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She leads the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the U.K., her career spans research in particle physics, cosmology, and computing on both sides of the Atlantic. She obtained her Ph.D. at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining NERSC. Her group leads the support of supercomputing for experimental science, and her work focuses on data-intensive computing and research. This includes using high-performance computing (HPC) to scale up machine-learning algorithms that can tackle new, larger scientific problems; and leveraging artificial intelligence to gain insight from cosmological data.
Nicola Ferrier received her doctorate from Harvard University in 1992. After postdoctoral fellowships at Oxford University and Harvard, she joined the Department of Mechanical Engineering at the University of Wisconsin (UW)-Madison in 1996. She became an associate professor in 2003 and professor in 2009. She received the NSF CAREER award (1997) and the UW Vilas Associates Professorship (1999) and the UW Honored Instructor Award (2009). She joined the Mathematics and Computer Science Division at Argonne in 2013.
Ferrier’s research interests are in the use of computer vision (digital images) to control robots, machinery, and devices, with applications as diverse as medical systems, manufacturing, and projects that facilitate “scientific discovery” (such as her recent project using machine vision and robotics for plant phenotype studies).
Lawrence Berkeley National Laboratory (Berkeley Lab), a U. S. Department of Energy Office of Science national lab managed by the University of California, delivers science solutions to the world – solutions derived from hundreds of patented and patent pending technologies plus scores of copyrighted software tools and published, peer-reviewed manuscripts.
Berkeley Lab has more than one hundred cutting-edge research projects using AI to find new scientific solutions to national problems. Through this effort, computer scientists, mathematicians, and domain scientists are collaborating to turn burgeoning datasets into scientific insights. Visit Berkeley Lab’s Machine Learning for Science site for more information.
Berkeley Lab’s advanced materials expertise is applied to innovation in batteries and other energy storage technologies, semiconductors, and photovoltaics. Additional energy-related areas of expertise include grid modernization and security, bio-based fuels and chemicals and building energy and demand response. Several National User Facilities are available for collaborative engagement: the Advanced Light Source, Molecular Foundry, National Energy Research Scientific Computing Center (NERSC), Energy Sciences Network, and the Joint Genome Institute. Other specialized facilities include FLEXLAB for building energy research and the Advanced Biofuels Process Demonstration Unit.
Ernest Orlando Lawrence, the lab's founder, believed team science yielded the greatest discoveries. That belief is reflected today in interdisciplinary teams and collaborative projects connecting Berkeley Lab, industry, and other research organizations. Berkeley Lab's Intellectual Property Office, connects industry partners with lab innovations and unique facilities to enable lab-to-market transition.