Wei Liu | Neuromorphic | Best Researcher Award

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Dr. Wei Liu | Neuromorphic | Best Researcher Award

Doctor at Sun Yat-Sen University, China

Professional Profile

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Summary

Dr. Wei Liu is a dedicated postdoctoral researcher specializing in microelectronics, hardware security, and neuromorphic computing. Based in Guangzhou, he is currently affiliated with the School of Microelectronics Science and Technology at Sun Yat-sen University. With a strong foundation in communications and microelectronics, Dr. Liu has built a multidisciplinary research profile at the intersection of neural hardware, stochastic computing, and secure circuit design. As an IEEE member, he actively contributes to the academic community through impactful publications and collaborative projects.

Educational Details

Dr. Liu earned his Ph.D. in Microelectronics and Solid-State Electronics from Sun Yat-sen University in 2024, where he focused on integrating stochastic methods with neuromorphic design. He completed his M.S. in Microelectronics from Tsinghua University, Beijing, in 2015, gaining advanced training in semiconductor devices and circuit design. His academic journey began with a B.S. in Communications Engineering from Wuhan University of Technology in 2010, laying the foundation for his future research in intelligent and secure hardware systems.

Professional Experience

Currently, Dr. Liu serves as a postdoctoral researcher at the School of Microelectronics Science and Technology, Sun Yat-sen University. In this role, he is engaged in cutting-edge research projects involving neuromorphic hardware development, stochastic computing applications, and secure architecture design. His work includes system-level modeling, hardware acceleration, and circuit optimization for low-power and fault-tolerant computing. He has collaborated with leading researchers and contributed to multiple high-impact journals and conferences.

Research Interests

Dr. Liu's primary research interests lie in neuromorphic computing, where he develops brain-inspired systems for efficient and adaptive processing; stochastic computing, exploring its potential in energy-constrained and approximate computing; and hardware security, focusing on resilient architectures and cryptographic techniques to mitigate vulnerabilities in modern electronic systems. His interdisciplinary approach addresses pressing challenges in next-generation intelligent electronics.

Author Metrics

Dr. Liu has co-authored several peer-reviewed articles in high-impact journals such as IEEE Transactions on Biomedical Circuits and Systems and Electronics. Notable publications include innovations in approximate spiking neural networks and biologically plausible neuron models using stochastic computation. His work has been cited for its contributions to low-cost and energy-efficient neuromorphic architectures. His metrics reflect a growing influence in the field of microelectronics and computational neuroscience.

Awards and Honors

Dr. Liu has been recognized for his academic excellence and research contributions, earning accolades at national and international conferences. His work on SC-IZ and SC-PLR has received attention for bridging biological plausibility with practical neuromorphic hardware implementations. As an IEEE member, he actively participates in technical communities and workshops, contributing to the advancement of low-power intelligent systems.

Publication Top Notes

1. SCSC: Leveraging Sparsity and Fault-Tolerance for Energy-Efficient Spiking Neural Networks

  • Publication Date: January 20, 2025

  • Publication Type: Conference Paper

  • Conference: 30th Asia and South Pacific Design Automation Conference (ASP-DAC)

  • DOI: 10.1145/3658617.3697718

  • Contributors: Bo Li, Yue Liu, Wei Liu, Jinghai Wang, Xiao Huang, Zhiyi Yu, Shanlin Xiao

  • Summary: This paper presents the SCSC framework that enhances energy efficiency in spiking neural networks by leveraging neuron-level sparsity and fault-tolerance features. It targets low-power applications in neuromorphic computing systems.

2. SC-PLR: An Approximate Spiking Neural Network Accelerator With On-Chip Predictive Learning Rule

  • Publication Date: October 2024

  • Publication Type: Journal Article

  • Journal: IEEE Transactions on Biomedical Circuits and Systems

  • Contributors: Wei Liu, Shanlin Xiao, Yue Liu, Zhiyi Yu

  • Summary: SC-PLR introduces a neuromorphic accelerator using a novel predictive learning rule implemented on-chip. It reduces computational complexity while maintaining biological plausibility and hardware efficiency.

3. SC-IZ: A Low-Cost Biologically Plausible Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic Computing

  • Publication Date: February 27, 2024

  • Publication Type: Journal Article

  • Journal: Electronics (MDPI), Volume 13, Issue 5, Article 909

  • Contributors: Wei Liu, Shanlin Xiao, Bo Li, Zhiyi Yu

  • Summary: This work proposes SC-IZ, a biologically plausible and low-cost implementation of the Izhikevich neuron using stochastic computing, scalable for large neuromorphic systems.

