Laryn Qi

I graduated in May 2024 with an MS in EECS at UC Berkeley advised by Narges Norouzi and John DeNero. My research was in building and evaluating AI tools for CS education with the Algorithms & Computing for Education (ACE) and Berkeley AI Research (BAIR) labs.

Prior, I received my Bachelors in CS and Music at UC Berkeley. You can find my coursework here.

I provide private tutoring for CS 61A students. Please see this page for details.

larynqi [at] berkeley [dot] edu
Resume  /  LinkedIn  /  GitHub

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This project aims to replicate the behavior of OpenAI and Deepmind's Deep Reinforcement Learning from Human Preferences using preferences elicited from GPT4-V instead of humans. The code and architecture of the project are based on Matthew Rahtz's implementation of the original paper, simplified for our purposes and translated into PyTorch.

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Implementations of various popular randomized graph algorithms. Topics and problems covered include  Cover Time,  Min Cut,  Stopping Time,  Approximate Max Cut,  and  Approximate Independent Set. Also includes an incomplete implementation of the first approximation algorithm for Minimum Balanced Cut by Spielman and Teng.

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We present a survey on state-of-the-art cut algorithms. Topics and problems covered include  Fair Cuts,  Minimum Balanced Cut,  and  Sparsest Cut.

[Spring 2023] Outstanding Graduate Student Instructor (OGSI) Award.

[Spring 2023] Top 6.5% in CS 189 (Machine Learning) Kaggle competition (>700 students). Trained an SVM model to classify CIFAR-10 data with 56% accuracy.

[Fall 2021] Top 6% in CS 170 (Algorithms) Final Project competition (>700 students). Designed a greedy algorithm implemented with dynamic programming to approximate an NP-hard problem. Generated and tested heuristics on the cloud.

[Spring 2021] Upsilon Pi Epsilon (UPE). International CS Honor Society. Top third of Berkeley CS majors.

[Spring 2020] CS 61A Outstanding Academic Intern. Awarded to top 9 out of over 130 Academic Interns for exemplary teaching.

Last updated January 14, 2025.