Noah Fahlgren, PhD
Dr. Noah Fahlgren is a prominent scientist who leads the data science facility at the Donald Danforth Plant Science Center, where his work spans the cutting-edge intersections of high-throughput phenotyping, computer vision, machine learning, genomics, and computational biology. His journey into plant science began during his time as an undergraduate researcher in Dr. Jim Carrington’s lab at Oregon State University, coinciding with the revolutionary emergence of high-throughput DNA sequencing. Today, at the Danforth Center, Dr. Fahlgren’s team focuses on building innovative computational tools designed to bridge different fields of expertise and empower scientists to solve complex big data challenges. Notably, he is the pioneer behind PlantCV, a widely recognized open-source image analysis software package that enables researchers worldwide to extract biologically meaningful data from hundreds of thousands of plant phenotyping images.
Ananth Kalyanaraman, PhD
Professor, Boeing Chair in Computer Science, WSU.
Dr. Ananth Kalyanaraman is the Director of the School of Electrical Engineering and Computer Science and the Boeing Chair of Computer Science at Washington State University. He serves as the lead PI and Director of the NSF-USDA NIFA AgAID AI Institute. Additionally, he holds a joint appointment at the Pacific Northwest National Laboratory (PNNL). He serves as affiliate faculty for the Molecular Plant Sciences Graduate Program and the Paul G. Allen School for Global Health. He earned his Ph.D. in Computer Engineering and an M.S. in Computer Science from Iowa State University, following his B.E. in Computer Science and Engineering from the Visvesvaraya National Institute of Technology. His research bridges the fields of parallel computing, graph analytics, and computational biology, with an emphasis on integrating data science, AI, and machine learning into practical, real-world applications. His work is centered on the development of scalable algorithms and software designed to analyze massive datasets, particularly within the domains of agriculture, plant sciences, and the broader life sciences.
Stephen Ficklin, PhD
Associate Professor and Chair of the Department of Horticulture, WSU.
Dr. Stephen Ficklin leads a computational dry lab at WSU dedicated to bridging the gap between advanced data science and agricultural systems. His research program focuses on the creation of software tools, computational approaches, and systems-level models to address both basic and applied biological hypotheses at the molecular level, with a heavy emphasis on transcriptomics, gene expression analysis, and the development of machine learning models that learn gene relationships from RNA-seq data. With a Ph.D. in Plant and Environmental Sciences from Clemson University, Dr. Ficklin’s expertise spans systems genetics, multiomic networks, whole-genome assembly, and the development of community biological databases using the Tripal software platform. His work uniquely embodies the intersection of biological systems and computational intelligence, making him a perfect fit to speak on how advanced informatics can decode complex plant traits and accelerate modern agricultural innovation.
Jia Dong, PhD
College of Agriculture and Life Sciences, Cornell University.
Dr. Jia Dong’s research sits at the cutting edge of agricultural innovation, combining bioengineering, synthetic biology, robotics, and artificial intelligence to boost crop yield and environmental resilience. A Faculty Fellow at the Cornell Atkinson Center for Sustainability, she focuses on uncovering the molecular mechanisms behind crop productivity and plant stress responses, and translating those discoveries into the development of resilient, sustainable crop varieties. Her work pioneers high-throughput automation by harnessing a robotic biofoundry to establish a rapid, automated Design-Build-Test-Learn (DBTL) engineering cycle. This scalable platform has successfully generated new crop varieties with enhanced oil accumulation and is being deployed to optimize yields, drought tolerance, and disease resilience across key food and energy crops, including sorghum, sugarcane, and tobacco. Most recently, her research focuses on integrating robotics and AI to drive high-throughput protein engineering in maize. Her expertise illustrates how the fusion of robotics and machine learning is breaking traditional bottlenecks to shape the next generation of scalable crop development.