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About Me

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.

My Journey

2022 - Present

Ph.D.

University of Illinois, Urbana-Champaign

Summer 2024

Applied Scientist Intern

Amazon

May 2023 - Feb 2024

Applied Scientist Intern

Amazon

2019 - 2022

AI Researcher

VUNO Inc.

2017 - 2019

M.S.

University of Illinois, Urbana-Champaign

2014 - 2017

B.S.

University of Illinois, Urbana-Champaign

Research

Investigating the Foundations of Learning Algorithms

My research is centered on understanding the fundamental limits and capabilities of machine learning algorithms.

View All Publications

Posts

pages

Page Not Found

Sorry, but the page you were trying to view does not exist.

About Me

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.

My Journey

2022 - Present

Ph.D.

University of Illinois, Urbana-Champaign

Summer 2024

Applied Scientist Intern

Amazon

May 2023 - Feb 2024

Applied Scientist Intern

Amazon

2019 - 2022

AI Researcher

VUNO Inc.

2017 - 2019

M.S.

University of Illinois, Urbana-Champaign

2014 - 2017

B.S.

University of Illinois, Urbana-Champaign

Research

Investigating the Foundations of Learning Algorithms

My research is centered on understanding the fundamental limits and capabilities of machine learning algorithms.

View All Publications