Daniel L. Lau, Ph.D., professor in the Department of Electrical and Computer Engineering at the UK Stanley and Karen Pigman College of Engineering, has been named a 2026 Institute of Electrical and Electronics Engineers (IEEE) Fellow, for his contributions to digital printing and 3D imaging.
The grade of Fellow, one of the highest honors bestowed by IEEE, recognizes individuals with an extraordinary record of accomplishments that have advanced engineering, science and technology, and delivered significant value to society. Fewer than one-tenth of one percent of IEEE voting members are elevated to Fellow each year.
Lau is internationally recognized as a pioneer in digital halftoning, the process used by printers and displays to convert continuous-tone images into binary or multi-level dot patterns. His primary contribution to this area was green-noise halftoning, an advanced technique that creates continuous-tone images using clusters of minority pixels (mid-frequency noise) instead of isolated dots (blue noise). Green-noise halftoning enables printers and displays to achieve their smallest resolvable dots and highest effective resolution while maintaining stability and visual quality.
Lau’s seminal first-authored paper introducing green-noise halftoning appeared in the Proceedings of the IEEE and has become a foundational reference in the field. His contributions include widely cited journal publications, influential books—most notably Modern Digital Halftoning—and numerous U.S. patents that have shaped both academic research and industrial practice.
In addition to digital printing, Lau has made transformative contributions to structured-light 3D imaging, developing high-speed algorithms and systems capable of real-time, high-accuracy shape measurement. His work laid critical foundations for non-contact 3D fingerprint capture, demonstrating for the first-time performance comparable to traditional contact-based methods. This research led to the formation of FlashScan3D and Seikowave, whose technology has played a significant role in national and international standards for contactless fingerprint evaluation adopted by National Institute of Standards and Technology and the National Institutes of Justice. Today, contactless 3D fingerprint scanners based on structured light are entering widespread use in forensics, security and biometric identification.
Lau’s work has developed robust decision-making and reconstruction rules in complex sensing systems. Through studying stereo vision systems that use multiple cameras and projectors, he identified methods to more effectively combine available data to improve the accuracy and robustness of 3D reconstruction. In addition, he has led and co-led interdisciplinary projects applying these techniques to real-world problems, including surveillance and face recognition with structured light and machine vision for biomedical and agricultural uses such as high-throughput drug screening and 3D body-condition scoring for dairy cows.
Over his 25-year career, Lau’s research has combined deep theoretical insight with practical system design, resulting in enduring impact across digital printing, imaging, sensing and machine vision. With a current focus on directed hypergraph signal processing and directed-hypergraph neural networks for game-structured systems, Lau’s long-term goal is to create a principled framework in which asymmetric, many-to many interactions—as found in multi-player games, multi-agent decision processes and complex engineered systems—are modeled, analyzed and learned using directed hypergraphs and their associated neural operators.
