Network & Security Research Laboratory (NETSEC) was established after Prof. Jun Li joined U of O in Fall, 2002. NETSEC lab has graduate and undergraduate student researchers, visiting students and scholars, and collaborators from both industry and academia all over the world, closely working together on various research topics related to cyber security, especially network security. NETSEC lab actively research how to make networking perform better and more securely, seeking cutting-edge solutions that are not only thought-provoking in a lab environment, but also feasible and deployable in the real world.

The Oregon Network Research Group (ONRG) in the Department of Computer and Information Science (CIS) at the University of Oregon (UO) conducts research in several applied areas of network systems. ONRG (formerly known as MIRAGE group) was founded by Professor Reza Rejaie in January 2004. The lab is located at Room 331 of the CIS Department (Deschutes Hall) at UO campus. The lab's research on network traffic analysis and online social networking are particularly related to cybersecurity.

The Adversarial Machine Learning Laboratory directed by PI Lowd makes use of shared infrastructure, such as ACISS, to run large-scale machine learning experiments. Current experiments involve learning statistical models for webspam and other adversarial domains that are robust to malicious noise, as well as exploratory attacks on classifier systems. It plans to acquire additional servers for hosting data repositories and running smaller-scale experiments. The lab also collaborates and shares resources with the Advanced Integration and Mining Laboratory run by Prof. Dejing Dou at UO.

With the emergence of general-purpose system-on-chip (SoC) architectures in an array of application domains, some key security challenges arise. In these systems, tenants, i.e., intellectual property (IP) cores or processing units, may come from different providers and executable code may have varying levels of trust. It is therefore important to support multi-level user-defined security protocols that can isolate hardware subsystem and code while enabling optimal sharing of computing resources and data among the tenants. In this work, we are developing security mechanisms for integrating multiple components, such as secure to non-secure cores, into the same chip design, while also maintaining their individual security, preventing data leakage and corruption, and promoting collaboration among the components.

The High Performance Computing (HPC) Laboratory directed by PI Norris conducts research in several areas of high-performance computing (HPC), including static analysis of software for building performance models and detecting security vulnerabilities, and source-to-source approaches for semantics-preserving (e.g., performance optimization) and semantics-modifying (e.g., security vulnerability fixes, automatic differentiation) transformations. The HPC Lab also performs research in modeling runtime characteristics of software, and developing and employing numerical optimization techniques for maxi- mizing multiple runtime objectives (performance, energy efficiency, resilience, etc.).

The Performance Research Laboratory directed by PI Malony works on projects funded by DOE and NSF to develop parallel performance measurement and analysis tools. In particular, the TAU performance system is a state-of-the-art, open source performance tool suite used widely in national laboratories and high performance computing research centers. Through a Major Research Instrumentation grant from NSF, the lab has created a large-scale cloud system named ACISS, a heterogeneous platform used for computational science, informatics, and data science.