4. Low-Cost Adaptive Exponential Integrate-and-Fire Neuron Using Stochastic Computing

  • Publication Date: October 2020

  • Publication Type: Journal Article

  • Journal: IEEE Transactions on Biomedical Circuits and Systems, Volume 14, Issue 5, Pages 942–950

  • Contributors: Shanlin Xiao, Wei Liu, Yuhao Guo, Zhiyi Yu

  • Summary: This paper introduces a compact, low-power model of the adaptive exponential integrate-and-fire neuron leveraging stochastic computing techniques for improved resource efficiency in neuromorphic hardware.

Conclusion

Dr. Wei Liu is an outstanding candidate for the Best Researcher Award in neuromorphic and secure computing, with groundbreaking contributions at the intersection of biology and technology. His innovative work on energy-efficient neural hardware, stochastic computing, and hardware security is shaping the future of intelligent systems. While expanding his global visibility could further amplify his impact, his research trajectory reflects not just promise but accelerating momentum, making him a deserving recipient of this recognition.

Abdullah Abonamah | Machine Learning | Best Researcher Award

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Prof. Abdullah Abonamah | Machine Learning | Best Researcher Award

Research Affiliate at George Washington University, United States

Prof. Abdullah A. Abonamah is a distinguished academic and technology leader with over 40 years of expertise in artificial intelligence (AI), machine learning, and higher education. He currently serves as a Professor of Computing and AI at George Washington University and Chairman of AI Learning Solutions in the UAE. Dr. Abonamah has held key leadership roles, including President of the Abu Dhabi School of Management and CEO of the UAE Academy. He holds a Ph.D. in Computer Science from the Illinois Institute of Technology and has contributed extensively to AI research, focusing on AI integration in business processes, healthcare, smart cities, and cybersecurity. With over 10 patents in AI-driven systems and numerous scholarly publications, his work is widely cited in both academia and industry. Dr. Abonamah has secured over $1 million in research funding and has received several prestigious awards, including the Government of Abu Dhabi Recognition Award. His innovative projects have influenced AI and digital transformation strategies globally, and he has represented the UAE in international delegations. Prof. Abonamah’s leadership, combined with his groundbreaking research, positions him as a deserving candidate for the Best Researcher Award.

Professional Profile
Scopus
Google Scholar

Summary

Dr. Abdullah A. Abonamah is a highly accomplished academic, technology strategist, and business leader with over four decades of experience in computing, artificial intelligence, and higher education leadership. He currently serves as Professor of Computing and AI at George Washington University’s Environmental and Energy Management Institute and is Chairman of the Board of AI Learning Solutions in the UAE. Dr. Abonamah has held numerous executive, academic, and advisory roles, including President and Provost of the Abu Dhabi School of Management, and CEO of the UAE Academy. His work bridges academia, innovation, and industry with a focus on AI adoption, data strategy, and digital transformation.

Educational Background

Dr. Abonamah holds a Ph.D. in Computer Science from the Illinois Institute of Technology, USA, and an M.S. in Computer Science and Engineering from Wright State University. He earned his B.S. in Computer Science from the University of Dayton and later obtained an Executive Management Certificate from Yale School of Management. His multidisciplinary academic foundation has empowered his leadership in both technical research and institutional development.

Professional Experience

Dr. Abonamah has served in numerous high-impact roles, including:

  • Professor of Computing at Abu Dhabi School of Management (2007–2024)

  • Professor of IT at Zayed University (2000–2007)

  • Director of the Institute for Technological Innovation (Dubai Internet City)

  • Chair, AI Management Institute at ADSM

  • Business leader and strategist in several startups and research institutes
    In his leadership positions, he led major organizational transformations, managed multimillion-dollar budgets, implemented ERP and AI systems, developed academic programs, and fostered public-private partnerships. He also served as Dean, Program Director, and Assistant Dean in various institutions, ensuring accreditation and global standards compliance.

Research Interests

Dr. Abonamah’s research spans artificial intelligence, machine learning, cybersecurity, fault-tolerant computing, and innovation ecosystems. His recent work focuses on the integration of AI into business processes, human-centered machine learning, and strategic data governance. He is also involved in applied AI projects in healthcare, smart cities, and education technology.

Author Metrics

Dr. Abonamah has authored and co-authored dozens of journal articles, book chapters, and conference papers, many of which are indexed in Scopus, IEEE, and Web of Science. He holds multiple patents and intellectual property certificates for AI-driven systems, ERP modules, academic tools, and mobile apps. His scholarship includes both foundational theory and practical implementations, making his work highly cited in both academic and industry domains.

