Anna Grasselino, Senior Scientist, Fermilab; Center Director, SQMS
Katherine Klymko, NERSC
Bios: (in alphabetical order)
David Bernal has a Ph.D. in Chemical Engineering from Carnegie Mellon University, where one of his interests was to study the use of quantum algorithms for nonlinear combinatorial optimization. During his Ph.D., David developed and co-taught the course on Quantum Integer Programming. David is currently an Associate Scientist in Quantum Computing at the Research Institute of Advanced Computer Science (RIACS) of the Universities Space Research Association (USRA) and QuAIL at NASA, developing and benchmarking quantum and physics-inspired optimization methods. After this position, David will start a tenure-track position at the Davidson School of Chemical Engineering at Purdue University.
Arielle Carr is an assistant professor at Lehigh University in the Computer Science and Engineering department. Her research focus is in the fields of applied linear algebra and applied numerical analysis, particularly on analysis and development of parallel algorithms and recycling techniques to improve costs associated with computing the iterative solution of large, sparse linear systems and eigenvalue problems. More recently, she has been exploring the necessary theoretical and practical modifications to effectively translate classical linear algebra to quantum linear algebra. She joined Lehigh as a professor of practice in 2018, and earned her Ph.D. in mathematics from Virginia Tech in 2021.
Swati Gupta is an incoming Assistant Professor at the Sloan School of Management at MIT, starting July 2023. Currently, she is a Fouts Family Early Career Professor and Assistant Professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech. She serves as the lead of Ethical AI in the NSF AI Institute on Advances in Optimization. She received a Ph.D. in Operations Research from MIT. Her research interests include optimization, machine learning, and algorithmic fairness, spanning various domains such as e-commerce, quantum optimization, and energy. She received the Class of 1934: Student Recognition of Excellence in Teaching in 2021 and 2022 at Georgia Tech, the JP Morgan Early Career Faculty Award in 2021, and the NSF CISE Research Initiation Initiative Award in 2019, and the Google Women in Engineering Award (India) in 2011. She was also awarded the prestigious Simons-Berkeley Research Fellowship in 2017-2018, where she was selected as the Microsoft Research Fellow in 2018. Her research and students have received recognition at various venues like INFORMS Doing Good with OR 2022 (finalist), MIP Poster 2022 (honorable mention), INFORMS Undergraduate Operations Research 2018 (honorable mention), INFORMS Computing Society 2016 (special recognition), and INFORMS Service Science Student Paper 2016 (finalist). Dr. Gupta’s research is partially funded by the NSF and DARPA.
Jeffrey Larson is a computational mathematician in the Mathematics and Computer Science division at Argonne National Laboratory. He develops, analyzes, and implements algorithms for solving difficult numerical optimization problems. He is especially interested in algorithms for solving problems in quantum information science, simulation optimization, and vehicle routing. Jeff joined Argonne in 2014 as a postdoctoral appointee after a postdoctoral position at the KTH Royal Institute of Technology. He earned his Ph.D. in applied mathematics from the University of Colorado Denver in 2012.
Giacomo Nannicini is an Associate Professor in the Daniel J. Epstein department of Industrial & Systems Engineering, University of Southern California (USC). Before joining USC, he was a Research Staff Member in the Quantum Algorithms group at the IBM T. J. Watson Research Center. He was also an assistant professor in Engineering Systems and Design at the Singapore University of Technology and Design. His main research interest is optimization broadly defined and its applications. Giacomo received several awards, including the 2021 Beale–Orchard-Hays prize, the 2015 Robert Faure prize, and the 2012 Glover-Klingman prize. His algorithms and software are used by one of the largest real-time traffic and mobility information groups in Europe and in IBM Watson Studio
Ruslan Shaydulin is a quantum algorithms researcher at the Global Technology Applied Research center at JPMorgan Chase. Ruslan’s research centers on applying quantum algorithms to classical problems, with a focus on optimization and machine learning. Prior to joining JPMorgan Chase, Ruslan was a Maria Goeppert Mayer fellow at Argonne National Laboratory.