CODE Colloquium: Trustworthy Machine Learning and the Security Mindset

18. 05. 2022 | 18.00 Uhr - 19.30 Uhr

In regelmäßigen Abständen laden wir in Kooperation mit ITIS e.V. hochkarätige Redner für 45-minütige Vorträge zu ausgewählten Themen der IT- und Cyber-Sicherheit an das Forschungsinstitut CODE ein. Im Anschluss an einen Vortrag folgt eine Questions- and Answers-Runde. Abgerundet wird das Kolloquium durch ein kleines Get-Together.

Diesmal dürfen wir folgenden Redner bei uns begrüßen:

Prof. Dr. Somesh Jha, University of Wisconsin Madison, USA
Title: Trustworthy Machine Learning and the Security Mindset

Der Einlass erfolgt ab 17:30 Uhr über den Haupteingang des FI CODE, Carl-Wery-Straße 18, 81739 München. Wir bitten um Anmeldung per Mail an

Die Veranstaltung findet auf Englisch statt.


Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks (DNNs), are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, healthcare, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences. Interest in this area of research has simply exploded. In this work, we will emphasize the need for a security mindset in trustworthy machine learning, and then cover some lessons learned.


Somesh Jha received his B.Tech from Indian Institute of Technology, New Delhi in Electrical Engineering. He received his Ph.D. in Computer Science from Carnegie Mellon University under the supervision of Prof. Edmund Clarke (a Turing award winner). Currently, Somesh Jha is the Lubar Professor in the Computer Sciences Department at the University of Wisconsin (Madison). His work focuses on analysis of security protocols, survivability analysis, intrusion detection, formal methods for security, and analyzing malicious code. Recently, he has focused his interest on privacy and adversarial ML (AML). Somesh Jha has published several articles in highly refereed conferences and prominent journals. He has won numerous best-paper and distinguished-paper awards. Prof. Jha is the fellow of the AAAS, ACM and IEEE.

Forschungsinstitut CODE
Forschungsinstitut CODE, Carl-Wery-Straße 18, 81739 München, Raum 0812 (EG, rechts neben dem Eingang)
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