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I am a Ph.D. Candidate in the ECE department at the University of Illinois, Urbana-Champaign, where I am fortunate to be advised by Prof. R. Srikant. My research interests are in the theoretical foundations of machine learning, with a focus on stochastic optimization, generative models, and probability theory. My doctoral work centers on establishing sharp characterizations of error in learning algorithms to better understand their fundamental limits.
I have had the opportunity to apply and expand my skills through several industry roles. During my internships at Amazon, I developed an ensemble model to predict customer satisfaction from sparse behavioral signals and built a listwise-ranking LLM as part of the search engine. Before my Ph.D., I spent three years as an AI Researcher and Team Lead at VUNO Inc., where I focused on building and leading the development of deep learning models for medical radiology.
When I’m not working, I enjoy traveling, exploring new foods, cooking, brewing coffee, and tinkering with personal software and hardware projects.
A variety of common markup showing how the theme styles them.
Coming soon! Stein kernel and applications to high dimensional central limit theorem.
Coming soon! Tail bounds for reversible Markov chains (spectral gap).
Coming soon! Lie brackets and control of underactuated systems.
My research is centered on understanding the fundamental limits and capabilities of machine learning algorithms.
View All PublicationsComing soon! Stein kernel and applications to high dimensional central limit theorem.
Coming soon! Tail bounds for reversible Markov chains (spectral gap).
Coming soon! Lie brackets and control of underactuated systems.
Sorry, but the page you were trying to view does not exist.
I am a Ph.D. Candidate in the ECE department at the University of Illinois, Urbana-Champaign, where I am fortunate to be advised by Prof. R. Srikant. My research interests are in the theoretical foundations of machine learning, with a focus on stochastic optimization, generative models, and probability theory. My doctoral work centers on establishing sharp characterizations of error in learning algorithms to better understand their fundamental limits.
I have had the opportunity to apply and expand my skills through several industry roles. During my internships at Amazon, I developed an ensemble model to predict customer satisfaction from sparse behavioral signals and built a listwise-ranking LLM as part of the search engine. Before my Ph.D., I spent three years as an AI Researcher and Team Lead at VUNO Inc., where I focused on building and leading the development of deep learning models for medical radiology.
When I’m not working, I enjoy traveling, exploring new foods, cooking, brewing coffee, and tinkering with personal software and hardware projects.
A variety of common markup showing how the theme styles them.
Coming soon! Stein kernel and applications to high dimensional central limit theorem.
Coming soon! Tail bounds for reversible Markov chains (spectral gap).
Coming soon! Lie brackets and control of underactuated systems.
My research is centered on understanding the fundamental limits and capabilities of machine learning algorithms.
View All Publications