Public Lecture Series: AI in Science and Engineering
Join our lecture series on “AI in Science and Engineering”, and learn how AI is (re)shaping these disciplines!
About
AI increasingly augments research practices, redefines scientific workflows, and changes teaching and training—requiring new skills, ethical frameworks, and collaborative infrastructures across disciplines.
Engaging with these changes is essential: AI already permeates daily life and has fundamentally altered how science and engineering are practiced, from data collection and analysis to experiment design, simulation, and validation. Understanding AI’s capabilities and limits helps researchers build systems that are safe, robust, and equitable. It also informs how we teach and train the next generation—integrating responsible innovation and interdisciplinary collaboration so that the scientific and engineering communities can advance AI that serves people and strengthens the foundations of science and engineering.
Our public lecture series AI in Science and Engineering explores how AI is transforming these disciplines and features lectures by the internationally renowned scientists Edith Elkind, Kate Smith-Miles, Matthias Scheutz, Shahar Mendelson, and Kristian Kersting. Bringing together computer science, mathematics, engineering, and science in general, the series highlights how AI is reshaping discovery, design, and deployment in science and engineering.
All lectures take place in TU Wien’s beautiful Festsaal, and are also streamed via Zoom. For details, please see individual lecture announcements below.
Upcoming Lectures
February 5, 2026: Edith Elkind
Northwestern University
Edith Elkind is a Ginny Rometty Professor of Computer Science at Northwestern University. She obtained her PhD from Princeton in 2005 and worked in Israel, Singapore, and the UK before joining Northwestern in 2024. She works in algorithmic game theory, focusing on algorithms for collective decision-making. She is a recipient of the SIGAI Autonomous Agents Research Award and a Fellow of EurAI. She served as a chair of multiple leading conferences in AI and algorithmic game theory (including IJCAI, ACM EC, AAMAS, WINE, and COMSOC), and serves as an editor-in-chief of Journal of AI Research.
March 16, 2026: Kate Smith-Miles
University of Melbourne
Kate Smith-Miles is Pro Vice-Chancellor (Research Capability) and a Melbourne Laureate Professor in the School of Mathematics and Statistics at The University of Melbourne. She is also Director of the ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). Prior to joining The University of Melbourne in 2017, she was Professor of Applied Mathematics at Monash University and inaugural Director of the Monash Academy for Cross & Interdisciplinary Mathematical Applications (MAXIMA). She was also previously Head of the School of Engineering and Information Technology at Deakin University with a Chair in Engineering. She obtained her first Professorship in Information Technology at Monash University, where she worked from 1996-2006. Professorships in three disciplines (mathematics, engineering, and information technology) have given her an interdisciplinary breadth reflected in much of her research. She has published 2 books on neural networks and data mining, and around 300 refereed journal and international conference papers in the areas of neural networks, optimisation, machine learning, and various applied mathematics topics.
March 19, 2026: Matthias Scheutz
Tufts University
Matthias Scheutz, Matthias Scheutz received a PhD in philosophy from the University of Vienna and a joint PhD in cognitive science and computer science from Indiana University Bloomington. He is the Karol Family Applied Technology Professor of computer and cognitive science in the Department of Computer Science at Tufts University in the School of Engineering and Director of the Human-Robot Interaction Laboratory. He is an AAAI and AAAS Fellow with over 400 peer-reviewed publications in artificial intelligence, agent-based computing, natural language understanding, cognitive modeling, robotics, and human-robot interaction. His current research focuses on complex ethical cognitive robots with natural language interaction, problem-solving, and instruction-based learning capabilities in open worlds.
March 24, 2026: Shahar Mendelson
Texas A&M University
Shahar Mendelson’s research focuses on the mathematics of Data Science, including the connections between Statistical Learning Theory, Empirical Process Theory, and Asymptotic Geometric Analysis. His works have been published extensively in top venues such as the Conference on Learning Theory, Annals of Probability, and Annals of Statistics. He has also received a number of awards including the Medal of the Australian Mathematical Society and the Technion Taub Prize for research excellence.
Date TBA: Kristian Kersting
Technical University of Darmstadt
Kristian Kersting is a Professor at the Department of Computer Science at the Technical University of Darmstadt, Germany. He is the head of the Artificial Intelligence and Machine Learning (AIML) lab, a member of the Centre for Cognitive Science, a faculty of the ELLIS Unit Darmstadt, and the founding co-director of the Hessian Center for Artificial Intelligence. After receiving his PhD from the University of Freiburg in 2006, he was with the MIT, Fraunhofer IAIS, the University of Bonn, and the TU Dortmund University. His main research interests are statistical relational AI, neuo-symbolic AI, as well as deep (probabilistic) programming and learning. Kristian has published over 200 peer-reviewed technical papers, co-authored a Springer book on Statistical Relational AI, and co-edited a MIT Press book on Probabilistic Lifted Inference.
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