Formal verification methods are making headway into required safety verification policies for transportation. However, they often fail to account for the fact that controllers, whether digital or human, have imperfect knowledge of the world. Without addressing this shortcoming, we risk never bridging the gap between theoretical and practical safety.

In this talk, we discuss the sometimes fatal consequences of these shortcomings, and describe a new logic with belief as a first-class citizen, allowing us to model, think and verify belief-aware cyber physical systems.

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Wearable cognitive assistance applications can provide guidance for many facets of a user's daily life. In this presentation, I will talk about my recent work that enables a new genre of such applications that require both heavy computation and very low response time on inputs from mobile devices. The core of this work is the design, implementation, and evaluation of Gabriel, an application platform that simplifies the creation of and experimentation with this new genre of applications.

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Monday, November 13, 2017 - by

The School of Computer Science's Ph.D. women are hard at work bringing new and exciting opportunities to Carnegie Mellon's Women @ SCS program. Directed by Carol Frieze, Women @ SCS creates and supports academic, social and professional opportunities for women in computer science. The program includes a wide range of women including undergraduate, master's and Ph.D. students — as well as faculty.

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Monday, November 13, 2017 - by

Carnegie Mellon University’s Libratus artificial intelligence, which scored an historic victory over four human poker pros earlier this year, has won the HPCwire Reader’s Choice Award for Best Use of AI.

The award from the supercomputing trade publication was announced at the 2017 International Conference for High Performance Computing, Networking, Storage and Analysis (SC17) in Denver, Colo.

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Friday, November 10, 2017 - by

Priya Donti, a doctoral candidate co-advised by Zico Kolter and Inês Azevedo at Carnegie Mellon University, has been awarded a Department of Energy Computational Science Graduate Fellowship (DOE CSGF) to support her Computer Science and Energy Policy research.

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My dissertation will demonstrate some of the limitations of existing approaches to automated curriculum design and propose a new approach that leverages learner-generated resources to create new educational content.

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Time-series data is now everywhere—IoT, user event streams, system monitoring, finance, adtech, industrial control, transportation, and logistics—and increasingly used to power core applications. It also creates a number of technical challenges: to ingest high volumes of structured data; to ask complex, performant queries for both recent and historical time intervals; to perform specialized time-centric analysis and data management.

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Hybrid systems combine discrete and continuous dynamics, which makes them attractive as models for systems that combine computer control with physical motion. However, verification of hybrid systems is undecidable in general and challenging for many systems of practical interest. Even when verification results are obtainable, verification only provides from guarantees when reality matches the verified model. Conversely, reinforcement learning-based controllers are lauded for their flexibility in unmodeled environments, but also do not typically provide guarantees of safe operation.

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The success of cloud computing stems from the ability to take extremely large pools of physical servers, connect them by a massive shared network, and then carve up those resources into separate virtual networks assigned to different tenants. For years, the ability to give each tenant an isolated virtual environment, configured exactly the way they want it, has depended on Software Defined Networking (SDN). In turn, Software Defined Networking has depended on software-based virtual switches running on the servers to modify each tenants’ packets in ways that create the isolation.

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