About

Prabhat Mishra is a Professor in the Department of Computer and Information Science and Engineering at the University of Florida and a UF Research Foundation Professor. His research spans embedded systems, hardware security, energy-aware computing, formal verification, system-on-chip validation, machine learning, and quantum computing. He is the author of 10 books and 42 book chapters, holds 30 patents, and has published more than 250 research articles in leading international journals and conferences. His work has been recognized with numerous honors, including an NSF CAREER Award, an IBM Faculty Award, three Best Paper Awards, eleven Best Paper nominations, and the EDAA Outstanding Dissertation Award. He is a Fellow of the IEEE, a Fellow of the AAAS, and a Distinguished Member of the ACM.

Research topics

• Computer Science
• Computer Security
• Embedded system
• Operating system
• Machine Learning
• Telecommunications
• Parallel computing
• Reliability engineering
• Computer network
• Real-time computing
• Computer engineering
• Engineering

Selected publications

• The Future of Fault-Tolerant Quantum Computing

  2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Quantum Testing and Validation

  2026-01-01

  book-chapterSenior author
  PublisherDOI

• Quantum Artificial Intelligence

  2026-01-01

  book-chapterSenior author
  PublisherDOI

• Design Automation Challenges in Quantum Computing

  2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Quantum Computing Architectures

  2026-01-01

  book-chapterSenior author
  PublisherDOI

• State Preparation for Quantum Computing

  2026-01-01

  book-chapterSenior author
  PublisherDOI

• Optimizing Generative Adversarial Networks with Tensor Decompositions for Resource-Constrained Systems

  IEEE Embedded Systems Letters · 2026-01-01

  articleSenior author

  Modern computing systems rely on generative AI algorithms to create new content or make decisions based on current observations. It is difficult to utilize generative AI in resource-constrained embedded devices since generative models have large memory requirements due to the presence of high-dimensional tensors within the model’s layers. In this paper, we utilize tensor decomposition to reduce the memory requirements in generative adversarial networks. Specifically, we explore diverse tensor decomposition methods to decompose high-dimensional tensors into several smaller-dimensional tensors with minimal information loss. Our experimental results demonstrate that tensor decomposition can provide significant reduction in memory requirements (58% in training memory and 64% in model storage) while maintaining the quality of the generated images. This work would serve as a stepping stone for designing memory-efficient generative AI algorithms for resource-constrained embedded systems.

  PublisherDOI

• Emerging Trends in cervical cancer Treatment: Transitioning from traditional to innovative delivery strategies

  International Journal of Pharmaceutics · 2025-06-18 · 4 citations

  reviewOpen access

  Cervical cancer (CC) remains the second most common cause of cancer-related deaths among women in the United States, following breast cancer. As per American Cancer Society reports, with approximately 4,320 deaths expected annually, it continues to pose a significant threat to global public health. The highest prevalence of CC has been reported in low to middle-income nations in Asia, Africa, and Latin America, where screening and treatment are scarce. Although surgery, radiation therapy, and chemotherapy have long been the cornerstones of CC care, their drawbacks, such as exorbitant expenses, restricted availability, and serious side effects, including neuropathy and infertility, highlight the need for creative alternatives. Current research demonstrates the effectiveness of cutting-edge treatments; for example, pembrolizumab (FDA approval, 2021) immunotherapy has been shown to improve progression-free survival in metastatic CC by 30%. In preclinical models, drug delivery systems based on nanotechnology, such as cisplatin-loaded nanoparticles, have shown a 40% increase in tumor drug concentration with decreased systemic toxicity. By addressing the limitations of existing treatments and concentrating on the pathophysiology of the illness, particularly HPV-driven oncogenesis, this study explores the shift from traditional to advanced CC therapy. It looks at diagnostic innovations such as early detection using MRI and ultrasound, as well as molecular diagnostics with computer-aided methods that have a 25% higher sensitivity in detecting precancerous lesions. The review also discusses therapeutic innovations, ranging from conventional methods like surgery and chemotherapy to newer approaches like photodynamic therapy, which has been shown to reduce tumors by 60% in early-stage CC trials, nanomedicine, targeted therapies (like bevacizumab), localized drug delivery via vaginal gels, nanofibers, intravaginal rings, immunotherapy, and gene therapy. It provides a thorough understanding of the changing treatment environment by analysing ongoing clinical studies and patient patterns.

