Jiabin Shen | Neuromorphic Computing | Best Researcher Award

Assist. Prof. Dr. Jiabin Shen | Neuromorphic Computing | Best Researcher Award

Assist. Prof. Dr. Jiabin Shen, Associate Professor, Fudan University, China.

Dr. Jiabin Shen is an innovative Associate Professor at Fudan University, specializing in photonic computing and emerging memory technologies. With a strong academic foundation and research productivity, he has made remarkable contributions to neuromorphic hardware design. Dr. Shen earned his Ph.D. from the University of Chinese Academy of Sciences and has since excelled through postdoctoral research and faculty roles. His work bridges theoretical and practical aspects of optoelectronic systems, as evidenced by over 20 publications in high-impact journals, 12 patents, and national recognition. A trailblazer in his domain, Dr. Shen continues pushing the boundaries of post-Moore’s Law hardware innovations.

Publication Profile

Scopus

ORCID

🎓 Education Background

Dr. Jiabin Shen received his Ph.D. in Microelectronics and Solid-State Electronics from the prestigious University of Chinese Academy of Sciences (UCAS) in 2021. During his doctoral studies, he focused on the convergence of photonic systems and advanced computing models. His academic journey was marked by rigorous theoretical training and impactful research outcomes. After completing his doctorate, he joined Fudan University as a postdoctoral researcher, further honing his expertise in optoelectronics. In 2024, he was appointed as an Associate Professor at Fudan University, solidifying his position as a rising star in the fields of photonics and neuromorphic engineering.

🧑‍🏫 Professional Experience

Dr. Shen began his professional academic career with a postdoctoral research fellowship at Fudan University, where he worked on cutting-edge optoelectronic systems from 2021 to 2023. His exceptional contributions led to his appointment as an Associate Professor in 2024 at the same institution. Throughout his tenure, Dr. Shen has managed six high-level research projects and published 22 SCI/Scopus-indexed papers. Although he has not yet been involved in consultancy or industry collaborations, his work remains highly impactful in academic circles. He also holds 12 patents, underscoring his commitment to practical, innovative solutions in computing hardware.

🏅 Awards and Honors

Dr. Jiabin Shen has been selected for China’s prestigious National Young Talents Program, an honor bestowed on promising researchers making significant scientific contributions. His work in photonic computing has been featured among China’s Top 10 Scientific Advances, reflecting national recognition for his research excellence. Dr. Shen’s academic rigor and innovation are also reflected in his multiple patents and high citation count. With over 550 citations to date, his scientific outputs are highly respected in the computing and electronics research community. These accolades collectively affirm his standing as one of China’s most promising young scientists in computing.

🔬 Research Focus

Dr. Shen’s research is centered on photonic neuromorphic computing and emerging memory systems. He has made groundbreaking strides by designing an optoelectronic emulator capable of executing 6-bit precision optical-domain multiplication using FPGA platforms. This system effectively bridges the gap between theoretical designs and real-world hardware, allowing for accurate simulation of image convolution and inference learning. By accelerating the prefabrication verification process, his research enhances the development of photonic computing systems. His work is vital for future computing paradigms that surpass Moore’s Law constraints, setting the stage for the development of advanced, energy-efficient, and high-speed computational architectures.

🔚 Conclusion

Dr. Jiabin Shen stands at the forefront of next-generation computing technologies. As an Associate Professor at Fudan University, his integrated approach to research, combining photonics, neuromorphic systems, and hardware development, has positioned him as a key contributor to global scientific advancements. His academic output, comprising high-impact journal publications and patented innovations, underscores his role as a thought leader in post-silicon computation. Selected for elite national programs and featured among China’s top scientific breakthroughs, Dr. Shen’s career trajectory exemplifies excellence in research and innovation. His journey continues to inspire and redefine possibilities in computational science.

📚 Top Publications by Dr. Jiabin Shen

  1. All-optical arithmetic processing using phase-change photonic circuits
    Published in: Science, 2021
    Cited by: 95 articles
    Summary: Demonstrates integrated optical computing operations using phase-change materials.

  2. In-memory photonic computing with non-volatile materials
    Published in: Nature, 2021
    Cited by: 122 articles
    Summary: Introduces novel photonic memory units capable of accelerating neuromorphic workloads.

  3. FPGA-integrated emulator for photonic neuromorphic circuits
    Published in: IEEE Transactions on Neural Networks and Learning Systems, 2022
    Cited by: 66 articles
    Summary: Presents a simulation-emulation framework bridging software models and physical hardware.

  4. High-speed optoelectronic computing based on memristive switching
    Published in: Materials Today, 2021
    Cited by: 89 articles
    Summary: Explores memristive-based photonic systems for fast, reliable AI inference.

  5. Emulating synaptic plasticity using nanophotonic circuits
    Published in: Optica, 2022
    Cited by: 48 articles
    Summary: Achieves tunable photonic synaptic weights using silicon photonics for neuromorphic learning.

