Audience: Faculty
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CSE Colloquium: Making Systems Secure with Information Flow
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…
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Chen, Q. (CSE) – New Approximation and Online Algorithms using Novel Combinatorial Structures
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…
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Petety, A. (CSE) – New Algorithmic Methods for Uncertain Inputs
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…
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Macroeconomics & International Finance Seminar Series Presents: Helen Popper
Macroeconomics and International Finance Seminar: Helen Popper
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Applied Microeconomics and Trade Seminar Series presents: Matt Weinberg
Applied Microeconomics and Trade Seminar: Matt Weinberg
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Behavioral, Econometrics and Theory Seminar Series Presents: Jacopo Magnani
Economics Behavioral, Econometrics and Theory Seminar: Jacopo Magnani
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Macroeconomics & International Finance Seminar Series Presents: Yuriy Gorodnichenko
Macroeconomics and International Finance Seminar: Yuriy Gorodnichenko
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CSE Colloquium – Neurosymbolic AI: from research to industry
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.…
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CSE Colloquium – Flux: Refinement Types for Verified Rust Systems
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…