James Amundson, Fermilab: Dr. Amundson is the Associate Lab Director (ALD) of the Computational Science and AI Directorate, here at Fermilab. Jim received his PhD in theoretical particle physics from the University of Chicago. He was a postdoctoral research associate at Michigan State University and a visiting assistant professor at University of Wisconsin. He joined the laboratory in 1998. His research work includes both computational accelerator physics and quantum computing applications.
Rina Barber, University of Chicago: Rina Foygel Barber is a Professor in the Department of Statistics at the University of Chicago. Before starting at U of C, she was a NSF postdoctoral fellow during 2012-13 in the Department of Statistics at Stanford University, supervised by Emmanuel Candès. She received her PhD in Statistics at the University of Chicago in 2012, advised by Mathias Drton and Nati Srebro, and a MS in Mathematics at the University of Chicago in 2009. Her research interests are in developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems such as sparse regression, sparse nonparametric models, and low-rank models. She works on developing methods for false discovery rate control in settings where we may have undersampled data or misspecified models, and for distribution-free inference in settings where the data distribution is unknown.
Mathew Cherukara, Argonne: Mathew Cherukara received his Ph.D in computational materials science and engineering from Purdue University, and a bachelors in materials engineering from the Indian Institute of Technology (IIT) Madras. He is now the Group Leader of the Computational X-ray Science group at the Advanced Photon Source at Argonne National Laboratory. His research leverages AI to enhance and accelerate materials characterization, materials modeling and high-throughput materials screening. Examples include novel AI-enabled nanoscale imaging methods, AI-developed physical models, and AI-models for rapid material property prediction.
Bonnie Fleming, Fermilab: Dr. Fleming is Fermilab’s chief research officer and deputy director responsible for leading all areas of science and technology. From 2004 until just a couple weeks ago, Dr. Fleming was a faculty at Yale University. On September 6th, she transitioned into the University of Chicago’s as a professor of physics and part of the Enrico Fermi Institute.
Bonnie began her relationship with Fermilab as a Columbia University graduate student, working on the NuTeV experiment and later as a Lederman Fellow working on MiniBooNE. She is an internationally recognized expert in neutrino physics. She is the founding spokesperson for the ArgoNeuT and the MicroBooNE neutrino experiments, and a pioneer in developing the Liquid Argon Time Projection Chambers detector technology.
Eric Jonas, University of Chicago: Eric Jonas is an Assistant Professor in Computer Science at the University of Chicago, developing machine learning techniques to accelerate and improve scientific measurement, specifically focused on structured and combinatorial inverse problems. He earned his PhD, MEng, and MS all from MIT where he worked on neural data acquisition and hardware acceleration for machine learning. Prior to his return to academia, he was founder and CEO of Prior Knowledge, an AI database company which was acquired in 2012 by Salesforce.com, where he was Chief Predictive Scientist until 2014. In 2015 he was named one of the top rising stars in bioengineering by the Defense Department’s Advanced Research Projects Agency (DARPA).
Jennifer Ngadiuba, Fermilab: Jennifer is an Associate Scientist and Wilson Fellow at Fermilab. She is working on applications of AI to high-energy physics problems while being member of the CMS collaboration (one of the experiments at the CERN LHC). She received her PhD at the University of Zurich working on CMS data analyses and inner silicon pixel subsystems. After that, she was a postdoctoral fellow at CERN and then at Caltech, where she contributed to the CMS trigger system for which she developed fast AI methods for data analysis in real-time. Starting in 2022, she is one of the L2 coordinators of the CMS ML group.