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.

Shakila Rahman | Machine Learning | Best Researcher Award

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Ms. Shakila Rahman | Machine Learning | Best Researcher Award

Lecturer at American International University, Bangladesh

Author Profile

Scopus
Orcid
Google Scholar

Summary

Shakila Rahman is a dedicated academician currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science and Technology, American International University-Bangladesh (AIUB). She holds a strong academic background in Artificial Intelligence and Computer Engineering, with her research focusing on emerging areas such as UAV networking, wireless sensor networks, optimization algorithms, and machine learning. Shakila is actively involved in mentoring students, guiding projects, and publishing impactful research in reputed platforms.

Educational Details

Shakila Rahman earned her M.Sc. in AI & Computer Engineering from the University of Ulsan, South Korea, in 2023 with an impressive CGPA of 4.00 out of 4.50. She completed her B.Sc. in Computer Science and Engineering from International Islamic University Chittagong (IIUC), Bangladesh, in 2019, securing a CGPA of 3.743 out of 4.00. Prior to her university education, she completed her Higher Secondary Certificate (HSC) from Cox’s Bazar Govt. College and Secondary School Certificate (SSC) from Cox’s Bazar Govt. Girls’ High School.

Professional Experience

Shakila is currently employed as a Lecturer in the Department of Computer Science and Engineering at AIUB, Dhaka, Bangladesh, where she has been working since January 2023. She previously served as a Graduate Research Assistant at the University of Ulsan, South Korea, from September 2020 to December 2022 under Professor Seokhoon Yoon. Additionally, she worked as an Undergraduate Teaching Assistant at IIUC in 2019. She has participated in technical boot camps and workshops and actively contributes to academic supervision, having guided several student projects and a machine learning-based thesis group.

Research Interests

Her research interests span a wide range of cutting-edge topics including UAV Networking, Wireless Sensor Networks, Network Systems, Optimization Algorithms, Machine Learning, Deep Learning, Image Processing, and AR/VR Applications in Artificial Intelligence. These multidisciplinary areas reflect her focus on building intelligent and adaptive systems for real-world applications.

Author Metrics

Shakila Rahman actively maintains a presence on prominent academic platforms. Her ResearchGate profile can be found at https://www.researchgate.net/profile/Shakila-Rahman-3, and her ORCID ID is 0000-0001-6375-4174. She is also available on LinkedIn at Shakila Rahman. Her published works and citation records are regularly updated on these platforms.

Awards and Honors

During her master's studies, Shakila was awarded the prestigious Brain Korea 21 (BK21) Scholarship and a fully funded AF1 scholarship at the University of Ulsan, valued at approximately USD 21,000. She also received funding from Korean Government-supported National Research Foundation (NRF) projects to support her graduate research publications. These accolades recognize her academic excellence and research contributions in the field of computer science and engineering.

Publication Top Noted

1. Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

Authors: N. Navin, F.A. Farid, R.Z. Rakin, S.S. Tanzim, M. Rahman, S. Rahman, J. Uddin, ...
Journal: Journal of Imaging, Vol. 11, Issue 5, Article 134
Year: 2025
Citation: 1 (as of now)
Summary:
This study introduces a YOLOv11-based deep learning model designed to recognize both Bangla and English sign language alphabets in real-time. The model was trained on a custom bilingual sign dataset and achieved high accuracy and low latency. The contribution is notable in promoting inclusivity for hearing-impaired communities in multilingual regions like Bangladesh.

2. Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

Authors: R.Z. Rakin, M. Rahman, K.F. Borsa, F.A. Farid, S. Rahman, J. Uddin, H.A. Karim
Journal: Future Internet, Vol. 17, Issue 5, Article 187
Year: 2025
Summary:
This paper proposes an AI model using YOLOv11 to identify infrastructure faults (e.g., road cracks, bridge damage) through image data. Designed with smart city integration in mind, the model is tested in urban environments and demonstrates high efficiency.

3. A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

Authors: S. Sharmin, F.A. Farid, M. Jihad, S. Rahman, J. Uddin, R.K. Rafi, R. Hossan, ...
Journal: Journal of Imaging, Vol. 11, Issue 4, Article 115
Year: 2025
Summary:
This paper presents DLI-Net, a hybrid CNN framework for classifying and explaining acne severity. It incorporates Explainable AI (XAI) techniques to enhance trust and transparency in medical AI systems.

