Albert Keung, Ph.D.

Associate Professor, North Carolina State University

Albert Keung is an associate professor of chemical and biomolecular engineering, a University Faculty Scholar and Goodnight Early Career Innovator at North Carolina State University. His group applies synthetic biology and stem cell engineering approaches in three focus areas. Current work includes engineering human stem cell models to study neuroepigenetic mechanisms of addiction and neurodevelopmental disorders with a focus on Angelman Syndrome, developing high throughput synthetic biology platforms to study epigenetics and dynamic information transmission through chromatin, and creating scalable and highly dense DNA-based information storage systems. A unifying conceptual framework within the Keung group is discovering and harnessing the diverse mechanisms by which biological information is stored, accessed and transmitted beyond the genetic sequence. Keung trained at Stanford University (B.S. in chemical engineering), University of California, Berkeley (Ph.D. in chemical engineering) and MIT/Boston University (postdoctoral training in bioengineering). The collective work of those in the Keung group have been recognized by the NSF CAREER award, ACS Synthetic Biology Young Investigator Award, NIH Avenir Award and the Cure Angelman Syndrome Innovation Award.


Machine learning the hidden frontiers of biological function

Albert Keung is a synthetic biologist and stem cell engineer. His group has developed new technologies and experimental platforms to study epigenetic regulation in stem cell derived models of early human neurodevelopment, to help decipher the “histone code” using protein engineering platforms, and to engineer synthetic biology systems for new applications outside the realm of native biology, such as DNA-based digital data storage. Common across these applications is the growing capability to efficiently probe the massive parameter and functional spaces of biology. These types of capabilities have naturally led to the recent exciting applications of machine learning and artificial intelligence to diverse problems across many biological disciplines. Through the Simons Foundation Pivot Fellowship, Keung will immerse himself in the field of interpretable machine learning through the pioneering expertise and mentorship of Cynthia Rudin. Specifically, he will focus on key approaches to deal with sparsity of representation, especially in the context of the immense diversity of human health and biology, and how interpretable machine learning methods can promote responsible and equitable models in the face of this challenge. Related, interpretable methods could provide insights into biological function rather than just establish correlations, a common challenge in data intensive biological research. Supporting these efforts will be the development of new approaches to interface iterative experimental design cycles with machine learning to explore parameter spaces both native and non-native to biology. A central goal of the fellowship will also be to develop strategies in which molecular and biological concepts can be fused with disparate macroscale data types and applied to challenges facing society including health care, clinical outcomes, drug discovery and epidemiology.

For NSF CAREER, ‘award’ is not officially part of the title of the award so is lowercase. For the other accolades, ‘Award’ is capitalized because they are part of the official title.

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