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
- 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