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 […]
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. […]
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 […]