Abstract:
Artificial intelligence is transforming biology not simply by providing better analytical tools, but by reshaping the scientific process itself. This talk argues that AI is creating new biological representations, becoming a natural interface to scientific knowledge, and enabling closed experimental discovery loops that connect hypotheses, experiments and evidence. Drawing on examples from protein structure prediction, phenotyping, AI assistants and autonomous experimentation, the talk explores how AI is changing the way science is conducted. It concludes by arguing that future scientific leadership will depend not only on powerful AI models, but on building trusted infrastructures that connect data, experiments, people and governance into AI-native scientific ecosystems.
Short Bio:
Prof. Sotirios Tsaftaris obtained his PhD and MSc degrees in Electrical Engineering and Computer Science (EECS) from Northwestern University, USA in 2006 and 2003 respectively. He obtained his Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece.
Currently, he is Chair in Machine Learning and Computer Vision at the University of Edinburgh (UK) and Royal Academy of Engineering Research Chair. He is also a Turing Fellow with the Alan Turing Institute (London, UK) and a Fellow of ELLIS, the European Laboratory for Learning and Intelligent Systems. He has authored more than 180 journal and conference papers particularly in interdisciplinary fields and his work is (or has been) supported by the National Institutes of Health (USA), RAEng, EPSRC & BBSRC (UK), the European Union, and several non-profits and industrial partners. His research interests lie in artificial intelligence, computer vision, and image analysis.
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Meeting ID: 317 515 439 453 312
