Hongpeng Guo

Hongpeng Guo

Ph.D. Candidate in Computer Science

University of Illinois, Urbana Champaign (UIUC)


I am a fifth-year PhD student in Computer Science at the University of Illinois, Urbana-Champaign (UIUC). I am fortunate to be advised by Prof. Klara Nahrstedt at the MONET research group. My research interests lie at the intersection of systems and machine learning, with a focus on designing resource-efficient machine learning training and inference systems. Some applications I am currently working on include large-scale video analytics and federated learning.

Before joining UIUC, I got my bachelor’s degree in Computer Engineering from the University of Hong Kong (HKU). I was fortunate to be advised by Prof. King-Shan Lui.

In the past, I have worked with Google, Meta and IBM Research.

  • Distributed Systems
  • Efficient Machine Learning
  • Video Analytics
  • Federated Learning
  • Ph.D. in Computer Science, 2023

    University of Illinois, Urbana Champaign

  • B.Eng. in Computer Engineering, 2018

    University of Hong Kong


Jane Street Group LLC
Quantitative Trading Intern
May 2023 – Aug 2023 NYC, NY
Meta Inc
PhD ML Engineer Intern, Privacy Data Enrichment
May 2022 – Aug 2022 Menlo Park, CA
Google LLC
PhD Software Engineer Intern, Cloud Core Infrastructure
May 2021 – Aug 2021 Remote, US

Featured Publications

(2022). BoFL: Bayesian Optimized Local Training Pace Control for Energy Efficient Federated Learning. In Middleware 2022.

PDF Cite Code Poster Slides DOI

(2022). Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency. In ICDCS 2022.


(2021). CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale. In MMsys 2021.

PDF Cite Code Slides DOI

(2021). DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge. In SEC 2021.


(2020). Secure Broadcast Protocol for Unmanned Aerial Vehicle Swarms. In ICCCN 2020.

PDF Cite Slides DOI

(2019). Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing. In TMC 2019.


(2019). Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium. In INFOCOM 2019.



  • hg5@illinois.edu
  • 3101 Thomas M. Siebel Center for Computer Science, 201 N Goodwin Ave, Urbana, IL 61801