Prabhat Mishra
Professor | University of Florida
Gainesville, FL, UNITED STATES
Prabhat Mishra's research focuses on design and verification of energy-efficient and trustworthy electronic systems.
Biography
Prabhat Mishra is a professor in the Department of Computer and Information Science and Engineering and a UF Research Foundation professor, where he leads the CISE Embedded Systems Lab. His research interests include embedded and cyber-physical systems, hardware security and trust, computer architecture, energy-aware computing, formal verification, system-on-chip validation, machine learning and quantum computing.
Areas of Expertise (6)
Computer Architecture
Hardware Verification
Artificial Intelligence
Cybersecurity
Quantum Computing
Embedded Systems
Articles (3)
HIVE: Scalable Hardware-Firmware Co-Verification Using Scenario-Based Decomposition and Automated Hint Extraction
Institute of Electrical and Electronics EngineersAruna Jayasena and Prabhat Mishra
2024-04-01
Hardware-firmware co-verification is critical to design trustworthy systems. While formal methods can provide verification guarantees, due to the complexity of firmware and hardware, it can lead to state space explosion. There are promising avenues to reduce the state space during firmware verification through manual abstraction of hardware or manual generation of hints. Manual development of abstraction or hints requires domain expertise and can be time-consuming and error-prone, leading to incorrect proofs or inaccurate results.
State Preparation on Quantum Computers via Quantum Steering
Institute of Electrical and Electronics EngineersDaniel Volya and Prabhat Mishra
2024-01-24
Quantum computers present a compelling platform for the study of open quantum systems, namely, the nonunitary dynamics of a system. Here, we investigate and report digital simulations of Markovian nonunitary dynamics that converge to a unique steady state. The steady state is programmed as a desired target state, yielding semblance to a quantum state preparation protocol.
Hardware-Assisted Malware Detection and Localization Using Explainable Machine Learning
Institute of Electrical and Electronics EngineersZhixin Pan, et al.
2022-02-11
Malicious software, popularly known as malware, is widely acknowledged as a serious threat to modern computing systems. Software-based solutions, such as anti-virus software (AVS), are not effective since they rely on matching patterns that can be easily fooled by carefully crafted malware with obfuscation or other deviation capabilities. While recent malware detection methods provide promising results through an effective utilization of hardware features, the detection results cannot be interpreted in a meaningful way.
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