Machine Learning in Science Colloquium

Published: 4 December 2023

The integration of Machine Learning (ML) and Artificial Intelligence (AI) across scientific disciplines presents a unique opportunity for researchers to come together and ignite collaboration. The Machine Learning in Science (MLiS) Colloquium serves as a platform to facilitate conversations and collaborations leading to upskilling, knowledge-sharing, and innovation.

Mission Statement

Our aim is to create a community of researchers to share knowledge about machine learning to enhance research, develop skills relevant to industry and encourage interdisciplinary collaboration.

Overview

The adoption of Machine Learning (ML) and Artificial Intelligence (AI) across the natural sciences provides a common research focus across disciplines, creating an ideal platform to spark interdisciplinary interactions and collaborations. For example, researchers from chemistry, psychology, and physics are using the same underlying algorithms towards different applications. Early career researchers gaining hands-on exposure to cutting edge machine learning techniques is imperative to harness its potential. Machine Learning’s fast-paced evolution demands constant upskilling and transcending the conventional teaching-learning methods. The Machine Learning in Science (MLiS) colloquium series aims to provide a platform for researchers from different backgrounds to share knowledge and ideas about ML as they learn. The inclusion of computer scientists helps to troubleshoot common issues, validate ideas, and encourages research at the university to remain at the bleeding edge of AI. This colloquium series invites speakers and attendees from any discipline and any level of academia. Many of the concepts discussed in seminars are new machine learning methods which are still in their infancy and puts researchers at all levels on the same learning curve.

The colloquium series began in 2020 (pre-pandemic) with a networking event to attract volunteers to help organise efforts, and a group of around eight people formed the organising committee. The first colloquium featured six short talks from researchers in physics, bioengineering, computer science, neuroscience, and cardiology, with some time at the end for discussion, sharing ideas and meeting people. Following this, we held monthly online sessions during the pandemic, witnessing an increase in attendance from 17 to 33 within the first three months. The theme of the seminars usually revolves around a common ML model with two speakers sharing their insights on applying ML to their research.

The principle of our colloquium series is to bridge the gap between researchers from different fields by showcasing common machine learning techniques that can benefit all.

To continue the discussion beyond the webinars and foster a community, a Slack workspace was also created and promoted in our webinars. The Slack initially served as a useful platform for researchers to introduce themselves and their team and describe their AI interests. The Slack community channel has grown to 186 members and is currently used to promote our events and address any queries regarding the upcoming events.

In August 2022, we hosted a conference in the Advanced Research Centre, Glasgow, featuring three external keynote speakers from the University of Edinburgh, Google Deepmind, and the Max Plank Institute, Tübingen. Each keynote speaker introduced a session broadly themed around Medical Applications of Machine Learning; Physics Applications of Machine Learning; and Theoretical Machine Learning and Neuroscience. We also showcased 20 posters on the use of machine learning in scientific research. Prizes were also awarded to the best poster. The event was free to attend and attracted over 80 attendees.

We are continuing our colloquium series in collaboration with the Centre for Data Science and AI (CDSAI), hosting multidisciplinary talks about the uses of machine learning in the sciences, hands-on upskilling workshops, and networking events. Through this initiative, we endeavour to contribute to CDSAI’s aim of fostering cross-sectoral collaborations by showcasing the research at the University of Glasgow and UK-wide, providing a platform for knowledge transfer across disciplines, presenting opportunities for upskilling, and facilitating collaborations.

If you would like to get involved by presenting at one of our events or helping organise the seminars, then please write to us at scieng-mlinscience@glasgow.ac.uk.

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First published: 4 December 2023

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