Conference Information

Quantum Computing and Optimization
Lehigh University
July 30 through August 11, 2023

By harnessing the properties of subatomic particles, quantum computing (QC) has the potential to radically transform our capability to solve extremely difficult decision-making and optimization problems for which no efficient classical algorithms exist. The goal for this program is to help build the next generation of QC researchers by bringing together QC and optimization experts and students in an inquiry-based learning environment. We will provide a concentrated review of necessary prerequisite math, optimization, and classical and quantum computing material. The QC-specific curriculum will then be broken into three parts: foundations of QC, quantum optimization techniques, and advanced topics and applications in QC. Students will work closely with lecturers to apply their learning by engaging in interactive seminars and labs.

The summer school will be held at Lehigh University, Bethlehem, PA, from July 30 to August 12, 2023. In-person attendance is required. Graduate and advanced undergraduate students highly motivated to learn QC and optimization techniques are encouraged to apply. Experience in QC or optimization is not required to join the program.

Room and Board, Travel Assistance:

We will provide room and board (meals included) at Lehigh University to all accepted students. We will also provide a travel award of up to $500 per student. Exceptions to this travel award cap will be considered on a case-by-case basis only. Further information on funding and reimbursement will be provided upon notification of acceptance.


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Tamás Terlaky 
Industrial and Systems Engineering
Lehigh University
Luis Zuluaga
Associate Professor
Industrial and Systems Engineering
Lehigh University
Arielle Carr
Assistant Professor
Computer Science and Engineering
Lehigh University



David Bernal

Swati Gupta

Jeffrey Larson

Giacomo Nannicini

Ruslan Shaydulin

Stephen Thomas

Faculty Bios:

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.

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.

Stephen Thomas completed a Ph.D at the University of Montreal in computational math and is fluent in French. He also has an M.Eng from McGill and B.Math from the University of Waterloo. Throughout his career, he has focused on the intersection of high performance computing and scalable iterative solvers for large sparse linear systems with applications in weather/climate (EnvCan, NCAR, ANL), geoscience (Schlumberger), and renewable energy (DOE NREL, LLNL). His projects at NREL were focused on solvers for wind turbine simulations (ExaWind), and combustion (PeleLM) as part of the U.S. Department of Energy (DOE) Exascale Computing Program (ECP). Stephen has also taught graduate and undergraduate math courses at McGill University and CU Boulder.


The program will run from July 30 through August 11, 2023 at Lehigh University. More detailed information about courses will be posted closer to these dates. 


The summer school will be held at Lehigh University with program activities primarily held in the Industrial and Systems Engineering Department’s Mohler Lab. Bethlehem, Pennsylvania is a vibrant city and community that hosts many activities throughout the summer that students may enjoy during their downtime.