Scientific Organisers
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Francesca Arrigo, University of Strathclyde
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Francesco Tudisco, University of Edinburgh
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Paul Dobson, Heriot-Watt University
About:
As the size of data and models is growing at an unprecedented pace, the possibility of reducing dimension and computational resources while maintaining performance and robustness is one of the key challenges of modern data science and machine learning. While advancements in software and hardware have made it possible to develop data-led tools of massive scale by allocating increasing resources, we can draw upon decades of fundamental research in numerical analysis and scientific computing to recognize the immense impact of reducing model size and computational complexity in the design, implementation, and widespread adoption of scientific software for large-scale problems. This workshop brought together experts at the interface between (mathematics of) data science, numerical analysis, and scientific computing and will allow us to discuss the newest trends in numerical analysis, with a focus on the main aspects numerical analysis could contribute to the growing fields of data science and machine learning.
The primary objective of the workshop was to capitalise on the occasion to bring together researchers in numerical analysis and related fields to discuss the latest developments, exchange ideas, and foster new collaborations. We anticipate that the newly formed collaborations, as well as the well-established ones, will produce several outputs on prestigious journals.
Keynote Speakers
- Elena Celledoni, Norwegian University of Science and Technology
- Peter Grindrod, University of Oxford
- Francoise Tisseur, University of Manchester
- Ivan Tyukin, King's College London
- Jesus-Maria Sanz-Serna, Universidad Carlos III de Madrid
- Peter Kloeden, University of Tubingen
- Brynjulf Owren, Norwegian University of Science and Technology
- Vanni Noferini, Aalto University
- Alison Ramage, University of Strathclyde
- Andrew M. Stuart, California Institute of Technology
- Benedict Leimkuhler, University of Edinburgh
- Catherine Higham, University of Glasgow
- Aretha Teckentrup, University of Edinburgh
- Anders Hansen, University of Cambridge
- Alexander Bastounis, University of Leicester
- Konstantinos Zygalakis, University of Edinburgh
Programme:
| THURSDAY 11 APRIL 2024 | ||
| Registration and Refreshments | ||
| Welcome and Housekeeping | ||
| Peter Grindrod, University of Oxford | Desperately Seeking Something | |
| Francoise Tisseur, University of Manchester | Deflation Strategies for Nonlinear Eigenvalue Problems | |
| Andrew Stuart, California Institute of Technology | Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance | |
| Refreshments | ||
| Elena Celledoni, Norwegian University of Science and Technology | Deep learning of diffeomorphisms with applications | |
| Anders Hansen, University of Cambridge | On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know' | |
| Lunch | ||
| Alexander Bastounis, University of Leicester | On Smale’s 9th problem, generalised hardness of approximation and the limits of AI | |
| Catherine Higham, University of Glasgow | Deep learning algorithms for quantum imaging technology | |
| Ivan Tyukin, King's College London | The inevitability and typicality of instabilities and fragility in AI | |
| Refreshments | ||
| Brynjulf Owren, Norwegian University of Science and Technology | Stability of numerical methods set on Euclidean spaces and manifolds with applications to neural networks. | |
| Vanni Noferini, Aalto University | Composition of two forward stable algorithms: When is it forward stable? | |
| Welcome Reception, hosted at ICMS | ||
| FRIDAY 12 APRIL 2024 | ||
| Jesus-Maria Sanz-Serna, Universidad Carlos III de Madrid | A new optimality property of Strang's splitting | |
| Alison Ramage, University of Strathclyde | Multifidelity Methods for Sensitivity Analysis of a Pollutant Dispersal Model | |
| Peter Kloeden, University of Tubingen | Euler-like numerical schemes for Caputo fractional differential equations: deterministic and stochastic | |
| Refreshments | ||
| Konstantinos Zygalakis, University of Edinburgh | Optimization algorithms and differential equations: theory and insights | |
| Aretha Teckentrup, University of Edinburgh | Convergence rates of deep Gaussian process regression | |
| Lunch |
