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Farah Fahim is the Deputy Head of the Quantum Science Program at Fermilab. She is a Senior Engineer specializing in Mixed Signal ASIC design. She develops low-noise, high-speed, reconfigurable pixel detectors which operate in harsh environments for a variety of applications including High Energy Physics and Photon Science. Fahim has a PhD in Electrical and Computer Engineering from Northwestern University. She joined Fermilab in 2009 prior to which she was an Engineer at Rutherford Appleton Laboratory, UK. She holds four patents and several record of inventions. She received the best presentation award at IEEE NSS in 2016.
Brian Nord works at the intersection of artificial intelligence (AI), quantum computing, and astrophysics. Primarily, he uses big data sets from astronomy and AI techniques to learn about dark energy, dark matter and the very early universe. In particular, Brian uses deep neural networks to classify and measure large numbers of astronomical objects and remove noise from cosmic images. He applies generative modeling, like GANs, to produce fast simulations of the cosmos. He is also using deep reinforcement learning for the development of a self-driving telescope for automated astronomical observation and discovery.
On the other hand, Brian also uses big astronomical data sets to address key challenges with deep learning --- such as the interpretability of neural networks, integration of neural networks with statistics and integration of AI algorithms with physical models. In particular, he is developing techniques in Bayesian statistics to improve uncertainty quantification in deep learning models.
Brian is an Associate Scientist at Fermilab in Batavia, Illinois, and Senior Member of the Kavli Institute for Cosmological Physics. He is also the founder and Principal Investigator for the Deep Skies Lab (deepskieslab.ai), an international community of researchers who work in the space of artificial intelligence and astrophysics.
Areas of expertise: Artificial Intelligence; Cosmology; Astrophysics; Simulations; Deep Learning; Statistical Modeling
Dr. Laible specializes in Biophysics, with a research emphasis on the metabolic engineering and functional characterization of membrane proteins.
He joined Argonne National Laboratory in 1995 as a post-doc, examining the functional consequences of substitutions in membrane protein complexes of known structure that perform the initial energy and electron transfer reactions in photosynthetic organisms.
He has secured funding from a variety of government and industrial agencies, including the DOE, NIH, DARPA, and pharmaceutical and agribusiness companies. His team specializes in engineered variants of membrane protein complexes for directed abiotic applications, and he has led a large interdisciplinary, inter-institutional effort focused on the design of novel reagents for their stabilization and crystallization.
Activities have recently expanded into the use of the tools that his team has developed in schemes designed to produce next-generation biofuels and bioproducts. He also has long-standing interests in understanding the behavior of microorganisms in communities and regulatory mechanisms involved in energy, nutrient, and carbon utilization. The Argonne team he directs has extensive expertise in microfluidics, biosensors, deep learning, and genetic tools development and collaborates closely with researchers engaged in life-cycle analyses.
Meltem Urgun-Demirtas leads the Bioprocesses and Reactive Separations in Argonne National Laboratory’s Applied Materials Division. The group focuses on re-engineering of plant flow diagram to develop innovative technologies for industrial applications as well as development and application of intensified reactor and separation technologies for bioenergy and bioproducts production, water treatment, and manufacturing. She is also a Fellow at the Northwestern and Argonne Institute of Science and Engineering (NAISE).
Urgun-Demirtas has over 20 years of experience in the design and operation of chemical and bioprocesses, development and scale up of new technologies from bench- to pilot- and field-scale, techno-economic analysis and modeling of processes. Currently, she serves as Argonne’s Program Manager for Bioenergy Technologies Office of DOE which includes sustainability analysis of feedstock and development of new technologies and materials for production of biofuels and bioproducts.
Most recently, Urgun-Demirtas has been working on the development of new processes to produce renewable energy and products from organic waste streams and treatment of wastewater using advanced membrane technologies.
Matthew Reno is a Principal Member of Technical Staff in the Electric Power Systems Research Department at Sandia National Laboratories. His research focuses on distribution system modeling and analysis with big data and high penetrations of photovoltaics by applying cutting edge machine learning algorithms to power system problems. Matthew is also involved with the IEEE Power System Relaying Committee for developing guides and standards for protection of microgrids and systems with high penetrations of inverter-based resources. He received his Ph.D. in electrical engineering from Georgia Institute of Technology.
Frances Chance (a computational neuroscientist by training) has always been fascinated by how neural circuits compute information. She is intrigued by potential parallels between the operations of neural systems and the challenges faced by modern computers (for example analysis of large datasets). Her research program at Sandia Labs applies knowledge of neural systems towards the development of novel neuro-inspired algorithms and brain-based architectures to improve the performance of computing systems and other engineered systems. She received her PhD and MS from Brandeis University and her BS from the California Institute of Technology.