Pittsburgh, Pennsylvania, United States
● Mentored by Xinyue Cui and Yoonsoo Nam, advised by Prof. Swabha Swayamdipta ● Text Simplification Metrics: Designed a novel reference-free metric for text simplification by introducing LLM judges, eliminating the need for specialized training data. ● Model Architecture Design: Developed an efficient evaluation framework utilizing pre-trained models such as Llama 3 without fine-tuning, enabling broad domain coverage and robust simplification assessment. ● Evaluation: Demonstrated superior performance in evaluating simplifications, achieving 51.2% correlation with human judgment and outperforming traditional metrics, such as FKGL and SARI, and trained metrics such as LENS.
● Mentored by Yimin Tang, advised by Prof. Sven Koenig ● Trainable Heuristic Environment: Developed an RL environment to train heuristics for multi-robot path planning, leveraging 4D representations to capture spatial-temporal relationships between robot paths and environmental constraints. ● Two-Phase Training Strategy: Crafted a two-phase training strategy, initially replicating traditional heuristics and subsequently enhancing search efficiency with a node expansion reward system. ● Search Efficiency Assessment Tool: Implementing a quantitative evaluation system based on node expansion metrics, enabling direct measurement of search efficiency improvements for the learned heuristic function.
● Mentored by Bryan Shaddy, advised by Prof. Assad Oberai ● Worked on physics-informed machine learning techniques to model wildfire spread using diffusion and GAN models
● Teaching Assistant for CSCI-201: Principles of Software Development for Prof. Victor Adamchik ● Helped the professor prepare the computer lab exercises and coached students in the lab for their coding assignments
● Pipeline Optimization: Led end-to-end optimization of ML sales prediction pipeline, achieving 86% reduction in interruptions, 30% faster runtime, and 25% cost savings while improving data quality by fixing critical bugs affecting 60% of the dataset. ● Research Leadership: Spearheaded feature engineering initiatives and performance optimization research, presenting findings to 80+ stakeholders including directors and VPs. ● Performance Recognition: Demonstrated exceptional performance resulting in return offer for Summer 2024
● Video Analysis Research: Researched state-of-the-art methodologies in Computer Vision (CV) and Natural Language Processing (NLP) for video analysis. ● Audio-Video Embedding: Designed and implemented a Transformer-based model for multimodal (video and audio) embedding generation with PyTorch, achieving 60% precision on AudioSet dataset.
● Grader for CSCI 163: Theory of Algorithms for Prof. Nicholas Tran ● As a freshman, graded homework and exams for a course primarily taken by upperclassmen.
• Conducted independent research on using CNNs for identifying exoplanet candidates from photometric data • Achieved 61% accuracy in identifying exoplanet candidates, comparable to currently existing ML techniques using light curve data • Simulated time series images of stars to increase training samples by 200% • Designed and implemented 3D CNN model for binary classification of stars using Pytorch