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Campus E1 1
66123 Saarbrücken (Germany)

Short Bio

Prof. Bernd Finkbeiner, Ph.D. is a faculty member at the CISPA Helmholtz Center for Information Security and a professor for computer science at Saarland University. He obtained his Ph.D. in 2003 from Stanford University. Since 2003, he leads the Reactive Systems Group, which became part of CISPA in 2020. His research focus is the development of reliable guarantees for the safety and security of computer systems, including specification, program synthesis and repair, and static and dynamic verification. 
 

CV: Last four stations

2020 - now
Faculty member at CISPA
2006 - now
Professor at Saarland University
2003 - 2006
Junior professor at Saarland University
1996 - 2002
Research assistant at Stanford University

Publications by Bernd Finkbeiner

Year 2019

Article

Formal Methods Syst. Des.

Conference / Medium

CSF
IEEE Computer Security Foundations Symposium, CSF

Conference / Medium

ATVA
Automated Technology for Verification and Analysis - 17th International Symposium, ATVA 2019, Taipei, Taiwan, October 28-31, 2019, Proceedings

Conference / Medium

CAV
Computer Aided Verification - 31th International Conference, CAV

Conference / Medium

CAV
Computer Aided Verification - 31th International Conference, CAV

Conference / Medium

ATVA
Automated Technology for Verification and Analysis - 17th International Symposium, ATVA 2019, Taipei, Taiwan, October 28-31, 2019, Proceedings

Conference / Medium

CAV
Computer Aided Verification - 31th International Conference, CAV

Conference / Medium

LICS
IEEE Symposium on Logic in Computer Science, LICS

Conference / Medium

ATVA
Automated Technology for Verification and Analysis - 17th International Symposium, ATVA 2019, Taipei, Taiwan, October 28-31, 2019, Proceedings

Conference / Medium

CONCUR
30th International Conference on Concurrency Theory, CONCUR 2019, August 27-30, 2019, Amsterdam, the Netherlands

Teaching by Bernd Finkbeiner

2020

Neural-Symbolic Computing

In this seminar, we will explore new research that shows that deep neural networks are, in fact, able to reason on “symbolic systems”, i.e., systems that are built with symbols like programming languages or formal logics.

2019/20

Verification

This course takes an up-to-date look at the theory and practice of program verification.

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