Muhammad Rashed
استادیار · Electronic Design Automation (EDA)
University of Texas at Arlingtonمعرفی
Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015).
- Ph.D.: Computer Engineering, University of Central Florida, 2024
- B.S.: Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015
His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing.
The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals.
Scientific Awards:
- UTA CARES Grant for OER Creation
- Research Experiences for Undergraduates (REU) Grant
- Alireza Seyedi Doctoral Research Innovation Endowed Scholarship
- David T. & Jane M. Donaldson Memorial Scholarship
- IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination
- Best Research Video Award, Design Automation Conference (DAC)
Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college.
His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.
