Principal Scientist (CMU CyLab), Technical Professional Leader -- Data Science (NASA Ames/KBR). [CyLab page] [] []

Corina Pasareanu is an ACM Distinguished Scientist, working at NASA Ames. She is affiliated with KBR and Carnegie Mellon University's CyLab. Her research interests include model checking, symbolic execution, compositional verification, probabilistic software analysis, autonomy, and security. She is the recipient of several awards, including ETAPS Test of Time Award (2021), ASE Most Influential Paper Award (2018), ESEC/FSE Test of Time Award (2018), ISSTA Retrospective Impact Paper Award (2018), ACM Impact Paper Award (2010), and ICSE 2010 Most Influential Paper Award (2010). She has been serving as Program/General Chair for several conferences including: ICSE 2025, SEFM 2021, FM 2021, ICST 2020, ISSTA 2020, ESEC/FSE 2018, CAV 2015, ISSTA 2014, ASE 2011, and NFM 2009. She is on the steering committees for the ICSE, TACAS and ISSTA conferences. She is currently an associate editor for IEEE TSE and for STTT, Springer Nature.

Enabling One-Line Rust Verification with Program Synthesis (AWS, Co-PI), Machine Learning for JavaScript Vulnerability Detection ( DTI, PI), Verifiable Personalization for Federated Learning (CyLab, PI), HUGS: Human-Guided Software Testing and Analysis for Scalable Bug Detection and Repair (NSF, PI), Safety of shared control in autonomous driving (Safe-SCAD) (Assuring Autonomy International Programme, Co-PI), Provably Robust Deep Learning (DARPA GARD, co-PI), Mera: Memoized Ranged Systematic Software Analyses (NSF, Co-PI), ISSTAC: Integrated Symbolic Execution for Space-Time Analysis of Code (DARPA, Co-PI), Symbolic PathFinder: Symbolic Execution for Java Bytecode (NASA, Main developer).
I serve on the CAV Award Committee. Please send your nominations!
I will give lectures at the Marktoberdorf Summer School 2023. Please apply!
I am a Faculty Host for the Carnegie Bosch Fellowship Program. Please apply.

PhD Students and PostDocs:
Some Recent Papers: See also [Google Scholar] [DBLP].
  • Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study, Corina S. Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, Huafeng Yu, in CAV 2023, preprint.
  • Feature-Guided Analysis of Neural Networks, Divya Gopinath, Luca Lungeanu, Ravi Mangal, Corina S. Pasareanu, Siqi Xie, Huanfeng Yu, in FASE 2023.
  • On the Perils of Cascading Robust Classifiers, Ravi Mangal, Zifan Wang, Chi Zhang, Klas Leino, Corina Pasareanu, Matt Fredrikson, in ICLR 2023.
  • Toward Certified Robustness Against Real-World Distribution Shifts, Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, Hamed Hassani, George J. Pappas, Corina Pasareanu, Clark Barrett, in SatML 2023.
  • Degradation Attacks on Certifiably Robust Neural Networks, Klas Leino, Chi Zhang, Ravi Mangal, Matt Fredrikson, Bryan Parno, Corina Pasareanu, in TMLR 2022.
  • Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components, Radu Calinescu, Calum Imrie, Ravi Mangal, Corina S. Pasareanu, Misael Alpizar Santana, Gricel Vzquez, preprint 2022.
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