Data-Driven Optimization for User Interaction in the Mobile Era: Information Access, Security and Beyond

Thursday, February 21, 2019 - 4:00pm

As mobile has become a big part of our everyday life, it is important to investigate how to optimize users’ mobile interaction experience. In this talk, I will describe my research on automatically optimizing mobile user interactions with data-driven approaches. The talk focuses on my two approaches to address the deficiency in existing interactions: the first approach optimizes user interaction with faceted browsing by leveraging search log data, the second approach optimizes user interaction with security permission requests by leveraging mobile-appstore data.

The Impact of Social and Cultural Contexts on Users’ Privacy Management

Wednesday, February 27, 2019 - 4:00pm

Context plays a critical role in shaping users’ privacy attitudes and behaviors. Previous privacy studies mostly focus on physical and technical contexts, such environmental cues and data collection methods. The impact of social and cultural contexts has not been sufficiently addressed.

Colloquium - Lightweight Requirements Engineering for Exascale Co-design

Wednesday, March 6, 2019 - 4:00pm

Given the tremendous cost of an exascale HPC system, its architecture must match the requirements of the applications it is supposed to run as precisely as possible. Conversely, applications must be designed such that building an appropriate system becomes feasible, motivating the idea of co-design. In this process, a fundamental aspect of the application requirements are the rates at which the demands for different resources grow as a code is scaled to a larger machine. However, if the anticipated scale exceeds the size of available platforms this demand can no longer be measured.

What Needs to be Added to Machine Learning?

Friday, March 8, 2019 - 4:00pm

Supervised learning is a cognitive phenomenon which has proved amenable both to theoretical analysis as well as exploitation as a technology. However, not all of cognition can be accounted for directly by supervised learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the major component of cognition that needs to be added.

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