Dr. Daniel L. Lau
Machine learning and AI for data with complex relational structure — graph and hypergraph neural networks, geometric deep learning, and AI for 3D vision and computational imaging.
Machine learning & AI researcher
Daniel L. Lau is the Databeam Professor of Electrical and Computer Engineering at the University of Kentucky and an IEEE Fellow. His research develops machine learning and artificial intelligence methods for data with complex relational structure, centered on graph and hypergraph neural networks and geometric deep learning.
Recent work includes tensor-based hypergraph neural networks (T-HyperGNNs, HyperNATE), hypergraph U-Net architectures, and graph-learning methods for inference on physical systems such as power-distribution networks and neural connectomes. He also works in computer vision and computational 3D imaging. His research has been supported by NSF, AFOSR, DOE, and industry partners. Explore the research →
Fellow of the IEEE
Companies founded
Dr. Lau was a founder of FlashScan3D, a company that specialized in non-contact fingerprint scanning using structured light, and Seikowave, which developed 3D scanners for non-destructive evaluation of corrosion in pipelines.
Patents & expertise
With over 40 patents in imaging and related technologies, Dr. Lau has made significant contributions to the fields of machine vision and 3D imaging. He also provides subject-matter expertise in patent law, offering services such as patent claim evaluation, claim-chart preparation, and expert testimony.
Professional leadership
An active leader in the machine-vision community, Dr. Lau serves on the Association for Advancing Automation (A3) Technical Board for Imaging and Sensing and has presented webinars on 3D imaging for Vision Systems Design for over a decade.
Color printing & halftoning
His research into accurate color printing — green-noise / stochastic halftoning, dot-placement models, and overprint screening — is the foundation of the FM Halftone Screening Photoshop plugin, which brings production-grade screening to anyone preparing press-ready separations.
Decades of experience. "I bring decades of experience in bridging academic and industry research and development, leveraging machine learning, graph & hypergraph neural networks, and AI for imaging to solve complex challenges and drive technological innovation."