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The fourth wave: ultrawide bandgap compound semiconductors for photonics and electronics

Xiaohang Li, Assistant Professor, Electrical and Computer Engineering

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KAUST

Wide bandgap (WBG) compound semiconductors including GaN have shown enormous success in solid-state lighting, display, and electrification in recent decades due to superior properties such as direct bandgap, high electron mobility, and large breakdown field. They have been changing the world by elevating living standards and addressing grand challenges such as global warming. The pioneering researchers have been recognized by numerous accolades including the Nobel Prize and most recently, the Queen Elizabeth Prize. Lately, the III-nitride and III-oxide ultrawide bandgap (UWBG) compound semiconductors with bandgap larger than 3.4 eV have attracted increasing attentions: they have been regarded as the 4th wave/generation after the consequential Si, III-V, and WBG semiconductors. Because the UWBG along with other properties could enable electronics and photonics to operate with significantly greater power and frequency capability and at much shorter far−deep UV wavelengths, respectively, both crucial for human society. Besides, they could be employed for the revolutionary quantum information science as the host and photonic platform. However, extensive multi-disciplinary studies of growth, materials, physics, and devices are essential to unearth the potentials due to the infancy. This seminar would cover the latest research on those aspects. It includes growth of state-of-the-art materials, discovery of unique material properties, and development of a widely adopted device physics framework for photonics and electronics especially short and long wavelength photonic devices.
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Research at the Image and Video Understanding Lab (IVUL) - Graduate Seminar - ECE

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KAUST

In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding): We aim to extract meaningful semantic information from large-scale video data by tackling research problems such as object tracking, activity detection, moment retrieval, and language grounding in video. Theme 2 (Visual Computing for Automated Navigation): We develop methodology to enable more accurate, reliable, and robust perception of the visual world for automated navigation applications (e.g. self-driving cars and UAVs). In this theme, we tackle research problems such as object tracking, segmentation, and detection in 3D data, as well as transfer learning from simulation (sim2real). Theme 3 (Fundamentals/Foundations): In this theme, we tackle fundamental research problems in CV and ML that transcend specific applications with focus on deep network theory/analysis (e.g. robustness, certification, and interpretability) and structured optimization methods for large-scale CV/ML problems. Throughout the talk, I will highlight some of the interesting projects at IVUL to encourage students to get interested in the research field.
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