  PublisherDOI

• Traffic Analysis Attacks on Wireless NoC-Based SoCs

  2025-05-05

  articleSenior author

  Network-on-Chip (NoC) enables on-chip communication between diverse cores in modern System-on-Chip (SoC) designs. Despite the advantages of wireless NoCs (WiNoC) over its electrical counterpart, the use of a shared medium during wireless communication introduces a unique set of vulnerabilities. In WiNoCs, the medium access control (MAC) protocol orchestrates traffic flow across wireless hubs, shaping how traffic patterns emerge based on the protocol and the applications. In this paper, we propose a traffic analysis attack on wireless traffic governed by MAC to infer applications running in an SoC. Specifically, this paper the following major contributions. We outline a threat model involving a malicious wireless hub and a colluding application to leak traffic traces. We utilize deep learning to exploit spatiotemporal traffic features of the MAC protocol to infer applications running in the system, posing significant security and privacy risks. Extensive evaluation demonstrates that our proposed traffic analysis attack can infer applications with high accuracy (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$95\%-99\%$</tex>) across diverse WiNoC configurations and applications. We also propose a lightweight countermeasure to defend against traffic analysis attack that uses stochastic scheduling of wireless packets over virtual wireless hubs. Experimental results show that our proposed countermeasure can defend against traffic analysis attacks with minimal overhead.

  PublisherDOI

• Topology-aware Detection and Localization of Distributed Denial-of-Service Attacks in Network-on-Chips

  ArXiv.org · 2025-05-20

  preprintOpen accessSenior author

  Network-on-Chip (NoC) enables on-chip communication between diverse cores in modern System-on-Chip (SoC) designs. With its shared communication fabric, NoC has become a focal point for various security threats, especially in heterogeneous and high-performance computing platforms. Among these attacks, Distributed Denial of Service (DDoS) attacks occur when multiple malicious entities collaborate to overwhelm and disrupt access to critical system components, potentially causing severe performance degradation or complete disruption of services. These attacks are particularly challenging to detect due to their distributed nature and dynamic traffic patterns in NoC, which often evade static detection rules or simple profiling. This paper presents a framework to conduct topology-aware detection and localization of DDoS attacks using Graph Neural Networks (GNNs) by analyzing NoC traffic patterns. Specifically, by modeling the NoC as a graph, our method utilizes spatiotemporal traffic features to effectively identify and localize DDoS attacks. Unlike prior works that rely on handcrafted features or threshold-based detection, our GNN-based approach operates directly on raw inter-flit delay data, learning complex traffic dependencies without manual intervention. Experimental results demonstrate that our approach can detect and localize DDoS attacks with high accuracy (up to 99\%) while maintaining consistent performance under diverse attack strategies. Furthermore, the proposed method exhibits strong robustness across varying numbers and placements of malicious IPs, different packet injection rates, application workloads, and architectural configurations, including both 2D mesh and 3D TSV-based NoCs. Our work provides a scalable, flexible, and architecture-agnostic defense mechanism, significantly improving the availability and trustworthiness of on-chip communication in future SoC designs.

  PublisherOA PDFDOI

Recent grants

• SHF: Small: Design-for-Debug Architecture for Post-Silicon Security Validation

  NSF · $608k · 2019–2024

• SaTC: CORE: Small: Trustworthy System-On-Chip Design using Secure On-Chip Communication Architecture

  NSF · $580k · 2019–2024

• CAREER: New Directions in Functional Verification of Heterogeneous Multicore Architectures

  NSF · $400k · 2008–2014

• CSR: Small: Collaborative Research: Dynamic Reconfiguration for Adaptive Computing in Heterogeneous SoCs

  NSF · $308k · 2015–2019

• SHF: Small: Automated Techniques for Efficient Post-Silicon Validation and Debug of Integrated Circuits

  NSF · $474k · 2012–2017

Frequent coauthors

• Magdy S. Abadir

  Norwegian University of Science and Technology

  2707 shared

• Mark Tehranipoor

  University of Florida

  2706 shared

• Nabil Khaled

  University of South Florida

  2704 shared

• Masanori Hashimoto

  Kyoto University

  2704 shared

• Xiaoqing Wen

  Kyushu Institute of Technology

  2704 shared

• Chirn Chye Boon

  Nanyang Technological University

  2704 shared

• Jos¿ Pineda de Gyvez

  Princeton University

  2704 shared

• Raffaele De

  Drexel University

  2704 shared

Education

• Ph.D., Information and Computer Science

  University of California Irvine

  2004

• M.Tech., Computer Science and Engineering

  Indian Institute of Technology Kharagpur

  1995

• B.E., Computer Science and Engineering

  Jadavpur University

  1994

Awards & honors

• NSF CAREER Award
• IBM Faculty Award
• three Best Paper Awards
• eleven Best Paper nominations
• EDAA Outstanding Dissertation Award