Dynamic Flow Routing and Scheduling for Time-Critical Network Services
This talk discusses flow routing and scheduling for network services with stringent demand and deadline requirements.
Xuanli Lin
PhD Student in Computer Science
Xuanli Lin is a fifth-year PhD student in the Computer Science department at Arizona State University, supervised by Dr. Guoliang Xue.
His research interests include network optimization, network security, artificial intelligence, and the Internet of Things.
Recent Publications
You can also filter and search all my publications.
(2026).
Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations.
IEEE Transactions on Networking (Early Access).
(2026).
Multi-Pair Fidelity-Aware Rate Allocation in a Quantum Network: Approximation Schemes.
arXiv:2608.11501.
(2026).
Resource-Aware Intrusion Detection in Infrastructure Networks: A Game-Theoretic Approach.
arXiv:2608.06655.
(2026).
Task Offloading across Unreliable Edge Networks via Distributional Dynamic Programming.
IEEE INFOCOM, 1-6.
(2026).
AEGIS: Throughput-Guaranteed Resilient Routing via a Conditional Value-at-Risk Approach.
IEEE Transactions on Networking, 34, 4929-4943.
Recent & Upcoming Talks
Most Vulnerable Attack Trace in a Probabilistic Attack Graph
This talk discusses the definition of the most vulnerable attack trace in probabilistic graphs and methods to find them.
IoT System Vulnerability Analysis and Network Hardening with Shortest Attack Trace in a Weighted Attack Graph
This talk discusses how to analyze vulnerabilities in an attack graph using attack traces and ways to harden them.
8th ACM/IEEE Conference on Internet of Things Design and Implementation
Network Hardening in IoT Networks with Weighted Attack Graphs
This talk discusses how IoT networks can be analyzed in weighted attack graphs and how an effective hardening plan can be devised using attack graphs and attack traces.
Inferring User Activities from IoT Device Events in Smart Homes: Challenges and Opportunities
This talk discusses how user activities can be inferred from IoT device events even when device failures make activity patterns ambiguous.
31st International Conference on Computer Communications and Networks (ICCCN 2022)
Education
- PhD in Computer Science at Arizona State University, Tempe. 2021 - present
- MS in Computer Science at Arizona State University, Tempe. 2019 - 2021
- BS in Computer Science at Arizona State University, Tempe. 2015 - 2018
TAship
- Fall 2022 - Spring 2024, CSE 310 Data Structures and Algorithms
- Spring 2019, Spring 2020, CSE 460 Software Analysis and Design
Contact
- kazumi@asu.edu
- BYENG 492, 699 S Mill Ave, Tempe, AZ 85281