Presenter: Hans Boehm, Google Abstract: C++11 extended the language to include threads, defining a concurrency memory model to specify the semantics of shared variables, including “atomic” variables that can be accessed without mutual exclusion. Although this followed Posix threads by more than a decade, and the revision of the Java memory model by a few […]
Speaker: Koushik Sen, UC Berkeley and Google DeepMind Abstract: Coding has emerged as an important application area for large language models (LLMs), with a proliferation of code-specific models and their applications across various domains and tasks such as program repair, performance optimization, debugging, test generation, documentation, and security hardening. In this talk, I will describe […]
Presenter: Roozbeh Mottaghi, University of Washington Abstract: Data has revolutionized progress across AI fields like natural language processing and computer vision. Yet, in robotics, data collection remains a significant challenge: robots must interact with complex, dynamic environments, making the process slow, costly, and difficult to scale. In this talk, I will discuss how simulation is […]
The United Nations (UN) and the Baskin School of Engineering at the University of California, Santa Cruz, are collaborating to bring the “Reboot the Earth” hackathon to the West Coast for the first time. This is a social event bringing together aspiring developers to create open source software solutions that address the climate crisis, including […]
Modern AI systems demand low-latency high-quality retrieval and serving over billion-scale keys and vectors. This proposal studies learned hashing and overlay networks to co-locate semantically related items and steer queries with minimal coordination. We first present LEAD, to our knowledge the first use of order-preserving learned hash functions in distributed key-value overlays, enabling efficient range […]
Presenter: Luis Lamb, Catholic Institute of Technology Abstract: Neurosymbolic AI brings together the statistical nature of machine learning with the formal reasoning capabilities of symbolic AI. It seeks to offer a balanced approach to contemporary AI technologies, by combining the ability to learn from data, with the capacity to reason upon knowledge acquired from an environment. […]
This dissertation focuses on designing and proving performance guarantees on algorithms when there is uncertainty in the input. The uncertainty could be from the user being unsure or future inputs that have not arrived yet. We look at different methods in which algorithms can be designed to be competitive against the optimal. One of the […]
Presenter: Ranjit Jhala, UCSD Abstract: Rust has risen as a language of choice for new systems code — from OS kernels to hypervisors, firmware and run-times — as it is memory safe and provides the sort of abstractions needed for efficient low-level systems implementation. We present Flux, a refinement type checker for Rust that shows how […]
One of the central challenges in quantum computing is finding or approximating the ground-state energy of a local Hamiltonian, a quantum analogue of classical constraint satisfaction problems (CSPs). Among these, the Quantum Max-Cut problem serves as a canonical example, paralleling the classical Max-Cut problem. Despite its foundational importance in both theoretical computer science and condensed […]
Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However, in the modern era, computational environments have changed drastically, and we […]
Presenter: Suresh Jagganathan, Purdue University Abstract: Unlike program verifiers, symbolic execution and property-based testing tools underapproximate program behavior: they aim to report only real bugs (no false positives), at the cost of potentially missing some (false negatives). Recent work has sought to place such tools on a more formal footing, primarily through the development of incorrectness […]
Join Graduate Womxn in Computing (GradWiC) for our final Womxn’s Luncheon of the quarter. We will be on the E2 Lanai patio weather allowing, or E2-599 in the case of inclement weather.
Simulation is a coinductive proof technique to assert the behavioral equivalence of computing systems that has seen fruitful application in distributed systems, concurrent process calculi, and programming languages, since the 1970’s. We have also utilized simulation in our prior work, where we formalized and proved a folklore claim that the state-based and operation-based approaches to […]
Hardware design flows have become increasingly complex as modern chips integrate billions of transistors and rely on aggressive synthesis optimizations to meet performance, area, and power targets. While these transformations improve circuit efficiency, they also erase the correspondence between gate-level netlists and their originating HDL source lines. The loss of traceability makes post-synthesis debugging, timing […]
Modern applications, such as data center workloads, have become increasingly complex. These applications primarily operate on massive datasets, which involve large memory footprints, irregular access patterns, and complex control and data flows. The processor-memory speed gap, combined with these complexities, can lead to unexpected performance inefficiencies in these applications, preventing them from achieving optimal performance. […]
Presenter: Andrew Myers, Cornell University Abstract: Modern civilization depends on complex, interconnected software systems that must safeguard trustworthy or private data. We have ever-growing mountains of code yet lack principled ways to build large systems that are secure. What is missing is a way to securely build these systems compositionally: module by module and layer […]
Transient execution attacks are serious security threats in modern-day processors. Out-of-order execution compels the processor to access data that should not be otherwise perceived. Leakage of that secret information creates a covert channel for the attacker for various types of transient and speculative attacks. Transient based execution attacks emanate when the secret information is leaked […]
The growing integration of Large Language Models (LLMs) into real-world applications has heightened concerns about their trustworthiness, as models may reveal private information, reproduce copyrighted content, propagate biases, or generate harmful instructions. These risks, alongside emerging privacy regulations, motivate the need for LLM unlearning, methods that remove the influence of specific data while preserving overall […]
Not sure if graduate school is right for you? Join us to learn what graduate school is really about and explore whether it’s the right path for you. We’ll cover topics such as qualifying exams, funding options, common misconceptions, and more! Click the link below to register for the event: https://ucsc.zoom.us/webinar/register/WN_31OHhwc7QPqJ7nSyiuAUNg
Presenter: Loris D’Antoni, UC San Diego Abstract: Language models excel at producing fluent text, but in domains like code and math, fluency isn’t enough — outputs must obey strict syntactic and semantic rules. A new wave of research is rethinking decoding itself: not as a process of sampling words, but as a negotiation between probability, structure, and […]