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About

Lihao Fu is a pioneering researcher at the intersection of synthetic biology, automation, and machine learning, whose work is reshaping how scientists design and engineer biological systems. With a focus on biological foundries and high-throughput experimentation, Fu has made significant contributions to accelerating the traditionally slow and labor-intensive cycles of strain engineering and protein evolution. His landmark work on applying Bayesian optimization to directed protein evolution — garnering over 60 citations — demonstrated how machine learning could dramatically reduce experimental burden by intelligently prioritizing candidate sequences before costly wet lab validation. Fu's research on automating synthetic biology workflows, including robotic construction and screening of lanthipeptide variant libraries in *E. coli*, highlights his commitment to scalable, reproducible science. His broader reviews on biological foundries and build-and-test automation in biofuel research, collectively accumulating over 80 citations, have helped establish conceptual frameworks guiding the field. His development of mass spectrometric screening approaches capable of processing nearly one sample per second further underscores his drive toward practical, high-throughput solutions. Fu's body of work represents a compelling vision for the future of automated, data-driven biotechnology.