cv/resume

M.S.

Carnegie Mellon University

Masters of Science in Machine Learning

GPA: 3.93/4.0

B.S.

Carnegie Mellon University

AI and Discrete Math/Logic double major

GPA: 4.0/4.0

High School Diploma

Thomas Jefferson High School for Science and Technology

GPA: 4.1/5.0

Software Engieer Intern (Research Team)

Meta

Worked at Meta (Formerly Facebook) on FAIR, working on bias amplification

• Work accepted to CVPR TSRML Workshop

Software Engieer Intern (Research Team)

Meta

Worked at Meta (Formerly Facebook) on robustness of probabilistic neural networks

Software Engieer Intern

Fiat Chrysler Automobiles

Worked at Fiat Chrysler (Now part of Stellantis) on absentee worker prediction

Story Generation

Generate stories (new story captions and corresponding images) from a single initial story caption/image

• predict both descriptive text for image generation and story caption for story text
• Course project for 11-777 Multimodal Machine Learning

Solving Graph Problems with Diffusion

Use Graph Neural Networks and Diffusion to solve graph problems like MST (minimum spanning tree)

• predict whether edges are in or out of MST using diffusion, where all edges start with randomized probability of being in MST
• course project for 10-708 Probabilistic Graphical Models

MIT Battlecode!

Competed on team of 4 in annual strategy-based coding competition run by MIT, January 2021, 2022, 2023

• $2000+ in prize winnings

Cozmo Depth Map

Calculated absolute depth map for Cozmo robot using deep learning and aruco markers

Siebel Scholarship

Siebel Scholars Foundation, Carnegie Mellon University

the Siebel Scholars program recognizes nearly 100 students each year whose work influences the technologies, policies, and economic and social decisions that shape the future.

CMU Rales Fellowship

Norman R. and Ruth Rales Foundation, Carnegie Mellon University

2 years of full graduate funding and leadership development training

The Mark Stehlik Introductory and Service Teaching Award

Carnegie Mellon University

Introductory teaching and course assistants complement the course offerings by providing quality guidance. The introductory experience rests on the shoulders of these talented many, drawn from within SCS and across campus. The Stehlik Award acknowledges those bringing their best and indispensible efforts to the task.

Teaching Assistant for 10-703 Deep Reinforcement Learning

Carnegie Mellon University

Teaching assistant for Deep Reinforcement Learning

• Updated reading list for course, hosted office hours, graded Assignments

Teaching Assistant for AI Scholars Program

Carnegie Mellon University

Teaching assistant for summer AI course for high schoolers from underrepresnted backgrounds

• Gave lecture on Generative Models, lead discussions/activity sessions
• helped design course material

Teaching Assistant for 15-251 theoretical computer science (3 Years)

Carnegie Mellon University

Teaching assistant for theoretical computer science course S21, S22, S24

(Head) Teaching Assistant for 15-151/21-128 discrete math (4 Years)

Carnegie Mellon University

Teaching assistant for discrete math course F20, F21 (Head TA), F22 (Head TA), F23 (Shadow Council)