Awards and Honors

Dr. Abdullah A. Abonamah has been the recipient of numerous prestigious awards and recognitions throughout his distinguished career. He was honored with the Government of Abu Dhabi Recognition Award in 2017 for his outstanding contributions to higher education and institutional development. Over the years, he has secured multiple competitive research grants totaling more than $1 million, including major funding for the development of AI and cybersecurity programs by the Federal Authority for Identity, Citizenship, Customs & Port Security and the Emirates Academy for Identity and Citizenship. His innovative projects have led to the creation of several intellectual property-certified digital systems, earning formal recognition from the UAE Ministry of Economy, with over 10 patented and certified software applications in AI, ERP systems, and academic tools. Dr. Abonamah was also awarded the US State Department MEPI Grant for the Emirati Women’s Organizational Leadership Program and received the Microsoft Instructional Lab Grant and other major institutional grants for research labs and technology initiatives. Recognized for his leadership, he has represented the UAE on international delegations, including a technological mission to Japan, and has consistently been acknowledged for his impactful work in promoting innovation, entrepreneurship, and digital transformation in education and governance.

Publication Top Notes

1. A Collaborative Adaptive Cybersecurity Algorithm for Cognitive Cities

  • Authors: A. Abonamah, F.N. Sibai

  • Published in: Journal of Computer Information Systems, 2025, pp. 1–16

  • Summary:
    This paper introduces a novel adaptive cybersecurity algorithm specifically designed for cognitive cities, which rely heavily on interconnected AI systems and IoT infrastructure. The algorithm leverages collaborative machine learning, enabling various smart subsystems to share threat intelligence and dynamically adjust defenses in real time. The model improves resilience, threat detection speed, and situational awareness, offering a scalable security solution for complex urban networks.

2. Managerial Insights for AI/ML Implementation: A Playbook for Successful Organizational Integration

  • Authors: A.A. Abonamah, N. Abdelhamid

  • Published in: Discover Artificial Intelligence, 2024, Vol. 4(1), Article 22

  • Summary:
    This publication acts as a strategic guide for executives and IT leaders aiming to deploy AI and machine learning within organizations. It outlines a structured playbook, highlighting critical success factors, common pitfalls, change management practices, and technology readiness considerations. The work is grounded in case studies and offers a framework for bridging technical solutions with organizational goals.

3. Discover Artificial Intelligence

  • Authors: A.A. Abonamah, N. Abdelhamid

  • Published in: Discover, 2024, Vol. 4, Article 22

  • Summary:
    This appears to be a companion piece or an editorialized version of the article above, with expanded commentary on AI governance, leadership roles, and ethical implementation frameworks. It emphasizes building institutional capability and fostering innovation culture for sustainable AI integration.

4. Wearable Sensor-Based Device for Predicting, Monitoring, and Controlling Epilepsy and Methods Thereof

  • Inventors: M.U. Tariq, A.A. Abonamah

  • Filing Number: US Patent App. 18/107,839

  • Filed in: 2023

  • Summary:
    This patent proposes a wearable biomedical device equipped with sensor arrays and AI algorithms for the real-time detection, prediction, and intervention of epileptic seizures. The system analyzes physiological data—such as ECG, EEG, and temperature signals—and uses machine learning to anticipate seizure events, offering alerts or therapeutic responses. It aims to enhance autonomous patient care and reduce medical emergencies, particularly in outpatient or home settings.

5. Artificial Intelligence Technologies and Platforms

  • Authors: M.U. Tariq, A. Abonamah, M. Poulin

  • Published in: Engineering Mathematics and Artificial Intelligence, 2023, pp. 211–226

  • Summary:
    This book chapter provides an in-depth analysis of leading AI platforms and ecosystems, such as TensorFlow, PyTorch, and Azure AI. It covers architecture, deployment strategies, and use cases across domains like healthcare, finance, and smart cities. The chapter emphasizes the selection criteria for AI tools, and how platform choices affect scalability, maintainability, and compliance in enterprise contexts.

Conclusion

Prof. Abdullah A. Abonamah is an outstanding and highly deserving candidate for the Best Researcher Award. His blend of academic scholarship, applied innovation, institutional leadership, and global impact positions him uniquely at the intersection of technology and societal advancement. His research addresses real-world challenges with AI-driven solutions, while his leadership roles have built enduring institutions and empowered future generations.

Given his contributions to AI research, higher education reform, cross-sectoral innovation, and IP development, Prof. Abonamah clearly meets and exceeds the criteria for this award. He is not only a prolific scholar but also a visionary leader and mentor, making him an ideal recipient of the Best Researcher Award.