Short Biography

Dr. Josh Siegel is an Assistant Professor of Computer Science and Engineering at Michigan State University and a frequent Lecturer at the Massachusetts Institute of Technology. His research explores emerging “Deep Technologies”—innovations that are disruptive, technically complex, and positioned for wide-scale societal impact. His lab focuses on connected and automated vehicles, pervasive sensing, applied AI, and secure systems.

Dr. Siegel holds Ph.D., S.M., and S.B. degrees in Mechanical Engineering from MIT and has authored over 100 peer-reviewed publications, with recognition from organizations including IEEE, SAE, and the Lemelson Foundation. His work has resulted in multiple issued and pending patents and has been featured by the Wall Street Journal, AutoNews, and NPR.

As a founder of multiple technology startups, Dr. Siegel has received national honors such as the Lemelson-MIT “Drive It” Prize and the MassIT Government Innovation Award. He also leads the development and global delivery of MIT’s DeepTech, IoT, and Innovation Bootcamps, reaching audiences from high school to the C-suite. His teaching and outreach emphasize hands-on learning, entrepreneurship, and equitable technology adoption.

The DeepTech Lab develops technologies that are transformative, technically challenging, and societally impactful. Current projects span autonomous and connected vehicles, resource-efficient AI, secure edge computing, and novel human-machine interfaces. By blending theory and application, the lab advances systems that are not only possible, but deployable—turning ideas once deemed impossible into everyday technologies.

Academic Positions

  • 2019 - Present Assistant Professor

    Michigan State University
    Computer Science and Engineering

  • 2017-2018 Research Scientist

    Massachusetts Institute of Technology
    Department of Mechanical Engineering

  • 2016-2017 Postdoctoral Associate

    Massachusetts Institute of Technology
    Department of Mechanical Engineering

Selected Awards

  • 2025 SAE International Educational Award Honoring Ralph R. Teetor
  • 2025 IEEE ISEC 3rd Best Paper Award
  • 2023 IEOM Smart Mobility First Prize Paper
  • 2020 IEEE Sensors Best Paper Award
  • 2018 ICAT-EGVE Best Demo Award
  • 2018 SCF AIMS Best Paper Award
  • 2015 Lemelson-MIT National Collegiate Student Prize Competition “Drive It” Winner
  • 2015 MassIT Government Innovation Competition Winner (CarKnow LLC)
  • 2014 MassChallenge Finalist (CarKnow LLC)
  • 2014 BMW-EURECOM “Highly Autonomous Driving in the IoT” Best Ideation Award
  • 2008 MIT Institute for Soldier Nanotechnologies Soldier Design Competition “Boeing” Prize Winner

Randomly-Selected Publications

This section shows three publications selected from the Publications page at random. It changes with every page reload.

More Publications

“Smartphone-Based Vehicular Tire Pressure and Condition Monitoring”

In "Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016", "2018"
"Siegel, Joshua

Value in vehicles: economic assessment of automotive data

Digital Policy, Regulation and Governance, 20(6): 513—527, 2018
Alexander M. Soley and Joshua E. Siegel and Dajiang Suo and Sanjay E. Sarma

Safe Energy Savings Through Context-Aware Hot Water Demand Prediction

Engineering Applications of Artificial Intelligence, 90: 103481, 2020
Siegel, Joshua and Das, Aniruddha and Sun, Yongbin and Pratt, Shane

List of Publications

Click here to see the full list of Professor Siegel's publications, with BibTex entries.

Research Projects

  • Cognitive Protection Systems (CPS)
    Generalizable AI-enhanced cybersecurity for constrained IoT devices
  • Pervasive Automotive Sensing Systems (PASS)
    Automotive subsystem fault detection using data from mobile phone sensors
  • Scalable Universal Diagnostic System (SUDS)
    Repurposing “data exhaust” to develop generalized algorithms for monitoring physical, electrical, and chemical systems
  • Physically-Adversarial Intelligent Networks (PAIN)
    Improving autonomous system performance through intentional, real-world adversarial engagement
  • Embedded Intelligence (EI)
    Architecting AI implementation for use on constrained systems