AI increasingly augments research practices, redefines scientific workflows, and evolves teaching and training—requiring new skills, ethical frameworks, and collaborative infrastructures across disciplines.

Join us on Wednesday, November 25 for Matthias Scheutz’s second lecture in our Public Lecture Series AI in Science and Engineering.

About

Matthias Scheutz is the Karol Family Applied Technology Professor of computer science and mechanical engineering in the Department of Computer Science at Tufts University in the School of Engineering, the Director of the Human-AI Interaction Center at the Tufts Institute of Artificial Intelligence, and the Director of the Human-Robot Interaction (HRI) Laboratory. He is an AAAI and AAAS Fellow with close to 500 peer-reviewed publications in artificial intelligence, artificial life, agent-based computing, natural language understanding, cognitive modeling, robotics, human-robot interaction, and foundations of AI and cognitive science. His current research focuses on complex, AI-enabled robots with natural language interaction, problem-solving, and fast instruction-based learning capabilities in open worlds.

Hybrid Event

Whether you’re a student or simply interested in how Artificial Intelligence is changing science and engineering practices–this lecture series is open to everyone:

• We look forward to meeting you in person at TU Wien’s TUtheFutureRooftop conference room at Getreidemarkt 9, 5th floor, room A03, in 1060 Vienna.
• If you can’t make it person, switch on your laptop and join us via Zoom (Meeting ID: 611 6663 4707, Passcode: DfBLeG4V).

Public Lecture Series AI in Science and Engineering

Our public lecture series AI in Science and Engineering explores how AI is transforming these disciplines. 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.

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.

Photographs and/or video will be taken at this event. By attending, you grant TU Wien Informatics full rights to use the material (and any reproductions or adaptations) for fundraising, publicity, or other purposes. This may include (but is not limited to) the right to use in our print and online publicity, social media, press releases, and funding applications. If you wish that no photographs explicitly depicting you are used for these purposes, please send us an informal message. — Thank you!

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