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Jayakar “Charles” Tobin Thangaraj is currently the Science and Technology Manager and the Deputy Director at the Illinois Accelerator Research Center (IARC). He works at the frontiers of accelerator science where bold ideas enable discoveries that transform our fundamental understanding of the universe. He is passionate about partnership between science, technology and startups to enable entrepreneurship and innovation to solve 21st century challenges in environment, medicine and society. He received both his M.S. and PhD from the University of Maryland. Charles joined Fermilab as a People’s Fellow in 2009.
Areas of expertise: Artificial Intelligence for Accelerators; Machine Learning for Accelerators
Nhan Tran is a Wilson Fellow at Fermilab working on the Compact Muon Solenoid experiment at the Large Hadron Collider and is also developing new dark sector experimental initiatives. He is generally interested in deploying machine learning as a powerful tool across fundamental physics. His recent research focus is on the intersection of machine learning with real-time systems and embedded electronics as well as heterogeneous computing to improve experimental efficiency and sensitivity. He received his PhD from Johns Hopkins University in 2011 and was a postdoctoral researcher at Fermilab prior to joining in his current position.
Areas of expertise: ML Algorithms for Data Reconstruction and Pattern Recognition; Real-Time Low-Latency ML in Resource-Constrained Environments; Heterogeneous Computing
Gabriel Perdue is a Scientist in Fermilab’s Quantum Institute, where he works on quantum computing for simulation and machine learning, and more generally on machine learning in physics. He also has a long history at Fermilab in neutrino physics and spent the last decade working on the MINERvA experiment and on the GENIE MC event generator.
Areas of expertise: AI Algorithms for Data Analysis and Systems Control
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