Shaping the Future: How Our 2022 Cohort is Redefining Science Communication

A new benchmark in academic communication within our community relating to cutting-edge physics research leveraging advanced machine learning methods.

The 2022 cohort at our recent industry partner event and data science career fair set a new standard in academic communication, brilliantly showcasing their cutting-edge physics research, enhanced with advanced machine learning techniques in two-minute spotlight talks and poster presentations.

"The student spotlight talks were on another level. Their achievements in marrying scientific rigour and cutting-edge machine-learning techniques with clarity are immense.

- Dr. Tim Scanlon, Co-Director of the Centre for Data Intensive Science and Industry

As these future leaders prepare for 6-month industry placements next academic year, they carry forward the invaluable skill of communicating complex science effectively. This preparation not only positions them for success in data-intensive science and industry but also elevates the standard for academic communication. We are proud of their achievements!

Join Us on This Journey: We invite you to follow their progress and engage with their groundbreaking work. Stay tuned for updates on their industry placements and future contributions to advance both science and machine learning.

Join the Next Wave of Innovators: Applications are now open for our next cohort! Seize the opportunity to be a part of this trailblazing community, where cutting-edge scientific research meets advanced machine learning and is distilled with exceptional communication. Apply now and take your first step towards becoming a future leader in science and industry.

Noah Clarke Hall is working on the bleeding edge of ultra-fast machine learning deployment.

Callum Duffy is creating quantum algorithms to find elusive clues in particle collisions.

Max Hart is developing transformers for a massive game of connecting the dots.

Nathan H. is deploying advanced edge detection algorithms to extract information from electrons in a magnetic bottle.

Paul Nathan is using auto-encoders to find anomalies within tens of millions of galaxies.

Alicja Polanska is using normalising flows to stabilize Bayesian model comparisons.

Nikita Pond sits at the forefront of leveraging transformer models to drastically improve Higgs boson measurements.

James Ray is using semi-supervised learning and explainable AI methods to break the (lego) mould of astronomical data analysis.

Alex Saoulis is using a masked auto-encoder for density estimation to understand earthquakes and preserve life.

Tara Tahseen is breaking open intractable problems with surrogate models to address questions of life, planet formation, and climate science.

Antónia Vojteková is deploying deep convolutional neural networks to decode the secrets of exoplanets.