Nathaniel Hudson, Ph.D.

A brief introduction.


312 John Crerar Library

5730 S Ellis Ave

Chicago, IL 60637

I am a computer scientist, currently serving as a Postdoctoral Scholar at Globus Labs out of the University of Chicago’s Department of Computer Science.

A high-level description of my research is the design of systems for serving AI on edge computing infrastructure — i.e., Edge Intelligence (EI) — for smart city applications. More specifically, my research centers around challenges related to resource limitations available at the edge for supporting EI. Trade-offs between latency, accuracy, resource usage, etc. are common themes in my work.

Some areas of study my research touches include (but are not limited to):

  • federated learning
  • service placement and request scheduling
  • lossy compression techniques
  • social mining
  • modeling of information diffusion processes
  • interdependent and complex networks
  • cyber-physical systems

recent news

Feb 27, 2023 I am thrilled to once again be serving as a TPC member for the 2023 IEEE SECON conference.
Jan 11, 2023 I will be serving as a TPC member for the 2023 IEEE EDGE conference.
Jan 5, 2023 Short paper on balancing trade-offs in federated learning has been accepted for the Work-in-Progress session of the 2023 IEEE PerCom Conference in Atlanta, GA.
Nov 1, 2022 I will be attending the 2022 ACM/IEEE Supercomputing Conference in Dallas Texas.
Sep 26, 2022 I will be serving as a TPC member for the 2023 IEEE DCOSS-IoT conference.
Sep 1, 2022 I will serve as a TPC member for the 2023 IEEE PerCom Work-In-Progress (WIP) session.
Aug 4, 2022 I will be attending Department of Energy Advanced Research Directions on AI for Science and Security (AI4SES) summer workshop series from August 16-18.
May 25, 2022 I was selected to serve as a TPC member for the 2022 IEEE SECON conference.
Apr 13, 2022 I successfully defended my doctoral dissertation entitled, “Smart Decision-Making via Edge Intelligence for Smart Cities”.
Apr 11, 2022 My paper entitled, “Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-Off with Federated Reinforcement Learning,” has been accepted for publication through the main conference track of IEEE SMARTCOMP 2022.

selected publications

  1. “Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-offs with Federated Reinforcement Learning”
    Hudson, Nathaniel, Oza, Pratham,  Khamfroush, Hana and 1 more author
    In 2022 IEEE International Conference on Smart Computing (SMARTCOMP) Jul 2022
  2. ICCCN
    “QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations”
    Hudson, NathanielKhamfroush, Hana,  and Lucani, Daniel E.
    In 2021 IEEE International Conference on Computer Communications and Networks (ICCCN) Big Data and Machine Learning for Networking (BDMLN) Workshop Jul 2021
  3. TNSE
    “Behavioral Information Diffusion for Opinion Maximization in Online Social Networks”
    Hudson, Nathaniel,  and Khamfroush, Hana
    IEEE Transactions on Network Science and Engineering (TNSE) Oct 2020