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

Apr 8, 2024 I have been accepted to participate in the 2024 Cyber Physical Systems (CPS) Rising Stars Workshop at the University of Virginia. The acceptance rate was roughly 16%; so I am very humbled to have been chosen to participate in this program.
Mar 23, 2024 Very honored to announce that my paper entitled, “QoS-Aware Edge AI Placement and Scheduling with Multiple Implementations in FaaS-based Edge Computing” has been recently accepted to appear in the Future Generation Computer Systems as part of the journal’s special issue on “Serverless Computing in the Cloud-to-Edge Continuum”. This paper poses the problem of deploying AI models at the edge and then scheduling requests for them in a QoS-aware manner. The use of federated learning to predict incoming requests is also applied.
Feb 17, 2024 I will be serving as a member of the TPC for the 2024 IEEE International Conference on Edge Computing & Communications (EDGE) conference, as part of the IEEE World Congress on SERVICES. Submit any relevant papers (specifically on edge computing, edge AI, edge-to-cloud solutions, etc.) to this conference.
Feb 13, 2024 I am thrilled to serve as a member of the TPC for the 2024 IEEE International Conference on Computer Communications and Networks (ICCCN) conference. Please submit any relevant papers to this venue.
Jan 22, 2024 Happy to be joining the TPC for the 2024 IEEE Mobile Ad-Hoc and Smart Systems (MASS) conference.
Jan 12, 2024 I will be serving as the web chair for the 2024 IEEE e-Science conference.
Nov 29, 2023 I am excited to announce that I will, once again, serve as a TPC member for the IEEE PerCom 2024 Work-In-Progress (WIP) session.
Oct 30, 2023 Paper entitled, “Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision”, accepted for publication through the 2023 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies conference.
Oct 8, 2023 A paper investigating multi-hop reasoning capabilities of Large Language Models has been accepted for publication through the 2023 BlackBoxNLP workshop. A preprint of the paper is available here.
Sep 28, 2023 I will be giving a talk on federated learning on the Globus Compute platform on October 13th, 2023 as part of the Rural AI Workshop on Serverless Federated Learning for Remote Internet-of-Things Applications (click here to learn how to attend).

selected publications

  1. FGCS
    “QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing”
    Hudson, NathanielKhamfroush, Hana,  Baughman, Matt and 3 more authors
    Future Generation Computer Systems 2024
  2. “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
  3. 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
  4. 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