• Professor of Practice, Computer Science & Engineering
  • Affiliated Faculty, Multidisciplinary Engineering
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Aakash Tyagi

Educational Background

  • Ph.D. Computer Engineering, University of Louisiana, Lafayette, Louisiana, 1993
  • M.S. Electrical and Computer Engineering, University of Louisiana, Lafayette, Louisiana, 1989
  • B.S. Electronics & Communication, Kamla Nehru Institute of Technology, Sultanpur, India, 1987

Research Interests

    • Hardware verification

    Teaching Interests

    • Computer Architecture
    • Data Structures and Algorithms
    • Hardware Verification

    Industry Experience

    • Twenty years of service at Intel in positions ranging from individual technical contributor to senior director in Server Development Group.
    • Worked on eight generations of CPU’s at Intel; most recently managed the design and execution of Knights Landing: 2015 2nd Generation Xeon Phi Product

Awards & Honors

  • Texas A&M Association of Former Students University-Level Distinguished Achievement Award for Individual Student Engagement, 2024
  • Texas A&M Open Educational Resources Award, 2023
  • Texas A&M CSE Undergraduate Faculty Teaching Excellence Award, 2022
  • Provost Academic Professional Track Faculty Teaching Excellence Award, 2022
  • Texas A&M Association of Former Students University-Level Distinguished Achievement Award in Teaching, 2021
  • Texas A&M Honoring Excellence Award for Outstanding Support of On-Campus Students’ Academic Success, 2020
  • Texas A&M Association of Former Students Distinguished Achievement Award in Teaching - College Level, 2019
  • Lambda Sigma Honor Society National Level Recognition (Advisor), 2018
  • Texas A&M ITS Instructional Pedagogy Award, 2018
  • Texas A&M CSE Undergraduate Faculty Teaching Excellence Award, 2017
  • Texas A&M College of Engineering Teaching Excellence Award, 2016
  • Texas A&M Center for Teaching Excellence Grant for Flipping Course Content, 2016
  • Texas A&M Professor of Practice Instructional Grant Award, 2015
  • Texas A&M CSE Undergraduate Faculty Teaching Excellence Award, 2015

Selected Publications

  • C. Liu, P. Parlapalli, D.K. Houngninou, M. Quinn, A Tyagi, J. Hu, “Improving Last-Mile Coverage in Functional Verification,” ACM/IEEE International Symposium on Machine Learning for CAD, September 2025.
  • S. Jasper, M. Luu, E. Pan, A Tyagi, M. Quinn, J. Hu, D.K. Houngninou, “BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis,” ACM/IEEE International Symposium on Machine Learning for CAD, September 2025.
  • M. Luu, S. Jasper, K. Le, E. Pan, M. Quinn, A. Tyagi, J. Hu, “VCDiag: Classifying Erroneous Waveforms for Failure Triage Acceleration,” ACM/IEEE International Symposium on Machine Learning for CAD, September 2025.
  • P. Sengupta, A. Tyagi, J. Hu, V.K. Rajan, H. Mostafa, S. Majumdar, “MinBLoG: Minimization of Boolean Logic Functions using Graph Attention Network,” ACM/IEEE International Symposium on Machine Learning for CAD, September 2024.
  • P. Sengupta, A. Tyagi, Y. Chen, J. Hu, “Quick Identification of Timing Critical Components in RTL Designs”, IEEE/ACM MLCAD Workshop, September 2023.
  • C. Chen, R. Kande, F. Andersen, A. Tyagi, A. Sadeghi, J. Rajendran, “HyPFuzz: Formal-Assisted Processor Fuzzing”, 32nd USENIX Security Symposium, August 2023.
  • P. Sengupta, A. Tyagi, Y. Chen, J. Hu, “How Good Is Your Verilog RTL Code? A Quick Answer from Machine Learning”, International Conference on Computer Aided Design, November 2022.
  • R. Kande, A. Crump, G. Persyn, P. Jauernig, A. Tyagi, A. Sadeghi, J.V. Rajendran, “TheHuzz: Instruction Fuzzing of Processors using Golden Reference Models for finding Software-Exploitable Vulnerabilities”, 31st USENIX Security Symposium, August 2022.
  • S. Gogri, J. Ju, A. Tyagi, et al, “Machine Learning-Guided Stimulus Generation for Functional Verification”, DVCON’20, San Jose, CA, March 2020.
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