Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five
Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

EntryItem 
John Doe2016Description of the item in the list
Jane Doe2019Description of the item in the list
Doe Doe2022Description of the item in the list
Header1Header2Header3
cell1cell2cell3
cell4cell5cell6
cell1cell2cell3
cell4cell5cell6
Foot1Foot2Foot3

Definition Lists

Definition List Title
Definition list division.
Startup
A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork
Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live
I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

Ordered List (Nested)

  1. List item one
    1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
    2. List item two
    3. List item three
    4. List item four
  2. List item two
  3. List item three
  4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H2O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.

Variable Tag

This allows you to denote variables.

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