 

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail | Artificial Intelligence | Best Researcher Award

Prof. Dr. Mohamed Maher Ben Ismail, King Saud University, Saudi Arabia

Dr. Mohamed Maher Ben Ismail is a distinguished full professor in the Computer Science Department at the College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia . With a prolific academic and research background spanning over two decades, Dr. Ben Ismail is recognized for his contributions in artificial intelligence, image processing, and data mining. His work bridges theory and practical applications in machine learning and statistical modeling, making him a leading voice in his field 🌐📚.

Professional Profile

Google Scholar

Scopus

🎓 Education Background

Dr. Ben Ismail holds a Ph.D. in Computer Engineering and Computer Science from the University of Louisville, USA (2011) 🇺🇸, where his dissertation focused on image annotation and retrieval using multi-modal feature clustering. He also earned a Master’s in Automatic and Signal Processing and a Bachelor’s in Electrical Engineering from the National School of Engineering of Tunis, Tunisia 🇹🇳. His early academic journey was distinguished by excellence in mathematics, physics, and competitive engineering entrance exams 🧠📘.

🧑‍🏫 Professional Experience

Dr. Ben Ismail currently serves as a Full Professor at King Saud University (2021–present), following roles as Associate Professor (2017–2021) and Assistant Professor (2011–2017). Previously, he worked as a Design & Development Engineer at STMicroelectronics, Tunisia, and as a Graduate Research Assistant at the University of Louisville’s Multimedia Research Lab, where he pioneered work on CBIR systems and integrated machine learning approaches. His academic role includes supervising thesis work, lecturing across AI, ML, algorithm design, and image processing 💼👨‍🏫.

🏆 Awards and Honors

Throughout his career, Dr. Ben Ismail has received numerous accolades, including the Best Faculty Member Award (2017) at King Saud University, the Graduate Dean’s Citation Award (2011), and the IEEE Outstanding CECS Student Award (2011) 🥇. He is also a member of the Golden Key International Honor Society and received early recognition through his promotion at STMicroelectronics and various graduate assistantships and scholarships 🎖️.

🔬 Research Focus

Dr. Ben Ismail’s research interests lie in Artificial Intelligence, Machine Learning, Pattern Recognition, Image Processing, Temporal Data Mining, and Information Fusion 🤖🧠. His work emphasizes robust statistical modeling and intelligent systems design, often applied to domains like IoT security, brain tumor detection, real estate prediction, and hyperspectral imaging. His prolific publication record in top-tier journals and conferences highlights his continuous contributions to advanced computational techniques and interdisciplinary innovation 📊📈.

📌 Conclusion

With a solid educational foundation, impactful research contributions, and extensive teaching experience, Dr. Mohamed Maher Ben Ismail stands as a key figure in advancing AI-driven solutions in academia and industry. His dedication to excellence and innovation marks him as a thought leader and an inspirational academic voice in the global computer science community 🌟🧑‍🔬.

📚 Top Publications Notes

  1. YOLO-Act: Unified Spatiotemporal Detection of Human Actions Across Multi-Frame Sequences
    📅 Published in: Sensors, 2025
    🔍 Cited by: 12 articles (as of mid-2025)
    🧠 Highlights: Proposes a YOLO-based system for recognizing actions across video frames.

  2. MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 9 articles
    🧠 Highlights: Enhances brain tumor classification using deep adversarial networks.

  3. RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic
    📅 Published in: Sensors, 2024
    🔍 Cited by: 18 articles
    🔐 Highlights: Focuses on adversarial ML methods to enhance IoT network security.

  4. Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
    📅 Published in: Cancers, 2024
    🔍 Cited by: 25 articles
    🧬 Highlights: Applies CNNs to early skin cancer detection using medical images.

  5. A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five YOLO Versions
    📅 Published in: Computation, 2024
    🔍 Cited by: 14 articles
    💡 Highlights: Compares YOLOv3 to YOLOv7 models for brain scan interpretation.

  6. Toward an Improved Machine Learning-based Intrusion Detection for IoT Traffic
    📅 Published in: Computers, 2023
    🔍 Cited by: 20 articles
    🔒 Highlights: Develops a secure ML framework to prevent intrusions in smart devices.

  7. Simultaneous Deep Learning-based Classification and Regression for Company Bankruptcy Prediction
    📅 Published in: Journal of Business & Economic Management, 2023
    🔍 Cited by: 8 articles
    💼 Highlights: Innovative DL model integrating financial classification with regression.

  8. Novel Dual-Constraints Based Semi-Supervised Deep Clustering Approach
    📅 Published in: Sensors, 2025
    🔍 Cited by: 6 articles
    📊 Highlights: Enhances clustering accuracy using semi-supervised constraints in DL.

  9. Better Safe than Never: A Survey on Adversarial Machine Learning Applications towards IoT Environment
    📅 Published in: Applied Sciences, 2023
    🔍 Cited by: 22 articles
    🔍 Highlights: Comprehensive survey exploring adversarial ML attacks and defense for IoT.

  10. Detecting Insults on Social Network Platforms Using a Deep Learning Transformer-Based Model
    📅 Published in: IGI Global Book Chapter, 2025
    🔍 Cited by: 11 articles
    🌐 Highlights: Uses transformer models to detect hate speech and insults online.