AI in Science and Engineering: Edith Elkind
Join us on February 5 for Edith Elkind’s lecture in our “AI in Science and Engineering” lecture series!
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TU Wien, Campus Karlsplatz
Festsaal -
1040 Vienna, Karlsplatz 13
Stiege 1, 1. Stock, Raum AA0148 -
This is a hybrid event.
See description for details.
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 Thursday, February 5 for Edith Elkind’s public lecture!
About
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.
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 in TU Wien’s beautiful Festsaal at 1040 Vienna, Karlsplatz 13, 1st floor, room AA0148.
- If you can’t make it person, switch on your laptop and join us via Zoom (Meeting ID: 661 3918 8284, Passcode: serwyN6e).
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.
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