i am a second year 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).

i am interested in the mathematical foundations of machine learning, which translates to learning and infering over high-dimensions efficiently. i am interested in reinforcement learning. 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. i can be trained very easily by showing me few slices of pizza instead to expensive gpus. i like working on fun real-world applications of machine learning. in my free time, i like to cook.


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


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
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
joint speech-text pre-training with application to text adaptation for asr
jian vora, cekic metehan, dhanush bekal, karel mundnich, srikanth ronanki, katrin kirchoff
a hubert-based shared encoder for speech and text modalities for better coherence in embeddings using a small amount of paired multimodal data, application to text adaptation for asr

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


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


reviewer: tmlr, ieee tsp, aaai

leadership: manager, electronics and robotics club