I am an MS student at Stanford in the Computer Science Department. Before that, I was a naive undergrad at IIT Bombay. I (pretend to) work on neural nonsense. You can find me in one of my two natural eigenstates (images on the left).

Very recently, I have been interested in LLM Agents, applications to scientific discovery, and shifting focus on evaluations of AI systems from AI models. I am also interested in the mathematical foundations of machine learning, which translates to learning and inferring over high-dimensions efficiently. In the past, I have worked on sparse recovery, tractable inference, and sequential decision-making problems. I have done some internships where I did funky deep learning.

I am a SOTA neural net capable of (a) few-shot meta adaptation, (b) robust to adversarial attacks, and (c) in-context learning abilities. In my free time, I like to cook.

Conference Publications & Workshop Papers

Benchmarking Large Language Models as AI Research Agents

Qian Huang, Jian Vora, Percy Liang, Jure Leskovec

can LLM Agents do AI Research: No but pretty good at ML engineering tasks

Sequential Consumption-Aware Ranking Model for Recommendations at Twitch

Jian Vora, Edgar Chen, Nikita Mishra, Saad Ali

leverage sequential user interaction data to improve ranking in recommendations

GNN Predictions on k-hop Egonets Boosts Adversarial Robustness

Jian Vora

make GNNs adversarially robust in under 5 lines of code

Scoring Black-Box Models for Adversarial Robustness

Jian Vora, Pranay Reddy Samala

adversarially robust models have sharper explanations and sparser lime weights, use this as a good subsitute for robust accuracy where trying to find attacks to the model can be hard

PAC Mode estimation using PPR Martingale Confidence Sequences

Shubham Jain, Rohan Shah, Sanit Gupta*, Denil Mehta*, Inderjeet Nair*, Jian Vora*, Sushil Khyalia, Sourav Das, Vinay Riberio, Shivaram Kalyankrishnan

asymptotically optimal mode estimation of a discrete distribution by construcing confidence sequences (1v1, 1vr); applications to election polls and contract verification in blockchains

Recovery of Joint Probability Distribution from one-way marginals: Low Rank Tensors and Random Projections

Jian Vora, Karthik Gurumoorthy, Ajit Rajwade

model a joint pmf as a low-rank tensor, recover the mode factors from 1D marginals estimated from random projections of data

Compressive signal recovery under sensing matrix errors combined with unknown measurement gains

Jian Vora, Ajit Rajwade

compressive recovery when the sensing matrix is misspecified and there are unknown sensor gains

Preprints

Plug&Play Multimodal Generative model allowing tractable inference

Jian Vora, Isabel Valera, Guy Van den Broeck, Antonio Vergari

learn a joint distribution over multiple modalities while allowing for efficient marginalization, conditioning, likelihood evaluation using probabilisitc circuits on the fused latent space

Other selected research + course projects

Efficient learning of log-concave mixtures

Jian Vora, Vivek Borkar

random projections of data drawn from a mixture of log-concave densities are provably distributed as a gaussian mixture in the subspace

Tractable Cooperative Multi-Agent Reinforcement Learning

learn a joint policy over actions of all agents allowing for efficient inference by modeling q-function to be a factor graph

Improving Inference in latent variable models

improved inference in VAEs by reducing two gaps -- approximation gap by using hierarchical VAEs and amortization gap by performing unamortized inference

Spatio-Temporal Action Detection and Classification

participated in the trecvid'19 challenge which involved performing action detection and classification in videos. proposed a stage-wise architecture of object detection followed by tracking and activity classification

Continual Learning for Keyword Spotting and Speaker Identification

proposed a joint model to perform simultaneous kws and sid based on an interspeech 2021 challenge

Conditional Style-GAN for audio generative modeling

Modified stylegan to allow for conditioning and trained on audio spectrograms

Teaching

Summer 2023: Course Assistant for CS221: Artificial Intelligence at Stanford

Winter 2023: Course Assistant for CS234: Reinforcement Learning at Stanford

Fall 2022: Course Assistant for CS224V: Conversational Virtual Assistants with Deep Learning at Stanford

Winter 2022: Course Assistant for CS236G: Generative Adversarial Networks at Stanford

Spring 2021: Teaching Assistant for MA111: vector calculus at IITB

Service

Reviewer: TMLR, IEEE TSP, AAAI, NeurIPS FMDM Workshop

Leadership: Manager, Electronics and Robotics club