Ms. Ujunwa Madububa Mbachu | Cybersecurity | Best Researcher Award

Ms. Ujunwa Madububa Mbachu | Cybersecurity | Best Researcher Award

Ms. Ujunwa Madububa Mbachu, University of Southern Mississippi, United States

Ms. Ujunwa Madububa Mbachu is a Ph.D. candidate in Computer Science (Cybersecurity) at the University of Southern Mississippi, USA. She is a Visiting Instructor at the School of Computing Sciences and Computer Engineering and a Research Associate at the SPEN Lab, focusing on security and privacy in emerging networks. With extensive experience in teaching, research, and industry leadership, she is the President of the Cyberwatch Foundation, promoting inclusivity in cybersecurity education. Her expertise spans cybersecurity, machine learning, cloud computing, and privacy protection. She has received prestigious awards, including the Dissertation Completion Grant and Hall of Fame Induction at her university.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Best Researcher Award 

Ms. Mbachu is an exceptional candidate for the Best Researcher Award due to her groundbreaking contributions in cybersecurity, privacy, and emerging network security. Her Ph.D. research on Secure and Privacy-Aware Traffic Management Services in Autonomous Vehicles addresses critical global challenges in data protection and smart transportation. She has authored impactful research, led cybersecurity initiatives, and mentored students in computing sciences. As a leader in the Cyberwatch Foundation, she actively bridges academic research and real-world applications. Her dedication to advancing cybersecurity knowledge and fostering technological inclusivity makes her a highly deserving nominee for this prestigious recognition.

🎓 Education 

Ms. Mbachu is currently pursuing a Ph.D. in Computer Science (Cybersecurity) at the University of Southern Mississippi (2021–2025), with a dissertation on Secure and Privacy-Aware Traffic Management Services in Autonomous Vehicles, under the supervision of Dr. Ahmed Sherif. She earned an M.Sc. in Information Technology from the National Open University (NOUN), Nigeria (2017), focusing on the socioeconomic implications of national security and privacy systems. Her academic foundation includes a strong background in computer science, cybersecurity, and privacy research, preparing her for innovative contributions in data security, artificial intelligence, and cloud computing.

👩‍💻 Professional Experience

Ms. Mbachu is a Visiting Instructor at the University of Southern Mississippi, teaching computer science and IT courses across various modalities. She has also served as a Graduate Teaching Assistant, mentoring students and supporting research in cybersecurity. As a Research Associate at the SPEN Lab, she actively investigates security and privacy challenges in emerging networks. In the industry, she is the President of Cyberwatch Foundation, driving cybersecurity education initiatives. Her roles in academia and leadership demonstrate her commitment to advancing cybersecurity knowledge and empowering future researchers in the field.

🏅 Awards & Honors

Ms. Mbachu has received numerous accolades, including the 2025 Graduate School Dissertation Completion Grant and Hall of Fame Induction at the University of Southern Mississippi. She was also awarded the 2025 Student Travel Grant for her outstanding contributions to research. In 2021, she was honored with the College of Arts & Science Student Travel Award for her impactful academic work. These recognitions highlight her excellence in cybersecurity research, academic performance, and leadership in technology education. Her commitment to innovation and mentorship in cybersecurity has earned her prestigious acknowledgments from both academic and professional institutions.

🔬 Research Focus 

Ms. Mbachu’s research spans cybersecurity, privacy protection, machine learning, deep learning, and cloud computing. Her work focuses on securing emerging networks, with particular interest in privacy-aware traffic management in autonomous vehicles. She explores how artificial intelligence and cryptographic models enhance data security in smart infrastructures. Her studies also address cloud security, cyber-attack prevention, and AI-driven risk assessments. Through her leadership at the Cyberwatch Foundation, she advocates for inclusive cybersecurity education. Her multidisciplinary research contributes to both theoretical advancements and real-world cybersecurity applications, ensuring safer digital ecosystems in emerging technologies.

