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Recent experiments have revealed surprisingly large performance variation across repeated executions of some applications, even after taking standard benchmarking precautions. One possible explanation is that ASLR produces memory layouts with significantly different performance characteristics. If so, an important challenge is determining how these layouts differ and identifying the memory-layout properties responsible for the observed performance changes.

A possible research direction is to develop techniques and tools for detecting ASLR-induced performance variation, comparing memory layouts across executions, and identifying the characteristics that distinguish faster and slower runs. Such a tool could potentially build upon HeapLENS and leverage AI-assisted analysis to help explain observed performance differences.

Tags: C/C++, Data Structures, Multithreading, Memory Management, Operating Systems, Systems, 2nd Year +

Professor T. Brown recently developed a system called HeapLENS to help researchers automatically examine the memory layout of multithreaded applications. HeapLENS is specifically designed to produce compact, high-quality, curated output suitable for AI-driven analysis. While HeapLENS output can already enable AI agents to improve application memory layouts by a significant margin, the current workflow invokes HeapLENS only once and uses its output only once. A natural research direction is therefore to adapt HeapLENS to support repeated interaction with an AI agent, enabling an iterative optimization cycle in which incremental changes can be proposed, evaluated, and refined.

Tags: C/C++, Data Structures, Multithreading, Memory Management, Operating Systems, Systems, Artificial Intelligence, 2nd Year +

Professor T. Brown and collaborators recently designed a concurrent version of the van Emde Boas tree that incorporates a number of novel space optimizations and can outperform other state-of-the-art concurrent ordered sets by a large margin. However, this data structure relies on hardware transactional memory (HTM) for synchronization. The goal of this project is to extend this work to universally available synchronization mechanisms for systems without HTM support, with optimistic concurrency control (OCC) being one natural direction.

Tags: C/C++, Data Structures, Multithreading, Systems, 2nd Year +

Modern AI and machine learning systems are increasingly trained and deployed on distributed infrastructures consisting of multiple servers working together. While distributed computing enables larger models and faster processing, it also introduces new security challenges. Communication between nodes, shared resources, and distributed coordination mechanisms can create vulnerabilities that may not exist in single-machine systems. The goal of this project is to understand and evaluate security risks that arise when training or running AI/ML models in distributed environments. By identifying and studying these vulnerabilities, we can help build more secure and trustworthy AI systems.

Tags: Networks, Operating Systems, Artificial Intelligence, Machine Learning, Security, Systems, All Years

In this project, students will evaluate a recently proposed CDC algorithm and compare it against state-of-the-art techniques. Working in teams, students will use open-source implementations and real-world datasets to conduct benchmarking experiments and measure metrics such as chunking throughput, chunk-size distributions, and deduplication efficiency. Team members will focus on complementary tasks, including experiment design, dataset preparation, benchmarking, data analysis, and visualization. The primary goal during the program is to evaluate the algorithm and understand its strengths and limitations. Students who continue beyond the program may also explore integrating the algorithm into an open-source benchmarking framework and investigating further improvements to chunking techniques.

Tags: Systems, Algorithms, C/C++, Go, Python, All Years