Turing Gateway to Mathematics by Cambridge University

What's Turing Gateway to Mathematics about?

In March 2013, the Isaac Newton Institute for Mathematical Sciences (INI) launched the Turing Gateway to Mathematics (TGM). The TGM is an impact initiative of the INI and aims to stimulate the interchange of knowledge and ideas between academics of different disciplines and users of modern mathematics. Named after Alan Turing because of his exceptionally wide influence across a very broad front, the Gateway is a channel for collaboration and cooperation between academia and industry. It will help to shorten pathways to impact and increase access to modern mathematical methods for other industrial and academic areas.

What's Turing Gateway to Mathematics about?

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Turing Gateway to Mathematics episodes:

Geometric Methods for the Approximation of High-Dimensional Data Sets and High-Dimensional Dynamical Systems

Large Data Limits for Graphical Models

Dimensionality Reduction Techniques in Medical Data Analysis

High Dimensional Changepoint Estimation via Sparse Projection

Using Novel Data Sources to Model Commodity Markets

Surface Approximations and Geometric Energies

Your Browser Can Prove Your Lemmas

Novel Uses of Shipping Data in Commodity Markets

Nonparametric Estimation of Diffusions in the Low-Frequency Setting

Reconstruction Methods in Transmission Electron Tomography

Learning a Sampling Pattern for MRI

Approaches to Sampling and Inference for Bayesian Nonparametrics

Photoacoustic Tomography with Total Variation Regularisation

Quantum Entropies Through Convex Optimisation

Sparse Principal Component Analysis via Random Projections

Welcome and Introduction, Update from CCIMI

Joe Keller and the Courant Institute 1970-71: Beginning a Mathematical Journey

Segmentation of Organs at Risk for Radiotherapy Planning

The Human Connectome Project Multimodal Cortical Parcellation: New Avenues for Brain Research

Wrap-up and Questions

Introduction to Imaging and Data Integration With an Update from LCMH

Elevator Pitch Summary

Microstructure Imaging with Diffusion MRI

Imaging plus X: Data-driven Models of Neurological Disease Progression

Cardiac MRI and Myocardial Infarction: the role of Mathematical Modelling

Calibration of the Heart Model: Dealing with Uncertainty

Modelling Challenges in Segmentation of Lung Images in Presence of Missing Data

Development of Cancer Diagnostics using mid-Infrared Imaging including FEL-based SNOM

Localising Shapes in Neuroimaging Data

Welcome and Introduction with update from CMIH

Introduction

Presentation: Challenge 5. Understanding Rainfall Patterns

Presentation: Challenge 4. Making Decisions Using Uncertain Forecasts

Presentation: Challenge 3. Estimating Flood Probability Using Historical Data

Presentation: Challenge 1. A Risk-based Analysis of Small Scale, Distributed, "Nature-based" Flood Risk Management Measures Deployed on River Networks

Presentation: Challenge 2. Modelling Sewer Networks

Closing Remarks

Opening Comments

Introduction: Challenges 4 & 5. Making Decisions Using Uncertain Forecasts and Understanding Rainfall Patterns

Introduction: Challenge 2. Modelling Sewer Networks

Introduction: Challenge 3. Estimating Flood Probability Using Historical Data

Introduction: Challenge 1. A Risk-based Analysis of Small Scale, Distributed, “Nature-based” Flood Risk Management Measures Deployed on River Networks

Introduction

Mirages, in the Spirit of JBK: Rays, Caustics, Waves and Raman's Instructive Confusion

Radiation by a Dielectric Wedge

From Kiiking to Kuzmak

Numerical Investigations of the Properties of Nonlinear Free Surface Flows

Closing Remarks

An Asymptotic Matching Problem in Braginskii Magnetohydrodynamics

The Unified Transform, Imaging, Asymtotics