4. A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Authors: S. Rahman, S. Akter, S. Yoon
Journal: Electronics, Vol. 14, Issue 1, Article 1
Year: 2024
Citation: 2 (as of now)
Summary:
This study explores a reinforcement learning-based approach using Deep Q-Learning for UAV navigation in constrained wireless sensor networks. The algorithm optimizes path planning in real-time, even in environments with signal interference or node failures.

5. A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

Authors: S. Salehin, S. Rahman, M. Nur, A. Asif, M. Bin Harun, J. Uddin
Journal: Iranian Journal of Electrical & Electronic Engineering, Vol. 20, Issue 4
Year: 2024
Citation: 2 (as of now)
Summary:
This paper proposes a YOLOv9-based model to detect abnormal human activity, particularly violent behavior, in real-time video surveillance. The system is trained on public datasets and achieves high detection accuracy.

Conclusion

Ms. Shakila Rahman is a promising and emerging researcher, with an impressive blend of academic excellence, funded research, and contributions to cutting-edge domains like machine learning and UAV networks. Her commitment to mentoring students and publishing research makes her a very strong candidate for the Best Researcher Award, particularly among early-career researchers or those in developing countries.

Gan Xu – Artificial Intelligence – Best Researcher Award

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Gan Xu – Artificial Intelligence – Best Researcher Award

Mr. Gan Xu distinguished academic and researcher in the field Artificial Intelligence.

🌐 Professional Profile

Educations📚📚📚

He is currently pursuing a Ph.D. in Finance at the Capital University of Economics and Business in Beijing, China, since September 2021. Prior to this, he completed his Master’s in Finance from Beijing Union University, Beijing, China, graduating in June 2021. His academic journey began with a Bachelor’s degree in Biotechnology, which he obtained from Guilin Medical University, Guilin, Guangxi, China, in June 2010.

Research Experience

He participated in the Project of the National Social Science Foundation of China, focusing on the “Research on Level Measurement, Spatial and Temporal Divergence, and Improvement Path of Rural Financial Services for Rural Revitalization” (19BJY158), where he was mainly responsible for the research design of some sub-topics and participated in enterprise research. Additionally, he contributed to the Key Topic of the China Mobile Communication Federation on the “Research on the Application of Blockchain Technology in Finance” (CMCA2018ZD01), taking charge of the research design of certain sub-topics and writing research reports. Furthermore, he was involved in the research project on “Financial Support for Deepening Financial Services for Private and Micro and Small Enterprises” as part of the Comprehensive Reform Pilot City Project in Jincheng City, Shanxi Province, where he was responsible for independently participating in application writing.

Social Experience

He has co-authored several significant publications, including “Financial Density of Village Banks and Income Growth of Rural Residents” with Yang, G.Z., Xu, G., Zhang, Y., and others, published in Economic Issues in 2021. Additionally, he contributed to “Knowledge Mapping Analysis of Seven Decades of Rural Finance Research in China” with Zhang, F., Xu, G., Zhang, X.Y., and Cheng, X., which appeared in Rural Finance Research in 2020. He also co-authored “A Review of Blockchain Applications in the Financial Sector” with Zhang, F. and Cheng, X., published in Technology for Development in 2019.

Honors

  • Received Beijing Outstanding Graduates in 2020
  • Outstanding graduate of Beijing Union University in 2020
  • First Prize of Excellent Paper in the First Annual Meeting of the Financial Technology Professional Committee of the China Society for Technology Economics, 2019
  • Second Prize of Excellent Paper in the 13th China Rural Finance Development Forum, 2019
  • Second Prize of Excellent Paper of the 9th Annual Conference of China Regional Finance and Xiongnu Financial Technology Forum, 2019

📝🔬Publications📝🔬

Pavithra sekar – Deep Learning – Best Researcher Award

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Pavithra sekar -Deep Learning – Best Researcher Award

Dr. Pavithra sekar distinguished academic and researcher in the field Deep Learning.

