Phillip Kerger

Phillip Kerger completed his PhD in Applied Mathematics at Johns Hopkins University, where his research focused on optimization, classical and quantum algorithms, and complexity. Professor Kerger’s work at NASA’s Quantum Artificial Intelligence Laboratory (QuAIL), in collaboration with USRA, allowed him to delve into distributed quantum algorithms. Prior to his PhD, he earned his bachelor’s degree in mathematics from Fordham University, where he conducted research on parameter estimation for heavy-tailed stochastic processes.

Dr. Kerger is deeply passionate about teaching. At Johns Hopkins University, he served as the primary instructor for multiple courses and was recognized as a teaching fellow in the Department of Applied Mathematics and Statistics, along with several awards for his work as a teaching assistant.

• Optimization
• Quantum Computing
• Integer Programming
• Probability

• Kerger, Molinaro, Jiang, Basu 2024. A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles. Accepted to ICML 2024, in press.
• Basu, Jiang, Kerger, Molinaro, * 2024. Information Complexity of Mixed-Integer Convex Optimization, published in Mathematical Programming, Series B. Available here.
• Kerger, Miyazaki 2023. Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing. Published in Frontiers in Theoretical Computer Science's "Experience in Quantum Annealing" research topic, 2023. Available here.
• Kerger, Bernal Neira, Gonzalez Izquierdo, Rieffel 2023. Mind the O-tilde: Asymptotically Better, but Still Impractical, Quantum Distributed Algorithms. Published in Algorithms, 2023. Available here.
• Basu, Jiang, Kerger, Molinaro, * 2023. Information Complexity of Mixed-Integer Convex Optimization, IPCO 2023 proceedings. Available here.
• Kerger, Phillip & Kobayashi, Kei, 2020. Parameter estimation for one-sided heavy-tailed distributions. Published in Statistics & Probability Letters, Elsevier, vol. 164(C). Available here.