Artificial Intelligence for Mathematical Discovery

Join via the online stream (also to view the archived talk).

Abstract:

Progress in mathematics often involves observing a large number of well distributed examples, postulating conjectures and rigorously proving them. In a physicist’s language, this is a “top-down” approach and many spectacular results in both these disciplines have been discovered through this process. Artificially intelligent machines are now able to both support and in limited instances outperform conventional mathematics. In this talk, I will give an overview of this burgeoning field and our ongoing research by focusing on two case studies. I will present our AI guided conjecture generation framework which has been used to discover new results in multiple domains of math. I will also show how techniques such as supervised learning and symbolic regression can be utilized for this purpose in the context of string geometry.

Bio:

Daattavya’s research interests are at the intersection of mathematical physics and machine intelligence. His main focus is on developing tools for the discovery of new mathematics and analyzing their structure. Other ongoing work includes studying interesting geometries that arise in string theory and mathematical physics, often through the application of machine learning techniques. Before joining Cambridge, Daattavya graduated with an MSc. in Mathematical and Theoretical Physics from the University of Oxford where his dissertation was on Calabi-Yau Manifolds and Mirror Symmetry.

Lunchtime recital featuring the Meraki Quartet comprised of members Munjung Heo, Tze Hin Kelvin Ng, Dorota Kolinek, and Stefan Rogers.



Programme:



Joseph Haydn - String Quartet Op. 20 No.5 in F minor



Felix Mendelssohn - String Quartet Op. 13 No. 2 in A minor

Abstract: The rapid increase of global mean temperature and unprecedented heat events require new approaches to support and monitor the climate adaptation and heat resilience of cities. Crafting effective plans necessitates accurate data and tools that adapt to the ever-changing dynamics of urban environments.

This presentation will show the recent advances in diagnosing and treating accurately, city by city, overheated urban areas (in time and space) where climate adaptation should be prioritised to promote heat resilience. The research aims to fully integrate crowdsourced urban climate observations (citizen weather stations) with satellite and remote sensing data using machine learning techniques to generate high spatio-temporal resolution observations of urban atmospheric states and dynamics. The results will support the development of an urban heat diagnosis tool with global applicability to enable insight and evidence-supported actions to promote zero-carbon and sustainable cooling at different scales. This research is part of the Future of Cooling Programme of the Oxford Martin School.



Bio: Jesus Lizana is Associate Professor in Engineering Science at the University of Oxford, with a unique experience profile in architecture and engineering. His research focuses on the cross-disciplinary challenges to support the transition towards zero carbon climate-responsive buildings.

At Oxford, Lizana is engaged in many research initiatives and has received several prestigious and extensive grants, including a Marie Curie Fellowship. He leads the research on Zero-Carbon Space Heating and Cooling at ZERO Institute and supports the interdisciplinary research in the Future of Cooling Programme of the Oxford Martin School. Alongside his academic career, Lizana also serves as a consultant on many building energy-related projects, data science, and sustainable cooling across various global locations, including the United Kingdom, India, Spain, Morocco, and Saudi Arabia.

Lizana received his PhD in low-carbon buildings at the University of Seville in Spain after completing a BSc in Architecture and an MSc in Building Engineering. Previously to his appointment at Oxford, he has lectured and conducted research at the University of Seville (Spain), the University of Edinburgh (Scotland), the Technical University of Munich (Germany), Universidade de Lisboa (Portugal), and the Spanish National Research Council (Spain).



This is a hybrid meeting. Please find the Teams link on XML webpage https://users.ox.ac.uk/~ndog0178/XML/xml_index.html.