🌐 Professional Profile

Educations📚📚📚

She holds a Bachelor of Engineering (B.E.) in Computer Science Engineering, which she completed in April 2006 with a percentage of 75.6%, earning a first-class with distinction from Vel Tech Engineering College, affiliated with Anna University. She further advanced her education by obtaining a Master of Engineering (M.Tech) in Information Technology in June 2011, achieving a percentage of 8.43 and graduating with first-class honors from Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, also affiliated with Anna University. She completed her academic journey with a Ph.D. in Information Technology, awarded on January 24, 2020, from St. Peter’s Institute of Higher Education and Research.

PROFESSIONAL EXPERIENCE:

She possesses about 17 years of experience in the field of education, encompassing teaching, administration, and research. Currently, she holds the position of Assistant Professor Sr in the School of Computer Science and Engineering at Vellore Institute of Technology, Chennai. Her career includes roles such as Assistant Professor (Sr) in the Department of Computer Science and Engineering at VIT Chennai since December 15, 2023. Previously, she served as Assistant Professor (SG) and IPR coordinator in the Department of Information Technology from March 31, 2021, to December 7, 2022, at Rajalakshmi Engineering College. Prior to that, she was Assistant Professor & Assistant HOD in the Computer Science & Engineering Department at VelTech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College for 9.6 years, from July 6, 2011, to January 25, 2021. She began her career as a lecturer in the Department of Information Technology at VelTech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College from August 24, 2006, to September 25, 2009.

 

DEVELOPMENT ACTIVITIES

She possesses a thorough understanding of her subject area, demonstrating an exceptional ability to effectively communicate complex concepts to her students. Her strong communication and comprehension skills enhance her role in internal administrative tasks within educational institutions. She has a proven track record in coordinating various activities such as symposiums, student projects, and conferences, where she provides guidance and support to ensure successful outcomes. Her extensive experience includes roles as Class Coordinator, Conference Coordinator, Project Coordinator for contests, and organizing events like the MOTOROLA FAER EVENT. Additionally, she has served as ISO coordinator, handled NBA coordination, and participated actively in autonomous file activities. She is involved in setting question papers for autonomous colleges and universities and has excelled in roles such as Class In-Charge, Student Mentor, and AICTE CII-Survey participant. She diligently maintains semester-wise result analysis reports, prepares weekly schedules, and curates course materials for effective teaching. Her commitment to excellence is evident in achieving over 90% results across all subjects and receiving a publication award of 16,000. Notably, she achieved a perfect 100% result in key subjects like Computer Programming, Operating System, Computer Architecture, Advanced Computer Architecture, and Problem Solving And Python Programming.

📝🔬Publications📝🔬

1. S. Pavithra and K. Venkata Vikas, “Detecting Unbalanced Network Traffic Intrusions With Deep
Learning,” in IEEE Access, vol. 12, pp. 74096-74107, 2024, doi: 10.1109/ACCESS.2024.3405187.
2. . Pavithra, T. Veeramani, S. Sree Subha, J.P. Sathish Kumar, S. Shanmugan, Ammar H.
Elsheikh, F.A. Essa, “Revealing prediction of Perched Cum Off-Centered Wick Solar Still
Performance using network based on Optimizer algorithm” Process Safety and
Environmental Protection,Volume 161,2022, Pages 188-200,ISSN 0957-
5820,https://doi.org/10.1016/j.psep.2022.03.0092022, .(SCI, Scopus).)(Impact factor: 7.92).
3. Meena, M., Kavitha, A., Karthick, Pavithra.S “Effect of decorated photoanode of
https://doi.org/10.1007/s12034-022-02828-9 .(SCI, Scopus).)(Impact factor: 1.92).
4. S.Pavithra, P.M Anu “An Efficient Data Aggregation with Optimal Recharging in Wireless
Rechargeable Sensor Networks” (Submission code: IJAIP-221302) for the International
Journal of Advanced Intelligence Paradigms (IJAIP) Inderscience
DOI: 10.1504/IJAIP.2022.10040244,2022 (Scopus). Impact factor ( 0.63)
5. S.Pavithra Assistive Chatbot device to support Visually Impaired Person to access
Transport Mode Status Using Deep Learning Model ARPN Journal of Engineering and
Applied Sciences waiting for Publication 2022. (Scopus).
6. S.Pavithra, R.Karthikeyan P.M Anu “Detection and classification of 2D and 3D Hyper
Spectral Image Using Enhanced Harris Corner Detector” “Scalable Computing: Practice and
Experience, ISSN 1895-1767, Volume 21, Issue 1, pp. 93–100, DOI