Assoc. Prof. Dr. Jiahao Liu | Neuromorphic Devices Awards | Best Researcher Award

Assoc. Prof. Dr. Jiahao Liu | Neuromorphic Devices Awards | Best Researcher Award

Assoc. Prof. Dr. Jiahao Liu, National University of Defense Technology, China

Liu Jiahao is an Associate Researcher in the Department of Nanoscience at the National University of Defense Technology, where he specializes in nano-optoelectronic intelligent information devices. He earned his Ph.D. in Electronic Science and Technology from the College of Computer Science at the same university, under the supervision of Researcher Fang Liang. His academic journey also included joint training at Tsinghua University in Physics. Liu has actively contributed to the scientific community as an expert reviewer for the National Natural Science Foundation of China and as a reviewer for prestigious international publishers such as IEEE and IET. Currently, he leads multiple research projects funded by national science foundations, focusing on the dynamics of skyrmions and their applications in neuromorphic computing, alongside other innovative studies in nanotechnology. Born in Xiangyang, Hubei, and a dedicated Party member, Liu is committed to advancing research in the field of nanoscience and its applications in modern technology.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award: Liu Jiahao

Liu Jiahao is an accomplished researcher in the field of electronic science and technology, with a strong focus on nano-optoelectronic intelligent information devices. His academic journey, impressive publication record, and active involvement in cutting-edge research projects highlight his qualifications for the Best Researcher Award.

Education

Ph.D. in Electronic Science and Technology
National University of Defense Technology
February 2020 – December 2022

  • Advisor: Researcher Fang Liang
  • Joint Training:
    • Department of Physics, Tsinghua University
    • November 2020 – September 2022
    • Advisor: Associate Professor Jiang Wanjun
    • Specialization: Physics

Work Experience

Lecturer
Department of Nanoscience, College of Frontier Interdisciplinary Science
National University of Defense Technology
December 2022 – December 2023

Associate Researcher
Department of Nanoscience, College of Frontier Interdisciplinary Science
National University of Defense Technology
December 2023 – Present

Publication top Notes:

Linear Weight Update Synaptic Responses in Ferrimagnetic Neuromorphic Devices

Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition

Skyrmionium creation and annihilation: Experimental and micromagnetic simulation demonstration

Electrical detection of mobile skyrmions with 100% tunneling magnetoresistance in a racetrack-like device

On-chip skyrmion synapse regulated by Oersted field

Skyrmions in nanorings: A versatile platform for skyrmionics

 

 

Prof. Giacomo Indiveri | Neuromorphic Engineering | Best Researcher Award

Prof. Giacomo Indiveri | Neuromorphic Engineering | Best Researcher Award

Prof. Giacomo Indiveri, University of Zurich and ETH Zurich,Switzerland

Giacomo Indiveri is a distinguished professor and group leader, currently holding positions at the Universität Zürich and the Neuroscience Center Zurich (ZNZ). With an academic career spanning nearly two decades, Indiveri has made significant contributions to the field of neuromorphic engineering, seamlessly integrating concepts from neuroscience, computer science, and electrical engineering.Indiveri’s academic journey began with a Master’s degree in Electrical Engineering from the University of Genoa, Italy, followed by further advanced studies in bioelectronics at the Scientific and Technological Park of the Island of Elba. He earned his Ph.D. in Electronic and Computer Engineering from the University of Genoa under the guidance of Riccardo Zoppoli. His postdoctoral work included collaborations with renowned scientists Rodney Douglas at the Universität Zürich and Christof Koch at the California Institute of Technology.

Professional Profile

Education 🎓

  • Habilitation in Neuromorphic Engineering
    ETH Zürich – ETHZ, CH, D-ITET
    09.2004 – 08.2006 (2 years)
    🏫
  • PhD / Dr.: Dottorato di Ricerca in Ingegneria Elettronica e Informatica
    University of Genoa, IT, Department of Biophysical and Electronic Engineering
    01.2001 – 05.2004 (3 years, 5 months)
    🎓
  • Further Advanced Studies: Industrial Doctoral Award (32/32 cum laude)
    PST-Elba, Scientific and Technological Park of the Island of Elba, Marciana (LI), IT
    National Research and Training Program on Technologies for Bioelectronics
    09.1992 – 03.1995 (2 years, 7 months)
    🌟
  • Master: Laurea in Electrical Engineering
    University of Genoa, IT, Department of Biophysical and Electronic Engineering
    09.1986 – 06.1992 (5 years, 10 months)
    🎓

Employment History 💼

  • Full Professor
    Universität Zürich – ZH, CH, Institute of Neuroinformatics
    01.2024 – Present (2 months)
    👨‍🏫
  • Group Leader
    ETH Zürich – ETHZ, CH, Neuroscience Center Zurich (ZNZ)
    03.2009 – Present (15 years)
    🧑‍🔬
  • Head of Institute
    Universität Zürich – ZH, CH, Institute of Neuroinformatics
    01.2018 – 12.2025 (8 years)
    🏢

Major Achievements 🏆

Achievement 1: Neuromorphic Processors and Agents

  • Developed a comprehensive framework of computational neuroscience toolsets, electronic circuits, and mixed-signal spiking neural network chips.
  • These systems emulate cortical circuits’ biophysics, used for basic research, education, and real-world applications.

Publications Notes:📄

Online Epileptic Seizure Detection in Long-term iEEG Recordings Using Mixed-signal Neuromorphic Circuits

Encoding seizures with partial synchronization: A spiking neural network for biosignal monitoring on a mixed signal neuromorphic processor

A physical emulation of somatosensory cortex as a Neuromorphic Twin for neural prostheses

A physical emulation of somatosensory cortex as a Neuromorphic Twin for neural prostheses

Author Correction: DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays