Celebrating the 2024 Nobel Prize in Physics and Chemistry

Friday Dec 6, 2024, 3:00 PM → 7:05 PM Europe/Stockholm

FD5 (AlbaNova Main Building)

The 2024 Nobel Prizes in Physics and Chemistry highlight significant advancements in artificial intelligence (AI) and machine learning (ML) that are reshaping scientific research. Physics laureates John Hopfield and Geoffrey Hinton pioneered methods in associative memory networks and Boltzmann machines, enabling AI to recognize patterns and autonomously extract properties from data. In Chemistry, David Baker, Demis Hassabis, and John Jumper are recognized for their innovative work in computational protein design and structure prediction. Baker's groundbreaking protein designs and the success of AlphaFold2 have revolutionised our understanding of biological processes and the development of new therapies.

The EDUCATE initiative within the OKC invites everyone to celebrate this year's Nobel Prizes and explore the impact of AI and ML across diverse scientific disciplines. Experts from mathematics, biology, climatology, and geology will share insights into current applications, challenges, ethical considerations, and future directions of these technologies.

We encourage students and researchers from the Faculty of Science to join us for an engaging afternoon that fosters interdisciplinary collaboration and inspires future generations of scientists to harness the power of AI in their work.

 

1 Opening Remarks

2 Patrick Bryant

3 Anneli Kruve

4 Golnaz Taheri: From Genes to Algorithms: How AI is Reshaping Life sciences

In this talk, I explore the transformative role of AI and ML in tackling some of the most critical challenges in computational biology. I will discuss how AI/ML, as powerful tools, offer solutions to decode complex biological systems, predict drug interactions, and advance cancer research. Highlighting the synergy between innovative methods and practical applications, this presentation focuses on two key research areas: identifying critical genes and pathways in cancer using deep learning and developing a novel framework for drug-drug interaction prediction using biological networks. By integrating cutting-edge AI/ML techniques with real-world challenges, we aim to contribute to shaping the future of life sciences.

4:25 PM Break and refreshments

5 Justin Alsing

6 Chun-Biu Li

5:30 PM Break and refreshments

7 Panel discussion and closing remarkds

8 Networking session