Angelos Athanasiadis | Neural Networks | Research Excellence Award

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Mr. Angelos Athanasiadis | Neural Networks | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Angelos Athanasiadis is a Ph.D. candidate in Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH), specializing in FPGA-based acceleration of Convolutional Neural Networks. With expertise spanning embedded system development, heterogeneous computing, and cyber-physical systems, he has contributed to both academic and industrial innovation through participation in EU research initiatives—including the ADVISER and REDESIGN projects—and through consultancy and R&D roles at EXAPSYS and SEEMS PC. His work focuses on advancing energy-efficient hardware acceleration, leading to the development of a parameterizable HLS matrix multiplication library for AMD FPGAs that enables full-precision CNN inference for accuracy-critical domains such as aerial monitoring and autonomous embedded systems. He further expanded the field with FUSION, an open-source high-fidelity distributed emulation framework integrating QEMU with OMNeT++ via HLA/CERTI synchronization to support deterministic, timing-aware multi-node execution and realistic prototyping of heterogeneous systems. Complementing his strong technical background, he holds an MBA awarded with high distinction and an M.Eng. in electronics and computer systems, supported by internships at Cadence Design Systems in Munich.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Pose Analysis in Free-Swimming Adult Zebrafish, Danio rerio: "fishy" Origins of Movement Design", Jagmeet S. Kanwal; Bhavjeet Sanghera; Riya Dabbi; Eric Glasgow, Preprint, 2025.

"Complex Sound Discrimination in Zebrafish: Auditory Learning Within a Novel “Go/Go” Decision-Making Paradigm", Anna Patel; Sai Mattapalli; Jagmeet S. Kanwal, Animals, 2025.

"From Information to Knowledge: A Role for Knowledge Networks in Decision Making and Action Selection", Jagmeet S. Kanwal, Information, 2024.

"NemoTrainer: Automated Conditioning for Stimulus-Directed Navigation and Decision Making in Free-Swimming Zebrafish", Bishen J. Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Animals, 2022.

"NemoTrainer: Apparatus and Software for Automated Conditioning of Stimulus-directed Navigation and Decision Making in Freely Behaving Animals", Bishen Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Preprint, 2022.

Jieru Song | Neuromorphic Computing | Best Researcher Award

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Ms. Jieru Song | Neuromorphic Computing | Best Researcher Award

Jieru Song at Fudan University, China📖

Jieru Song is a Ph.D. candidate at Fudan University’s School of Microelectronics, specializing in neuromorphic computing and optoelectronic devices. With a Bachelor’s degree in Microelectronics Science and Engineering from Nanjing University, he has contributed to advancing self-powered heterojunction devices and optoelectronic memristors for reservoir computing. His work focuses on the integration of sensing, storage, and computing, with a particular emphasis on energy-efficient solutions for neuromorphic applications. Jieru’s research has resulted in significant innovations in signal processing, energy-efficient computing, and artificial neural networks.]

Profile

Scopus Profile

Education Background🎓

  1. Bachelor’s Degree in Microelectronics Science and Engineering, Nanjing University
  2. Ph.D. (Ongoing), School of Microelectronics, Fudan University (Research in optoelectronic memristors and neuromorphic computing)

Professional Experience🌱

Jieru Song is currently pursuing his Ph.D. at Fudan University, focusing on the development of self-powered heterojunction devices and optoelectronic memristors for neuromorphic applications. His work integrates signal processing with energy-efficient computing solutions for reservoir computing. He has designed and fabricated devices for tasks such as speech and EMG signal classification, enhancing device performance through optimized fabrication techniques. His professional journey is characterized by contributions to innovative algorithms for signal conversion and classification, which have led to advancements in both hardware and software for computing systems.

Research Interests🔬

Her research interests include:

  • Neuromorphic Computing
  • Optoelectronic Memristors
  • Self-Powered Devices
  • Reservoir Computing
  • Energy-Efficient Computing Solutions
  • Signal Processing Algorithms

Author Metrics

Jieru Song has contributed significantly to the field of neuromorphic computing and optoelectronic devices, with his research published in several esteemed journals. Notably, his work on self-powered optoelectronic synaptic devices for both static and dynamic reservoir computing was published in Nano Energy in 2025, where it received widespread attention. Additionally, he has authored papers on photoelectric synaptic devices for neuromorphic computing, featured in IEEE Electron Device Letters (2024) and Journal of Semiconductors (2024). His publications reflect a strong focus on innovative devices for energy-efficient, integrated sensing, and computing systems, contributing valuable insights to the advancement of neuromorphic applications.

