This week’s Fridays@2 session is intended to provide advice on exam preparation and how to approach the Part A, B, and C exams. A panel consisting of past examiners and current students will answer any questions you might have as you approach exam season.
This session, led by the Counselling Service, will guide you through a CBT informed understanding of anxiety, which may arise about exams. The session includes:
Please join us for a fireside chat, hosted by OSE, between PQShield founder and visiting professor, Dr Ali El Kaafarani, and Sami Walter, associate at Oxford Sciences Enterprises (OSE).
Dr Ali El Kaafarani is the founder and CEO of PQShield, a post-quantum cryptography (PQC) company empowering organisations, industries and nations with quantum-resistant cryptography that is modernising the vital security systems and components of the world's technology supply chain.
In this chat, we’ll discuss Dr Ali El Kaafarani’s experience founding PQShield and lessons learned from spinning a company out from the Oxford ecosystem.
I will describe a new method for constructing conformal blocks for the Virasoro vertex algebra with central charge c=1, by "nonabelianization", relating them to conformal blocks for the Heisenberg algebra on a branched double cover. The construction is joint work with Qianyu Hao. Special cases give rise to formulas for tau-functions and solutions of integrable systems of PDE, such as Painleve I and its higher analogues. The talk will be reasonably self-contained (in particular I will explain what a conformal block is).
The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics, Brown University;
Also @MIT & Pacific Northwest National Laboratory
https://sites.brown.edu/crunch-group/
George Karniadakis is from Crete. He is an elected member of the National Academy of Engineering, member of the American Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames.
He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the SES GI Taylor Medal (2024), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 150 and he has been cited over 130,000 times.
We will review physics-informed neural networks (NNs) and summarize available extensions for applications in computational science and engineering. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification.
These two key developments have formed the backbone of scientific machine learning that has disrupted the path of computational science and engineering and has created new opportunities for all scientific domains. We will discuss some of these opportunities in digital twins, autonomy, materials discovery, etc.
Moreover, we will discuss bio-inspired solutions, e.g., spiking neural networks and neuromorphic computing.
Many justifications can be offered for the study of the history of mathematics. Here we focus on three, each of them illustrated by a specific historical example: it can aid in the learning of mathematics; it can prompt the development of new mathematics; and last but certainly not least – it's fun and interesting!
Despite the stereotype of the lone genius working by themselves, most professional mathematicians collaborate with others. But when you're learning maths as a student, is it OK to work with other people, or is that cheating? How do you build the skills and confidence to collaborate effectively? And where does AI fit into all this? In this session, we'll explore ways in which you can get the most out of collaborations with your fellow students, whilst avoiding inadvertently passing off other people's work as your own.
In this interactive session, Jenny Roberts from the Institute of Mathematics and its Applications will offer guidance on what employers are looking for in mathematical graduates, and how best to sell yourself for those jobs!