📖 Publication Top Notes 

  1. Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review
    • Year: 2024
    • Citations: 7
  2. Predictive Machine Learning Approaches for Mental Health Diagnoses in College Students
    • Year: 2024
  3. A Review of Machine Learning Techniques to Predict Mental Health Diagnoses
    • Year: 2024
  1. Secure and Privacy-Preserving Aggregation Scheme for Traffic Management Systems
    • Year: 2023
    • Citations: 2
  2. Hardware-Acceleration Based Privacy-Aware Authentication Scheme for Internet of Vehicles
    • Year: 2024
  3. Privacy-Aware and Hardware Acceleration-Based Aggregation Scheme for Smart Grid Networks
    • Year: 2023

 

 

Prof Dr. Jiliang Zhang | Hardware Security | Best Researcher Award

Prof Dr. Jiliang Zhang | Hardware Security | Best Researcher Award

Prof Dr. Jiliang Zhang, Hunan University, China

Prof. Dr. Jiliang Zhang, an eminent figure in computer science and technology, currently affiliated with Hunan University, China. 🎓 His academic journey includes a Ph.D. in Computer Science and Technology from Hunan University, where he received accolades for his dissertation on “Security and Trust for FPGA-based Systems.” 🏅 Prof. Zhang’s research interests span hardware security, including areas like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs), as well as new computing architectures such as in-memory computing and brain-inspired computing. 🔬 With a rich professional experience, including positions as a research scholar at the University of Maryland and visiting researcher at Tsinghua University, Prof. Zhang brings a wealth of knowledge and expertise to his role as a professor at Hunan University.

Professional Profile:

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science and Technology – Hunan University, China (2015)
    Dissertation: “Security and Trust for FPGA-based Systems” (Outstanding Doctoral Dissertation)
  • B.E. in Chemical Engineering and Technology – Shandong University of Science and Technology, China (2009)

Honors and Awards 🏅

  • World’s Top 2% Scientists – Stanford University, 2020-2023 (Ranked 36th in 2023 and 16th in 2022 in computer hardware and architecture)
  • CCF Distinguished Lecturer – 2022, 2023
  • Second Natural Science Award – Hunan Province, 2022

Research Interests 🔬

  • Hardware Security: Physical Unclonable Functions (PUFs), True Random Number Generators (TRNGs), Hardware Obfuscation, IP Protection, FPGA Security, Hardware Trojan Detection, Cryptographic Accelerators, CPU Security, and Applications in Secure Systems.
  • New Computing Architectures: In-memory Computing and Brain-inspired Computing.

Professional Experience 🏫

  • Professor – Hunan University (Dec 2020 – Present)
  • Associate Professor – Hunan University (May 2017 – Dec 2020)
  • Associate Professor – Northeastern University (May 2015 – Apr 2017)
  • Research Scholar – University of Maryland, College Park (Sept 2013 – Sept 2014)
  • Visiting Researcher – Tsinghua University, Beijing (Aug 2012 – Sept 2012; Sept 2010 – Jun 2011)

Google Scholar Metrics 📊

  • Citations: 3025
  • H-index: 29

Publication Top Notes:

  1. Design and Application of Programmable Analog Circuit for Solving Lyapunov Matrix Equation Based on Memristors
    • Journal: IEEE Transactions on Industrial Electronics
    • Year: 2024
  1. Design of Artificial Neurons of Memristive Neuromorphic Networks Based on Biological Neural Dynamics and Structures
    • Journal: IEEE Transactions on Circuits and Systems I: Regular Papers
    • Year: 2024
  2. FTOP: An Efficient Flow Table Overflow Preventing System for Switches in SDN
    • Journal: IEEE Transactions on Network Science and Engineering
    • Year: 2024
  3. Quantization Backdoors to Deep Learning Commercial Frameworks
    • Journal: IEEE Transactions on Dependable and Secure Computing
    • Year: 2024
  4. Timing Side-channel Attacks and Countermeasures in CPU Microarchitectures
    • Journal: ACM Computing Surveys
    • Year: 2024