Harry Dong PhD Student, Carnegie Mellon University I am an Electrical and Computer Engineering (ECE) PhD student at Carnegie Mellon University (CMU) where I have the pleasure of exploring my research interests in efficient machine learning algorithms with my advisor, Professor Yuejie Chi. Prior to CMU, I graduated with High Distinction from UC Berkeley with degrees in statistics and computer science in 2021. Please reach me through email: harryd [at] andrew [dot] cmu [dot] edu CV / Google Scholar / GitHub / Twitter

Research Overview

My research roughly aims to make powerful deep learning models more practical for use. My main focus is on the algorithmic side of LLM inference efficiency by leveraging inherent structures and patterns within the architecture, data, and/or pretrained weights. I also devote a significant amount of time investigating how LLMs and diffusion models can be applied to challenging science problems, particularly in materials science, where there may be physical constraints and low error tolerance. Previously, I have also worked on optimization.

Projects

• Deep Learning Efficiency: Efficient algorithms/architectures to reduce inference costs, with a focus on LLMs.

• Machine Learning in Materials Science: Scalable methods for materials data that have underlying physics relationships.

Previous Projects

• Tensor Robust Principal Component Analysis: Low rank tensor recovery from sparsely corrupted data with theoretical guarantees and empirical analysis.

• Traffic Routing: Optimal/stochastic vehicle routing in traffic networks.

• Deep Learning for Neuroscience: Neuronal ion conductances prediction from voltage signals.

Awards

• Wei Shen and Xuehong Zhang Presidential Fellowship (2024)

• Liang Ji-Dian Graduate Fellowship (2023)

• Michel and Kathy Doreau Graduate Fellowship in Electrical and Computer Engineering (2023)

• NSF GRFP Honorable Mention (2023)

• UC Berkeley High Distinction (2021)