Events

MaThRad Annual Review & Festive Mixer

The MaThRad Annual Review & Festive Mixer is a chance for our researchers, partners and colleagues to come together, look back on the year, and share what’s taking shape for the months ahead.

It’s a relaxed mix of short talks, a panel discussion, some group work, and plenty of time to catch up properly over food and conversation.

MaThRad remains the UK’s only research programme focused on the mathematical theory of radiation transport across fission, fusion and nuclear medicine. This gathering gives us space to connect across those areas, hear what people are working on, and think together about where the next year might take us.

Daniella Witten (https://www.danielawitten.com/), COPSS awardee, current co-editor of JRSSB.

Research interests: statistical machine learning, biostats, etc.

Suppose that a data analyst wishes to report the results of a least squares linear regression only if the overall null hypothesis—namely, that all non-intercept coefficients equal zero—is rejected. This practice, which we refer to as F-screening (since the overall null hypothesis is typically tested using an F-statistic), is in fact common practice across a number of applied fields. Unfortunately, it poses a problem: standard guarantees for the inferential outputs of linear regression, such as Type 1 error control of hypothesis tests and nominal coverage of confidence intervals, hold unconditionally, but fail to hold conditional on rejection of the overall null hypothesis.

In this talk, I will present an inferential toolbox for the coefficients in a least squares model that are valid conditional on rejection of the overall null hypothesis. I will present selective p-values that lead to tests that control the selective Type 1 error, i.e., the Type 1 error conditional on having rejected the overall null hypothesis. Furthermore, they can be computed without access to the raw data, using only the standard outputs of a least squares linear regression, and therefore are suitable for use in a retrospective analysis of a published study. I will also present confidence intervals that attain nominal selective coverage, and point estimates that account for having rejected the overall null hypothesis.

I will illustrate this selective procedure via re-analysis of a published result in the biomedical literature, for which the raw data is not available.

This is joint work with Olivia McGough (U. Washington) and Daniel Kessler (UNC Chapel Hill).

Overview

The workshop aims to promote the transfer of methods and ideas between the environmental modelling and infectious disease modelling communities. By discussing the common mathematical challenges that underpin both subjects, we hope that attendees will gain new tools and perspectives to advance their own research.

Overview

Modern neural networks operate at unprecedented scales across model size, data and compute. A central research problem is to understand how their performance scales with these factors, which guides how networks can be trained optimally at scale. In recent years, empirical heuristics for scaling have arguably driven much of the success of Large Language Models (LLMs). Theoretical work on scaling laws has also seen much fruitful progress, shedding light on empirical phenomena such as model collapse, emergence and training stability, while providing concrete practical insights on techniques such as hyperparameter tuning.

This three-day workshop will bring together researchers working at the frontiers of theoretical scaling laws to share their insights about the field. The workshop will be the first of its kind in the UK, inspired by successes of similar workshops in the US and Europe.

• The first half of the workshop consists of introductory tutorials, with the aim of equipping attendees with basic tools for framing and understanding problems in this field;
• The second half will feature talks on latest research advances.

The aim is for researchers across academia and industry to learn about and participate in this active field of research, which has seen many fruitful empirical outcomes.