Research Projects in SRAII 2026
+ Evaluation of Internet Peninsulas of Partial Reachability in Network Routing (check if fixed) (mentor: John Heidemann)
Growing out our our work studying Internet reliability, we identified peninsulas as cases where parts of the Internet are reachable from some places but not from others. Peninsulas are important because they can prevent users from reaching what they want, and users have no way to resolve the problem. In prior REU [Saluja22a] and PhD [Baltra23a] work we identified peninsulas by sending pings, thereby testing data plane connectivity. In this project, the student will look for peninsulas in BGP routing data, testing control plane connectivity, and compare the two. Research questions include: how closely do data-plane and control-plane peninsulas compare? Does control-plane and BGP give us more vantage points (VPs) than data-plane? How does the number of VPs affect how many peninsulas we see?
Expected research outcomes: Publications and poster presentations, algorithms that can be applied to track future peninsulas combining data plane and control plane information.
Expected learning outcomes: Students will learn how to process large datasets using Hadoop, how to perform statistical hypothesis testing and how to correlate measurements, how to present their research and how to write research papers.
+ AI agents for testbed experimentation (check) (mentor: tregubov)
Testbed experimentation requires multiple complex steps to allocate resources, initialize them and connect to them. Different testbeds have their own constraints on the ordering and the details that the steps require, and their own interfaces for the users to enact the steps. These complexity and diversity issues make for a steep learning curve for new users. This project will build AI agents for testbed experimentation. Agents will use LLMs and testbed-specific documentation to learn how to execute basic tasks (e.g., allocate resources, open terminal access to a given resource, install software packages on allocated resources, etc.) and they will offer a chat-based UI to users to specify their experimental needs. Agents will then either ask for clarification, when needed, or translate user wishes into necessary testbed actions and execute them. Research questions include: identification of basic testbed operations that agents should support, development of AI agent architecture including safety and security safeguards to protect user resources and ensure fair use of testbed resources, understanding portability of AI agents across testbeds.
Expected research outcomes: Publications and poster presentations, AI agent design and prototype deployment on SPHERE testbed.
Expected learning outcomes: Students will learn how to design and build AI agents and how to evaluate their performance. They will learn how to implement safeguards for AI agents.
</div> </div>+ Wireless and Traditional Network Measurements from Mobile Platforms (to fix) (mentor: Erik Kline)
5G/6G wireless networks are poorly understood and few measurement mechanisms exist to understand them. This project aims to pierce opaque wireless networks through measurements conducted from the phones themselves. The student will leverage existing measurement tools, such as scamper, executed on the mobile platform (i.e., Android and iOS). Implementations of these tools exist on the platforms, but the student may need to modify them to be fully functional. Research questions include determining the effectiveness of measurement tools on mobile networks, learning about the structure of mobile networks, and how they interconnect with the broader Internet.
Expected research outcomes: Publications and poster presentations, tools for further measurement of mobile networks.
Expected learning outcomes: Students will learn how to write and deploy code on phones and phone emulators, how to measure wireless network structure, how to present their research and how to write research papers.
+ Analyzing the Impact of Misinformation on Voter Behavior in Online Platforms (mentor: Emilio Ferrara)
This project aims to explore the influence of misinformation on voter behavior within social media platforms. Given the increasing prevalence of online misinformation and its potential impact on democratic processes [Ferrara2016] including by prior REU students [Allen2021] and [Ko2021], this research is both timely and significant. Research questions will address the effectiveness of different misinformation types and the role of platform algorithms. The student will utilize a mixed-methods approach, combining quantitative data analysis with qualitative case studies to understand how misinformation spreads and influences voter decision-making.
Expected research outcomes: Publications and poster presentations, models that quantify how misinformation influences voter behaviors.
Expected learning outcomes: Students will learn how to work with large-scale social network data, how to identify and track misinformation online, how to present their research and how to write research papers.
+ Predicting Emergent Collective Behavior in Multi-Agent AI Systems (done) (mentor: Luca Luceri)
This project develops a novel framework for predicting emergent collective behaviors in multi-agent AI systems by measuring micro-scale incentive transformations through inverse reinforcement learning. Autonomous AI agents increasingly populate critical systems, yet we lack methods to predict where, when, or what type of collective behaviors will emerge as these agents interact. The framework infers latent reward functions governing agent decision-making from observed behavioral trajectories in simulated and deployed multi-agent environments, then uses graph neural architectures to forecast macro-scale phenomena including coordination patterns, system fragmentation, and consensus formation. This project advances fundamental understanding of emergence in artificial collectives, offering transformative capabilities for designing resilient multi-agent systems, detecting coordination failures before they cascade, and ensuring deployed AI systems behave as intended at scale.
Expected research outcomes: Validated predictive framework (>80% accuracy) for agentic systems; taxonomy of emergence mechanisms; open-source multi-agent simulation environments and emergence monitoring tools; benchmark datasets; publications in top-tier AI venues; demonstrations for autonomous systems applications.
Expected learning outcomes: Students will learn inverse reinforcement learning for multi-agent systems, graph neural architectures for modeling agent interactions, and methods for predicting emergent collective behaviors in artificial systems. They will gain hands-on experience designing controlled agentic environments, analyzing emergence patterns, and developing tools for safe autonomous system deployment.
</div> </div>+ Boosting Simulation Techniques for Quantum Many-Body Physics (mentor: Itay Hen)
Simulations of quantum many-body physics systems are notorious for being a challenging task for today’s computers or even super computers. The Hen group devotes a significant portion of its resources to developing numerical simulation codes designed to advance the current state of the art in the field, thereby allowing the physics community to gain insights into the properties of complex large-scale quantum materials. In this project, the student will inspect various sub-routines of these simulation algorithms and develop ideas and write code to improve them.
Expected research outcomes: Publications and poster presentations, improved simulation algorithms.
Expected learning outcomes: Students will learn how to work with quantum simulators, how to evaluate simulation algorithms and how to test them, how to present their research and how to write research papers.