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Adjustment Criteria for Recovering Causal Effects from Missing Data

Design-time Detection of Physical-Unit Changes in Product Lines

Software product lines evolve over time, both as new products are added to the product line and as existing products are updated. This evolution creates unintended as well as planned changes to systems. A persistent problem is that unintended changes are hard to detect. Often they are not discovered until testing or operations. Late discovery is a problem especially in safety-critical, cyberphysical product lines such as avionics, pacemakers, and smart-braking systems, where unintended changes may lead to accidents.

Title: Structure Discovery in Bayesian Networks: Algorithms and Applications

Abstract: Bayesian networks are a class of probabilistic graphical models that have been widely used in various tasks for probabilistic inference and causal modeling. A Bayesian network provides a compact, flexible, and interpretable representation of a joint probability distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure from the data. This is called structure discovery.

Title: PaniniJ: Adding the Capsule Programming Abstraction to Java to Provide Linguistic Support for Modular Reasoning in Concurrent Program Design​
Date/Time: October 7th 2015, 9AM  
Place: Room 223 Atanasoff Hall
Faculty Advisor: Professor Hridesh Rajan

Abstract: