Talk Title

AI for the Sciences

Abstract

In recent years, machine learning (ML) and artificial intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. In his talk, Müller will touch upon the topic of ML applications in the sciences, in particular Chemistry and Physics. He will also discuss possibilities for extracting information from machine learning models with explainable AI to gain genuinely novel insights in molecular dynamics and quantum properties of matter. Finally, Müller will briefly discuss perspectives and limitations.

Short Bio

Klaus-Robert Müller has been a professor of computer science at Technische University Berlin since 2006; at the same time he is directing rsp. co-directing the Berlin Machine Learning Center and the Berlin Big Data Center and most recently BIFOLD . He studied physics in Karlsruhe from1984 to 1989 and obtained his Ph.D. degree in computer science at Technische Universität Karlsruhe in 1992. After completing a postdoctoral position at GMD FIRST in Berlin, he was a research fellow at the University of Tokyo from 1994 to 1995. In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST (later Fraunhofer FIRST) and directed it until 2008. From 1999 to 2006, he was a professor at the University of Potsdam. From 2012 he has been Distinguished Professor at Korea University in Seoul. In 2020/2021 he spent his sabbatical at Google Brain as a Principal Scientist. Among others, he was awarded the Olympus Prize for Pattern Recognition (1999), the SEL Alcatel Communication Award (2006), the Science Prize of Berlin by the Governing Mayor of Berlin (2014), the Vodafone Innovations Award (2017), Hector Science Award (2024), Pattern Recognition Best Paper award (2020), Digital Signal Processing Best Paper award (2022). In 2012, he was elected member of the German National Academy of Sciences-Leopoldina, in 2017 of the Berlin Brandenburg Academy of Sciences, in 2021 of the German National Academy of Science and Engineering and also in 2017 external scientific member of the Max Planck Society. From 2019 on he became an ISI Highly Cited researcher in the cross-disciplinary area. His research interests are intelligent data analysis and Machine Learning in the sciences (Neuroscience (specifically Brain-Computer Interfaces, Physics, Chemistry) and in industry.

Talk Title

From Segmentation to Molecular Property Prediction

Abstract

This talk will explain how my early work on computer vision on segmentation inspired me to get in to drug discovery. In my early work I was interested in going beyond bounding box recognition to recover the segmentation and 3 dimensional shape of objects. In this time I worked in the interesting field of graph theory, and in particular message passing algorithms. Then came the deep learning revolution which I joined early due to the influence of Andrew Zisserman and developed early neural network methods for recognizing and segmenting objects. This journey led me to see if other fields could be disrupted with neural networks, and naturally led me to looking at whether the same graph algorithms could be applied to molecular graphs. In this talk I will discuss this journey and out state of the art results on molecular prediction.

Short Bio

Professor Philip Torr did his PhD (DPhil) at the Robotics Research Group of the University of Oxford under Professor David Murray of the Active Vision Group. He worked for another three years at Oxford as a research fellow, and still maintains close contact as visiting fellow there. He left Oxford to work for six years as a research scientist for Microsoft Research, first in Redmond, USA, in the Vision Technology Group, then in Cambridge founding the vision side of the Machine Learning and Perception Group. He then became a Professor in Computer Vision and Machine Learning at Oxford Brookes University. In 2013, Philip returned to Oxford as full professor where he has established the Torr Vision group. He won several awards including the Marr prize (the highest honour in vision) in 1998. He is a Royal Society Wolfson Research Merit Award Holder. Recently, together with members of his group, he has won several other awards including an honorary mention at the NIPS 2007 conference for the paper 'P. Kumar, V. Kolmorgorov, and P.H.S. Torr, An Analysis of Convex Relaxations for MAP Estimation', in NIPS 21, Neural Information Processing Conference, and (oral) Best Paper at Conference for 'O. Woodford, P.H.S. Torr, I. Reid, and A.W. Fitzgibbon, Global Stereo Reconstruction under Second Order Smoothness Priors', in Proceedings IEEE Conference of Computer Vision and Pattern Recognition, 2008 . More recently he has been awarded best science paper at BMVC 2010 and ECCV 2010. He was involved in the algorithm design for Boujou released by 2D3. Boujou has won a clutch of industry awards, including Computer Graphics World Innovation Award, IABM Peter Wayne Award, and CATS Award for Innovation, and a technical EMMY. He then worked closely with this Oxford based company as well as other companies such as Sony on the Wonderbook project. He has been involved in numerous spin-outs as founder or advisor including: FiveAI, Onfido, Oxsight, Eigent.

Talk Title

AI for Physics, Physics for AI

Abstract

Artificial Intelligence (AI) is revolutionizing many aspects of our life, and its connections to physics have been long established. However, AI success stories in physics and astronomy are rare and limited to a few subfields only. I will argue that this is because many physics applications require development of physics specific AI methods. A few examples of physics specific nature of the data are large dimensionality of the data, stochastic nature of the data, and physics symmetries. I will discuss new methods that attempt to address these issues, and show that when applied to cosmology data these methods lead to significant improvements relative to traditional methods. Physics ideas have also influenced the development of AI, as witnessed by the 2024 Nobel Prize in Physics. I will discuss an example of such connections using the recently developed MicroCanonical Hamiltonian and Langevin Monte Carlo, which are a new class of sampling methods that outperform previous state of the art methods. These new sampling methods in turn enable new approaches that were not possible before, in a wide range of fields from physics to AI.

Short Bio

Uros Seljak is an American-Slovenian cosmologist and a professor of astronomy and physics at University of California Berkeley and Lawrence Berkeley National Laboratory. Seljak is a cosmologist who is particularly well-known for his research on cosmic microwave background radiation, galaxy clustering and weak gravitational lensing, and the implications of these observations for the large scale structure of the universe. In 1997, Seljak predicted the existence of B-modes in CMB polarization that are a tracer of primordial gravitational waves from inflation. Seljak is actively developing methods for Bayesian Inference and Artificial Intelligence, and applying them to physics, astronomy, and other sciences. He is a recipient of Warner Prize, Gruber Prize, a Packard Fellow and a Sloan Fellow. He is a member of American National Academy of Science and of American Academy of Arts and Sciences.