Keynote Speaker.

 

SafeDNN: Understanding and Verifying Neural Networks

The SafeDNN project at NASA Ames explores new techniques and tools to ensure that systems that use Deep Neural Networks (DNN) are safe, robust and interpretable. Research directions we are pursuing in this project include: symbolic execution for DNN analysis, label-guided clustering to automatically identify input regions that are robust, parallel and compositional approaches to improve formal SMT-based verification, property inference and automated program repair for DNNs, adversarial training and detection, probabilistic reasoning for DNNs. In this talk I will highlight some of the research advances from SafeDNN.

NASA Ames and Carnegie Mellon University, United States

Corina Pasareanu is a distinguished researcher at NASA Ames and Carnegie Mellon University. She is affiliated with CMU’s CyLab and holds a courtesy appointment in Electrical and Computer Engineering. At NASA Ames, Corina is developing and extending Symbolic PathFinder, a symbolic execution tool for Java bytecode. Her research interests include model checking and automated testing, compositional verification, model-based development, probabilistic software analysis, and autonomy and security.

 

She is the recipient of several awards, including ASE Most Influential Paper Award (2018), ESEC/FSE Test of Time Award (2018), ISSTA Retrospective Impact Paper Award (2018), ACM Distinguished Scientist (2016), ACM Impact Paper Award (2010), and ICSE 2010 Most Influential Paper Award (2010).

Corina has been serving as Program/ General Chair for several conferences including: ICST 2020, ISSTA 2020, ESEC/FSE 2018, CAV 2015, ISSTA 2014, ASE 2011, and NFM 2009. She is currently an associate editor for the IEEE TSE journal.