Jin's research spans a wide range of areas, including data fusion, audio recognition, language understanding, and explainable AI . His past work has tackled practical challenges such as data fusion for space management, acoustic event detection for smart buildings, and lighting virtualization via explainable AI techniques. His research is focused on industrial applications, aiming to derive tangible business value from technological innovations.
Jin also brings nearly two decades of industry experience in data analytics and AI, currently serving as the head of machine learning at Teradyne, a leader in semiconductor manufacturing automation. His previous roles include director of machine learning at Signify (formerly Philips Lighting), system architect at Acuity Brands, and principal DSP engineer at Xperi Inc. Recently, Jin's research has concentrated on applying AI to semiconductor testing, where he is developing AI-driven methodologies to optimize testing processes, enhance operational efficiency, power efficiency, and accelerate time-to-market for semiconductor products, delivering significant value to customers and stakeholders.