Mohammadhossein Mohammadisiahroudi
Assistant Professor
Math/Stat Department
UMBC
Joint work with Zeguan Wu, Brandon Augustino, Tamás Terlaky, and Giacomo Nannicini
12 noon–1pm
Friday, October 17, 2025
Remotely via WebEx: https://umbc.webex.com/meet/sherman
Abstract:
Quantum computing has emerged as a transformative computational paradigm capable of solving certain classically
intractable problems exponentially faster. Among its groundbreaking developments, Shor’s algorithm demonstrated an
exponential speedup for integer factorization, posing a potential threat to existing cryptographic systems and highlighting
the disruptive impact of quantum computing on cybersecurity. Quantum computing, however, also presents opportunities
to strengthen cybersecurity by enhancing optimization and information-processing capabilities. In this talk, we explore the
intersection of quantum computing and optimization, focusing on quantum algorithms that offer computational
advantages in solving large-scale optimization problems. In particular, we discuss the Quantum Interior Point Method,
which provides a complexity advantage for solving conic optimization problems. We further illustrate potential
applications in cybersecurity and information science, such as optimization models involving entropy and relative entropy
cones, demonstrating how quantum optimization techniques can contribute to secure and efficient cyber-infrastructures.
About the Speaker:
Mohammad (mhms379@umbc.edu) is an assistant professor in the Department of Mathematics and Statistics at UMBC.
He is also member of the Quantum Science Institute and Cybersecurity Institute. Before joining UMBC, from 2024 to
2025, he was a postdoctoral research associate in the Quantum Computing and Optimization Lab at Lehigh University. He
received his PhD in industrial and systems engineering from Lehigh University in August 2024. His research focuses on
developing, analyzing, and implementing efficient quantum and classical algorithms for solving large-scale optimization
problems arising in various applications, including machine learning, healthcare, and cybersecurity. His work has been
recognized with the 2025 Pritsker Doctoral Dissertation Award from Institute of Industrial and System Engineers, the
2023 INFORMS Computing Society Best Student Paper Prize, and the 2023 Van Hoesen Family Best Publication Award.
Host:
Alan T. Sherman, sherman@umbc.edu
Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.
The UMBC Cyber Defense Lab meets biweekly Fridays 12-1pm. All meetings are open to the public.
Upcoming CDL meetings:
Oct 31, 2025: Alan Sherman, String Matching by Humans through Simultaneous Presentation
Nov 14, 2025: Fabio Anza (Physics)
Nov 28, 2025: Thanksgiving weekend
Dec 12, 2025: Alan Sherman and Enis Golazewski, Security Analysis of the SecureDNA System