Monowar Hasan
Scholar

Monowar Hasan

Google Scholar ID: lLMWtnkAAAAJ
Assistant Professor, School of EECS, Washington State University
Cyber-Physical SystemsSecurityWireless Networks
Citations & Impact
All-time
Citations
1,675
 
H-index
18
 
i10-index
24
 
Publications
20
 
Co-authors
54
list available
Resume (English only)
Academic Achievements
  • Recent News:
  • - November 2025: Tamim received the competitive AWS Graduate Student Scholarship awarded by EECS.
  • - October 2025: Invited to serve on the technical program committee of VNC'26 and ECRTS'26.
  • - September 2025: Invited to serve on the technical program committee of RTAS'26.
  • - August 2025: Received the NSF CAREER award for a 5-year funding to detect and mitigate information leakage in time-critical cyber-physical systems.
  • - April 2025: Honored with the EECS Teaching 360 Award for teaching excellence and curricular contributions in computer systems.
Research Experience
  • Assistant Professor at the School of Electrical Engineering & Computer Science, Washington State University, leading the Cyber-Physical Systems Security Research Lab (CPS2RL). Previously, held an Assistant Professor position at the School of Computing, Wichita State University from 2021-2022.
Education
  • Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (UIUC); Doctoral work focused on securing real-time systems, particularly integrating security as a first-class design principle in real-time schedulers.
Background
  • Research Interests: Building trustworthy computer systems; Current research focus includes: (a) Security in real-time and cyber-physical systems, Internet-of-Things, and vehicular communication networks, (b) Building predictable and trustworthy machine learning models, and (c) Digital agriculture cybersecurity.
Miscellany
  • Looking forward to hiring two PhD students who will be working on the topics related to (i) security and resiliency of real-time and CPS/IoT/V2X/edge systems, (ii) quantum cyber-physical systems, and (iii) building resource-aware real-time machine learning frameworks.