Invited Speakers

Moa Johansson is an Associate Professor at Chalmers University in Gothenburg, Sweden. Her research interests include a diverse mix of applications of AI: from math and automated reasoning to sports science and natural language processing for political science. She is also the Co-Director of Chalmers Area of Advance in Information and Communication Technology. In the past, she’s been a postdoc at the University of Verona in Italy. She did her PhD in the Mathematical Reasoning Group at the University of Edinburgh.

Daniel Neider is the professor of “Verification and Formal Guarantees of Machine Learning” at TU Dortmund, Germany, and the Center for Trustworthy Data Science and Security. Prior to his current position, Daniel Neider led the “Safety and Explainability of Learning Systems” group at the Carl von Ossietzky University of Oldenburg, also located in Germany. He received his Ph.D. in Computer Science from RWTH Aachen University (Germany) in 2014, where he developed learning-based methods for software verification. Subsequently, he spent two years as a Postdoc at the University of Illinois at Urbana-Champaign (USA) and the University of California, Los Angeles (USA). Before joining the University of Oldenburg, Daniel headed the “Logic and Learning” group at the Max Planck Institute for Software Systems in Kaiserslautern (Germany). His research interests lie at the intersection of formal methods and machine learning, focusing specifically on the safety and reliability of learning systems.

Swarat Chaudhuri is a Professor of Computer Science and the director of the Trishul laboratory at UT Austin. His research lies at the interface of programming languages, formal methods, and machine learning. His aim as a researcher is to develop a new class of intelligent systems that are reliable, transparent, and secure by construction and can solve reasoning-intensive tasks beyond the scope of contemporary AI. Prof. Chaudhuri received a bachelor's degree in computer science from the Indian Institute of Technology, Kharagpur, in 2001, and a doctoral degree in computer science from the University of Pennsylvania in 2007. He has received the NSF CAREER award, the ACM SIGPLAN John Reynolds Dissertation award, the Morris and Dorothy Rubinoff Dissertation award from the University of Pennsylvania, Meta and Google Research awards, and several ACM SIGPLAN and SIGSOFT distinguished paper awards. He serves on the editorial boards of ACM Transactions on Programming Languages and Systems and Transactions on Machine Learning Research. He has served as a Program Chair for CAV 2016 and ICLR 2024.

Yasser Shoukry is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine where he directs the Resilient Cyber-Physical Systems Lab. His research goal is to develop algorithms and tools to reason about the resilience, security, and privacy of Artificial Intelligence (AI) controlled Cyber-Physical Systems and Internet-of-Things (IoT), in general, and robotic systems, in particular, providing a scientific basis to understand their fundamental properties and guide their design. His work spans both theoretical and experimental aspects of CPS and draws on tools from formal methods, embedded systems, control theory, and machine learning.

Yizheng Chen is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a member of MC2 (the Maryland Cybersecurity Center) and UMIACS. She works in the intersection of AI and security. She is interested in using AI to solve security problems, such as detecting malware, vulnerability, attacks and fraud. She is also interested in improving the robustness of these machine-learning models. Previously, she was a postdoctoral scholar at University of California, Berkeley and Columbia University. She holds a Ph.D. in Computer Science from Georgia Institute of Technology. Her work has received an ACM CCS Best Paper Award Runner-up and a Google ASPIRE Award. She is a recipient of the Anita Borg Memorial Scholarship.

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, where he leads the Verification of Autonomous Systems Lab. There, he develops methods and tools for the verification of AI systems so that they can be deployed safely and securely in applications of societal importance. At present they are contributing to the following research areas: i) verification of neural systems and autonomous systems realized via machine-learning, ii) explainability and Fairness in AI systems, iii) parameterized verification of robotic swarms, and iv) logic-based verification of multi-agent systems.

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in AI and deep learning, blockchain/web3, security and privacy. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, and several Test-of-Time and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an ACM Fellow and an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.