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Yonsei University.

Assistant Professor
Yoon Ho Jang
Education
  • 2023

    Ph.D. , Materials Science and Engineering, Seoul National University

  • 2018

    B.S., Materials Science and Engineering, Seoul National University

Career
  • 2026 - Present

    Assistant Professor, Yonsei Unviersity

  • 2025 - 2026

    Postdoctoral Fellow, Harvard University

  • 2023 - 2026

    Postdoctoral Fellow, Inter-University Semiconductor Research Center, Seoul National Universtiy

Research Interests
At the Advanced Semiconductor for Emerging Technologies Lab, we bridge the gap between fundamental materials physics and system-level hardware to redefine the future of computing. We focus on utilizing the intrinsic physical properties of emerging memory devices to overcome the bottlenecks of modern AI and data-centric workloads. By shifting the paradigm toward physics-driven intelligence, we aim to implement highly efficient hardware solutions. Our multidisciplinary research spans from the design of novel device structures to the development of hardware platforms for next-generation computing and emerging technologies.

Current Research Interests include:
- Physics-Driven AI Hardware: Hardware-based solutions for parameter reduction and high-efficiency computing to address AI scaling challenges.
- Physical Graph Computing: Next-generation architectures and devices that physically represent graph structures for autonomous network analysis.
- Next-generation Computing Solutions: Innovative hardware platforms for emerging technologies, including bio-hybrid systems and scalable, high-performance computing architectures.
Selected Publications
1. Nature Communications, Time-varying data processing with nonvolatile memristor-based temporal kernel, 2021
2. Advanced Materials, Graph analysis with multi-functional self-rectifying memristor array, 2023
3. Advanced Materials, Spatiotemporal Data Processing with Memristor Crossbar-Array-Based Graph Reservoir, 2023
4. Advanced Materials, Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis, 2024
5. Advanced Materials, Memristive Crossbar Array-based Probabilistic Graph Modeling, 2024
6. Advanced Materials, Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis, 2024
7. Advanced Materials, Multi-dimenstional Graph Analysis Using Vertical Memristive Crossbar Array, 2025
8. Nature Communications, Next-Generation Graph Computing with Electric Current-based and Quantum-inspired Approaches, 2025
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