Ilias Tagkopoulos, University of California, Davis 


Challenges and Opportunities at the Interface of AI and the Food System 


Abstract: Artificial Intelligence (AI) has the potential to transform US food systems by targeting its biggest challenges: improving food yield, quality, and nutrition, decreasing resource consumption, increasing safety, resiliency, and traceability, and eliminating food waste. Despite big leaps in AI capacity, food systems present several challenges in the application and adoption of AI: (1) Food systems are highly diverse and biologically complex, (2) ground-truth data is sparse, costly, and privately held, and (3) human decisions and preferences are intricately linked to every stage of food system supply chains. To address these challenges, we have created the USDA/NIFA AI Institute of Next Generation Food Systems, which aims to meet growing demands in the food supply by increasing efficiencies using AI and bioinformatics across food production and distribution systems. The Institute, launched in October 2020, brings together researchers from six institutions - UC Davis; UC Berkeley; Cornell University; the University of Illinois, Urbana-Champaign; UC Agriculture and Natural Resources; and the USDA's Agricultural Research Service. Through the integration of digital and biological technologies, AIFS research will pursue multidisciplinary science, industry engagement, and workforce development to address challenges across the U.S. food system.

Dr. Tagkopoulos is a Professor in the Computer Science and Genome Center at the University of California, Davis, and is the Principal Investigator and director of the newly formed USDA/NIFA AI Institute of Next Generation Food Systems - AIFS. He leads the predictive biology laboratory at Davis, an interdisciplinary group of computer scientists and experimental biologists working in topics related to AI, Bioinformatics, and Life sciences. He earned a Dipl.-Ing. in Electrical and Computer Engineering from the University of Patras in Greece, an MS in Microelectronics from Columbia University, and a PhD in Electrical Engineering from Princeton University.