Awards and Honors

Jieru Song has been recognized for his groundbreaking contributions to neuromorphic computing and optoelectronic devices, including publication in leading journals and conferences. His research has garnered attention in the scientific community for advancing energy-efficient, integrated sensing, and computing systems, laying the foundation for future scalable technological solutions.

Publications Top Notes 📄

1. Self-powered optoelectronic synaptic device for both static and dynamic reservoir computing

  • Authors: Jieru Song, J. Meng, C. Lu, D.W. Zhang, L. Chen
  • Journal: Nano Energy
  • Year: 2025
  • Volume: 134
  • Article Number: 110574
  • DOI: [Link Disabled]
  • Abstract: This paper presents the development of a self-powered optoelectronic synaptic device that can efficiently operate for both static and dynamic reservoir computing tasks. The device has applications in neuromorphic systems and energy-efficient computation.

2. Reconfigurable Selector-Free All-Optical Controlled Neuromorphic Memristor for In-Memory Sensing and Reservoir Computing

  • Authors: C. Lu, J. Meng, Jieru Song, D.W. Zhang, L. Chen
  • Journal: ACS Nano
  • Year: 2024
  • Volume: 18
  • Issue: 43
  • Pages: 29715–29723
  • Citations: 1
  • DOI: [Link Disabled]
  • Abstract: This study introduces a reconfigurable, selector-free all-optical controlled neuromorphic memristor designed for in-memory sensing and reservoir computing. This novel device has significant implications for neuromorphic computing systems.

3. InGaZnO-based photoelectric synaptic devices for neuromorphic computing

  • Authors: Jieru Song, J. Meng, T. Wang, D.W. Zhang, L. Chen
  • Journal: Journal of Semiconductors
  • Year: 2024
  • Volume: 45
  • Issue: 9
  • Article Number: 092402
  • Citations: 1
  • DOI: [Link Disabled]
  • Abstract: This article explores InGaZnO-based photoelectric synaptic devices, focusing on their application in neuromorphic computing. The devices are designed to enhance performance for computational tasks like image recognition and signal classification.

4. Fluorite-structured antiferroelectric hafnium-zirconium oxide for emerging nonvolatile memory and neuromorphic-computing applications

  • Authors: K. Xu, T. Wang, J. Yu, D.W. Zhang, L. Chen
  • Journal: Applied Physics Reviews
  • Year: 2024
  • Volume: 11
  • Issue: 2
  • Article Number: 021303
  • Citations: 3
  • Abstract: The paper investigates the use of fluorite-structured antiferroelectric hafnium-zirconium oxide in nonvolatile memory and neuromorphic computing applications. The material’s properties are optimized for energy efficiency in memory storage and computing systems.

5. Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

  • Authors: J. Meng, Jieru Song, Y. Fang, D.W. Zhang, L. Chen
  • Journal: ACS Nano
  • Year: 2024
  • Volume: 18
  • Issue: 12
  • Pages: 9150–9159
  • Citations: 7
  • Abstract: This research introduces ionic diffusive nanomemristors that exhibit dendritic competition and cooperation functions, designed for ultralow voltage neuromorphic computing. These memristors are key for advancing neuromorphic computing systems with minimal energy consumption.

Conclusion

Jieru Song has exhibited a strong and consistent track record of groundbreaking research in the emerging field of neuromorphic computing. His innovative work in self-powered optoelectronic devices, memristors, and energy-efficient computing solutions positions him as a leader in the field. His ability to combine technical ingenuity with practical applications has already begun to influence both the academic and technological communities.

Given the impressive impact of his research and its potential for long-term contribution to AI, signal processing, and sustainable computing, Jieru Song is highly deserving of the Best Researcher Award. With further development in industry collaborations and expanded research in cross-disciplinary applications, he can continue to push the boundaries of neuromorphic computing and its practical applications.