Research Areas

  • 1. Data science and applied machine learning in graphs, networks, systems
  • 2. Causal inference, explainable AI, and neurosymloic AI
  • 3. Reinforcement learning for system optimization
  • 4. Generative AI for text, software, and hardware applications
  • 5. Audio recognition and language understanding

    • Jin’s research spans a broad spectrum of AI-driven innovations, particularly focusing on practical industrial applications to maximize business value from technological advancements. His earlier work addressed practical challenges such as data fusion for space management, acoustic event detection in smart buildings and cities, and generative lighting virtualization via explainable AI techniques.

    His current focus is in 1) enhancing the efficiency of semiconductor testing and manufacturing process and 2) optimizing hardware and software designs in semiconductor manufacturing. By employing statistical analysis, graph-based machine learning, and explainable AI, he is refining testing procedures, improving yield rates, and shortening the time-to-market for semiconductor products. His current research also extends to the use of reinforcement learning and graph theory for hardware/software design optimization, and generative mutltimoal AI for text, software, and hardware Applications.

    Within nearly two decades of industry experience in leadership roles at Teradyne, Philips/Signify Research, Jin blends deep technical expertise with strategic business insights. This comprehensive perspective drives his commitment to translating cutting-edge AI research into practical, scalable, and high-impact solutions across various industrial and urban domains.