Invited Speakers

You will find our list of invited speakers with their bios on this page. Talk titles and abstracts coming soon.

All speakers listed on this page have been confirmed.

Prof. Moa Johansson (Chalmers University)

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.

Prof. Daniel Neider (TU Dortmund)

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.

Prof. Swarat Chaudhuri (UT Austin)

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.

Prof. Yasser Shoukry (UC Irvine)

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.

Prof. Yizheng Chen (University of Maryland, College Park)

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.

Prof. Alessio Lomuscio (Imperial College London)

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.

Dr. Lucas García (MathWorks)

Lucas García is a principal product manager for deep learning at MathWorks with more than 15 years of machine learning experience and research in the computer software industry. He works with customer-facing and development teams to define, develop, and launch new capabilities and applications that meet customer needs and market trends in deep learning. Lucas joined MathWorks in 2008 as a customer-facing engineer and has worked with engineers and scientists across industries to help them tackle real-world problems in AI. Lucas is also an active member of the EUROCAE WG-114/SAE G-34 working group, where he helps craft the ARP6983 process standard for developing Aircraft Systems leveraging AI capabilities. Before joining MathWorks, he worked as a software developer in finance. Lucas holds a Ph.D. in applied mathematics from the Complutense University of Madrid and Polytechnic University of Madrid. His research is focused on how neural networks can be used to solve combinatorial optimization problems.