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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Sun, 24 Aug 2025 13:46:22 GMT access-control-allow-origin: * etag: W/"68ab17ae-5d86" expires: Sun, 28 Dec 2025 13:06:57 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 666A:3827E5:7AC067:8997EC:69512918 accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 12:56:57 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210028-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766926617.109422,VS0,VE208 vary: Accept-Encoding x-fastly-request-id: cc99dd1f11f8786aaf94650c0a5dac04f1b7cf9a content-length: 4962 Kulin Shah

Kulin Shah

I am a Ph.D. student in Computer Science department at the University of Texas Austin advised by Prof. Adam Klivans. My research interest broadly lies in generative models. I like to understand (empirically and theoretically) building blocks of modern generative models with the hope of improving them.

I was a student researcher at Google with Rina Panigrahy working on understanding and improving reasoning capabilities of language models during June 2023-March 2024. Before joining Ph.D., I was a research fellow at Microsoft Research India, where I worked with Amit Deshpande and Navin Goyal. I graduated with my B.Tech. (Hons.) in Computer Science from IIIT-Hyderabad, where I worked with Naresh Manwani.

I am graduating in 2026 and looking for full-time research positions in industry. Please reach out if you think I would be a good fit!

(α-β denotes alphabetical order and * denotes equal contribution)

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions PDF
Jaeyeon Kim*, Kulin Shah*, Vasilis Kontonis, Sham M. Kakade, Sitan Chen
ICML 2025 | International Conference on Machine Learning
Outstanding Paper Award

Does Generation Require Memorization? Creative Diffusion using Ambient Diffusion PDF Code
Kulin Shah, Alkis Kalavasis, Giannis Daras, Adam Klivans
ICML 2025 | International Conference on Machine Learning

Learning general Gaussian mixtures with efficient score matching PDF
(α-β) Sitan Chen, Vasilis Kontonis, Kulin Shah
COLT 2025 | Conference on Learning Theory

Causal Language Modeling can Elicit Search and Reasoning Capabilities on Puzzles PDF Code
Kulin Shah, Nishanth Dikkala, Xin Wang, Rina Panigrahy NeurIPS 2024 | Conference on Neural Information Processing Systems

Unrolled denoising networks provably learn optimal Bayesian inference PDF
Aayush Karan*, Kulin Shah*, Sitan Chen, Yonina C. Eldar NeurIPS 2024 | Conference on Neural Information Processing Systems

Simple Mechanisms for Representing, Indexing and Manipulating Concepts PDF
(α-β)Yuanzhi Li, Raghu Meka, Rina Panigrahy, Kulin Shah
Preprint

Learning Mixtures of Gaussians Using the DDPM Objective PDF Poster
Kulin Shah, Sitan Chen, Adam Klivans
NeurIPS 2023 | Conference on Neural Information Processing Systems

Ambient Diffusion: Learning Clean Distributions From Corrupted Data PDF Code
Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alexandros G. Dimakis, Adam Klivans NeurIPS 2023 | Conference on Neural Information Processing Systems

Learning and Generalization in Overparameterized Normalizing Flows PDF Poster Code
Kulin Shah, Amit Deshpande, Navin Goyal AISTATS 2022 | International Conference on Artificial Intelligence and Statistics

RISAN: Robust Instance Specific Deep Abstention Network PDF
Bhavya Kalra, Kulin Shah, Naresh Manwani
UAI, 2021 | Conference on Uncertainty in Artificial Intelligence
Selected for Oral Presentation

Rawlsian Fair Adaptation of Deep Learning Classifiers PDF
Kulin Shah, Pooja Gupta, Amit Deshpande, Chiranjib Bhattacharyya
AIES, 2021 | AAAI/ACM Conference on AI, Ethics, and Society

Online Active Learning of Reject Option Classifiers PDF
Kulin Shah, Naresh Manwani
AAAI, 2020 | AAAI Conference on Artificial Intelligence
Selected for Oral Presentation

Sparse Reject Option Classifier using Successive Linear Programming PDF
Kulin Shah, Naresh Manwani
AAAI, 2019 | AAAI Conference on Artificial Intelligence
Selected for Oral Presentation

Ingredients for happiness: Modeling constructs via semi-supervised content driven inductive transfer learning PDF
Kulin Shah, Naresh Manwani
AFFCON workshop @ AAAI, 2019 | AAAI-19 Workshop on affective content analysis

PLUME: Polyhedral Learning Using Mixture of Experts PDF
Kulin Shah, PS Sastry, Naresh Manwani
Preprint

Wepage inspired by minimal research theme.

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