journey of others.

Researched in Chris Re's Lab at Stanford where I worked with Khaled Saab. We worked on making use of passively observed human signals, such as one's gaze, to learn better representations of tasks and help train more robust, generalizable deep learning models for medical image classification.

Worked as a Machine Learning Engineer and the first Product Manager for OccamzRazor. OccamzRazor is an early stage company developing a biomedical knoweldge graph which aims to advance understanding of drug efficacy and provide novel treatments for diseases. My initial focus of work was on expanding out their natural langauge processing pipeline by researching and engineering new architectures for biomedical information extraction. Later, I focused on expanding the product platform from just repurposing known drugs to the broader set of small molecules that actively interact with any druggable protein additionally spent time exploring how to make graph learning algorithms more interpretable.

Implemented architectures described in various machine learning research papers from scratch in PyTorch. I wanted to dive deeper into the content I was learning in my classes and found that the best way to do so was to get hands on experience with the intracies of each model. Some of my favorite architectures that I implemented were ResNet and a Neural Image Caption Generation Model.

Worked as a quantitative trading intern at IMC Trading on the Small Index Options Desk. I developed new trading strategies to improve performance on US Options Auctions by leveraging information regarding market sentiment.

Worked as a software engineering intern at SAS on the Enterprise Computing team. I aided in the team's shift to Kubernetes and developed a machine learning model to predict when potential workloads would cause system failures.

Researched with David Chin at UMass Amherst where we investigated the intersection of quantum and classical machine learning. In particuar, we looked to see whether quantum models could help us better assess the quality of care provided by hospitals.

Worked with David Doty at UC Davis where we built a domain specific language that allowed researchers to implement complex theoretical models in the field of algorithmic self assembly.

// Outside of Work I Enjoy:

• Playing chess. The Italian Game is my favorite opening.
• Cooking new dishes. Souvlaki has been my most recent hit recipe.
• Attending concerts and music festivals. Lollapalooza was paricularly memorable.
• Hiking and running. This trail in Lake Tahoe offers some amazing views.
• Deep diving into philosophy. Socrates holds a special place in my heart.
• Reading. My bookshelf is a good way to see whats on my list and what my favorites are.

Projects

Observational Supervision
Using passively observed human signals to improve medical imaging models.

Group Robustness
Improving model performance on atypical subgroups of data.

Biomedical Information Extraction
Uncovering biomedical data silos currently stuck within millions of pieces of literature.

Publications and Written Pieces

• Papers
• Written Pieces