This event will feature a slate of distinguished speakers from academia and industry, discussing current challenges and future opportunities in cyber security. With the recent receipt of a generous gift from Ripple, this edition of Cyber Security Day will focus on the pros and cons of Blockchain technology. There will be plenty of opportunities to engage with the nationally-renowned distinguished speakers, faculty, scientists, lead engineers, students, and other attendees from Oregon and beyond on cyber security and privacy, in general, and Blockchain technology, in particular.
Please join us for a reception to recognize the achievements of Computer Science and MACS undergraduates
Enjoy cake and refreshments as we wind down the academic year.
Students including CIS scholarship recipients, programming contest winners, student group leaders, and others will be recognized.
Everyone is welcome! We look forward to seeing you there!
Researchers have made substantial progress towards developing Artificial Intelligence (AI) systems capable of a human-level performance at a narrow range of tasks. However, there do not yet exist machines capable of the kinds of flexible and efficient learning that humans exhibit. To overcome this gap, I present research exploring how data from intelligent tutoring systems can be leveraged to reverse engineer human capabilities and build more human-like models of learning.
The research of computer vision was motivated by a dream of making an intelligent machine that is able to see like our human beings: to automatically analyze and understand massive visual inputs. With the explosive growing of computing power, this dream evolves to many exciting emerging applications, such as intelligent robots, autonomous vehicles, intelligent video surveillances, computer-aided doctors, etc. A core component in these applications is visual recognition (including object classification, detection and localization).
Through deep learning, deep neural networks have produced state-of-the-art results in a number of different areas of machine learning, including computer vision, natural language processing, robotics and reinforcement learning. I will summarize three projects on better understanding deep neural networks and improving their performance. First I will describe our sustained effort to study how much deep neural networks know about the images they classify.
Interactive Learning (IL) is a prominent machine learning paradigm that explores how intelligent autonomous systems learn to make improved sequential decisions through multiple rounds of interactions with the real world. IL-based systems have a wide range of applications including robotics, health-care, and marketing. However, for these systems to meet the practical requirements of the related application domains, there is a perennial need for developing efficient algorithms.
The state of network security today is quite abysmal. Security breaches and downtime of critical infrastructures continue to be the norm rather than the exception, despite the dramatic rise in spending on network security. Attackers today can easily leverage a distributed and programmable infrastructure of compromised machines (or botnets) to launch large-scale and sophisticated attacks. In contrast, the defenders of our critical infrastructures are crippled as they rely on fixed capacity, inflexible, and expensive hardware appliances.
Double spending is a fundamental problem which should be addressed in all cryptocurrency systems, including Bitcoin. While Bitcoin is equipped with inherent security mechanisms to thwart double spending, it requires transactions to be confirmed in a very slow speed, which is impractical in many applications. In this talk, I will discuss the existing solutions and propose new solutions to address the double spending problem for fast transactions in Bitcoin. In particular, I will focus on potential attacks, pitfalls, and defending mechanisms in addressing the double spending problem.