Colloquium

The proposed BS in Data Science degree

During Spring and Summer 2019, I led a small team to specify a BS in Data Science degree. This curriculum has been submitted to CASCC, along with the proposals for 6 new courses.

I will describe the due diligence that was performed prior to developing the curriculum and give an overview of the curriculum and the new courses.

There will be plenty of time at the end for questions/discussion.

Faculty Research Topics

This week, we host an introduction to faculty research topics. We encourage all faculty members and MS/PhD students to attend and hope these presentations will help students meet faculty members and get exposed to the full range of the research portfolio in our department. We feature the following presentations this week:

2019 Oregon Cyber Security Day

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.

Celebrating Undergraduate Success

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!

Computational Models of Learning: Leveraging Human Learning Data to Build Better Machine Learning Models

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.

Building Intelligent Seeing Machines

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).

Understanding and Improving Deep Neural Networks

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.

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