Dr. Jeffrey Chavis is a member of the principal professional staff at the Johns Hopkins University Applied Physics Laboratory (APL) and currently serves as the chief engineer for the applied data science branch at APL. He leads the development and application of advanced data science analytical techniques to provide data driven insights to solve problems in various domains including, but not limited to, cyber security, disease prediction, and artificial intelligence.

Dr. Chavis currently teaches in Johns Hopkins University’s Engineering for Professionals programs in both the software engineering and the information systems engineering programs. He serves as the Vice chair for the Baltimore Chapter of the Association of Computer Machinery, and is a senior member of IEEE. He was recognized with the Black Engineer of the Year Award (BEYA) for Professional Achievement at the BEYA STEM Global Competitiveness Conference in 2016.

Dr. Chavis earned a BS in Electrical Engineering from Howard University, an MS in Electrical Engineering from the University of Maryland, College Park and a D.Eng degree from Johns Hopkins University where he researched methods to secure the Internet of Things through application of Machine Learning.

 

Education History

  • BSEE Electrical Engineering, Howard University
  • MSEE Electrical Engineering, University of Maryland
  • DENG Computer Science, Johns Hopkins University

Work Experience

Principal Professional Staff, JHU Applied Physics Laboratory

Publications

Chavis, J. S. (2021). TOWARD ASSURANCE AND TRUST FOR THE INTERNET OF THINGS. Dissertation in Defense of the Degree of Doctor of Engineering. https://jscholarship.library.jhu.edu/handle/1774.2/64098

Chavis, Jeffrey S, Malcom Doster, Michelle Feng, Syeda Zeeshan, Samantha Fu, Elizabeth Aguirre, Antonio Davila, and Kofi Nyarko. 2021. “A Voice Assistant for IoT Cybersecurity.” In IEEE Integrated STEM Education Conference 2021. Princeton, NJ: IEEE Explore.

Chavis, J. S. (JHU/APL), & Syed, D. P. (JHU/APL). (2020). Envisioning Cybersecurity Analytics for the Internet of Things. In IEEE (Ed.), IEEE 5G World Forum. IEEE Explore. Mumbai, India

Hegde, M., Chavis, J. S., Kepnang, G., Mazroei, M. Al, Watkins, L., Johns, T., Identification of Botnet Activity in IoT Network Traffic Using Machine Learning, Valencia Spain; http://intelligenttech.org/IDSTA2020/

Chavis JS, Kunz A, Watkins LA, Rubin A, Buczak AL. A Capability for Autonomous IoT System Security : Pushing IoT Assurance to the Edge. In: 2nd Annual Workshop on Assured Autonomous Systems (IEEE WAAS 2020). San Francisco, CA, United states

J. S. Chavis, A. Buczak, A. Rubin and L. A. Watkins, “Connected Home Automated Security Monitor (CHASM): Protecting IoT Through Application of Machine Learning,” 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2020, pp. 0684-0690.

A survey of deep learning methods for cyber security; DS Berman, AL Buczak, JS Chavis, CL Corbett
Information 2019

Using sequential pattern mining for common event format (CEF) cyber data
AL Buczak, DS Berman, SW Yen, LA Watkins, LT Duong, JS Chavis; Proceedings of the 12th annual conference on cyber and information security 2017

Detection of tunnels in PCAP data by random forests; AL Buczak, PA Hanke, GJ Cancro, MK Toma, LA Watkins, JS Chavis; Proceedings of the 11th Annual Cyber and Information Security Research 2016

Unsupervised Machine Learning by Graph Analytics on Heterogeneous Network Device Data; JS Lin, E Guven, LT Duong, JS Chavis, MD Dinmore, PA Hanke, BG Magen; Procedia Computer Science 140, 144-151 2016

Sensor-Based Adaptive Methods for Wearable Devices
T Gao, AM Alm, SM Babin, JS Chavis; US Patent App. 12/632,890 2010

Sensor-based adaptive wearable devices and methods
T Gao, J Chavis, WE Bishop, RR Juang, AM Alm, DM White, DA Crawford; US Patent 7,629,881 2009

Honors and Awards

  • Black Engineer of the Year – Professional Achievement in Industry (2016)

Professional Organizations

IEEE
NSBE
ACM