Alan Zhu
Incoming CS PhD Student, University of California, Berkeley

I am an incoming CS PhD student at UC Berkeley. My research interests are in the field of Machine Learning (ML) generally, with a focus on improving usability and efficiency through Systems for ML. I will be supported by an NSF Graduate Research Fellowship.

I recently graduated with a BS from Carnegie Mellon University (CMU), where I majored in Computer Science with an additional major in Mathematical Statistics. At CMU, I worked in the Catalyst Lab under the supervision of Prof. Zhihao Jia and in the Interactive Structures Lab under the supervision of Prof. Alexandra Ion. I have also worked on research under the supervision of Prof. Qiaozhu Mei at the University of Michigan.


  • Machine Learning
  • Systems for ML
  • Efficient AI


University of California, Berkeley
2024 - present
PhD Computer Science
Carnegie Mellon University
2020 - 2024
BS Computer Science, additional major in Mathematical Statistics
4.0/4.0 GPA


Accelerating Retrieval-augmented Language Model Serving with Speculation, 2024, To appear at ICML
Zhihao Zhang , Alan Zhu , Lijie Yang , Yihua Xu , Lanting Li , Phitchaya Mangpo Phothilimthana , Zhihao Jia
SpecInfer: Accelerating Generative Large Language Model Serving with Speculative Inference and Token Tree Verification, 2024, ASPLOS
Xupeng Miao , Gabriele Oliaro , Zhihao Zhang , Xinhao Cheng , Zeyu Wang , Rae Ying Yee Wong , Alan Zhu , Lijie Yang , Xiaoxiang Shi , Chunan Shi , Zhuoming Chen , Daiyaan Arfeen , Reyna Abhyankar , Zhihao Jia
Robotic Metamaterials, 2024, CHI
Zhitong Cui , Shuhong Wang , Violet Yinuo Han , Tucker Rae-Grant , Willa Yunqi Yang , Alan Zhu , Scott E Hudson , Alexandra Ion

Current Projects

Examining Shortest-Path Distances to Samples in Graphs
PI: Prof. Qiaozhu Mei. We propose a framework to predict the distribution of shortest-path distances to a sample of nodes in a graph, generalizable to a variety of sampling methods. The framework has applications in sample selection for training graph neural networks and other studies. A manuscript is in preparation for WWW 2025.


See CV for responsibility details
TA for 15251 Great Ideas in Theoretical Computer Science
Carnegie Mellon University
Head TA: Fall 2023 - Spring 2024
Associate Head TA: Spring 2023
TA: Fall 2021 - Fall 2022


In high school (at Greenhills School) I was active in math and robotics competitions. I qualified for the USAJMO in 2016 and the USAMO in 2019. I also served as programming team lead for the robotics team (FRC Team #5530 - The Greenhills Lawnmowers), which qualified for the FIRST World Championships in 2019.