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Past events

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1 February 202314:00

Reproducibility standards for machine learning in the life sciences with Stephanie Hicks, Associate Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health

Stephanie will be talking about her paper on reproducibility standards and her work at John Hopkins. Full details
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12 January 202314:00

Geological mapping in the age of Artificial Intelligence with Dr Charlie Kirkwood (IDSAI)

200 years since the publication of Britain’s first geological map by William Smith in 1815, our official geological maps - and those of geological surveys around the world - continue to be produced by a process of mental modelling and hand-drawing, in which polygons are drawn around parcels of rock that are deemed sufficiently self-similar to be classed as distinct units.. Full details
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7 December 202214:00

Building a Social Media Belief Observatory by Professor Joshua Introne at School of Information Studies, Syracuse University

There is continuing concern that online problems like toxicity, extremism, misinformation, and polarization are indicators of deeper societal ills, and moreover, that mediating technologies exacerbate these social problems. Research has focused on specific challenges, like detecting, suppressing, and correcting misinformation, or preventing toxicity. However, the various pernicious online phenomena that have drawn research attention are all at some level connected to beliefs, and might be usefully examined as a particular type of belief dynamics. Full details
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29 November 202213:00

Deep Neural Networks as Dynamical Systems by Dr Andrew Corbett (IDSAI)

Interpretable AI is key to understanding the predictions of machine learning models. Full details
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23 November 202214:00

Variant-level fine-mapping in major depressive disorder: new insights, new questions by Joni Coleman, Lecturer in Statistical Genetics at KCL

The adoption of genome-wide association studies (GWAS) has produced a statistical revolution in psychiatric genetics, identifying hundreds of regions of the genome robustly associated with risk for developing psychiatric disorders. Full details
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17 November 202214:00

Bipartite attitude networks: a Method for Exploring Opinion-Based Groups, Group-Based Opinions, and Social Polarization by Professor Michael Quayle at University of Limerick

I introduce a network-based approach to modelling attitude-based groups as bipartite networks, where people are linked by the attitudes they jointly hold, and attitudes become socially connected when jointly held by people. This structure simultaneously links people into groups and attitudes into meaningful clusters. With examples from opinion-surveys, social-media, and simulations, I show how group structure emerges in this "attitude space", and how people are located in it.. Full details
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9 November 202213:30

Leakage and the Reproducibility Crisis in ML-based Science by Sayash Kapoor, a second year PhD candidate at Princeton University.

The use of machine learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences. Full details
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2 November 202214:00

Design Thinking On Mars: Architecting New Systems for Collaborative Science on the Martian Surface

Sara Schnadt is an artist, designer and systems architect who works on the Mars 2020 Mission at NASA Jet Propulsion Laboratory. In her 22-year career in software, she has designed tools that provide wide access to civic data sets in the arts, digital journalism, data science for scientific research, and earth-observing and solar system aerospace. Since joining JPL in 2017, Sara has designed a system architecture for mathematical model management for use in mission planning for the Europa Clipper mission. She has also designed and implemented an architecture across the Mars 2020 ground system that, for the first time, formally captures science intent in the science team’s planning and analysis tools.. Full details
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26 October 202214:00

Working with the National Pupil Database in the ONS Secure Research environment by Alexey Bessudnov, Associate Professor (Sociology) at Exeter

In this talk Alexey will describe his experience of working in the ONS Secure Research environment with the data from the National Pupil Database (owned by the Department for Education). Full details
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19 October 202214:00

Causal Inference: Introduction to Partial Identification and Recent Advances by Jakob Zeitler, PhD Candidate in Foundational Artificial Intelligence at the Centre for Doctoral Training in Foundational AI at UCL, London

Abstract: Causal inference provides the fundamental causal reasoning that machine learning is missing to effectively tackle decision making problems. So far, full identification of causal effects has been the focus of the majority of research: Strong and mostly untestable assumptions, such as no unmeasured confounding, yield point estimates of how a sprint will increase my endurance by 2% or how $10k more in savings will get my loan application accepted. Ideally, we would want to make fewer strong assumptions, but still provide informative suggestions.. Full details
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12 October 202214:00

Deep-learning detection of harmful algal blooms by Dr David Moffat, PML

The detrimental effects of harmful algal blooms (HABs) on the marine ecosystem, human health, and shellfish and aquaculture industry are well known. Anthropogenic activities have led to an increase in frequency, extent and magnitude of HAB activity. Full details
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5 October 202214:00

Working with No 10 and ONS data scientists: data science for public policy by Dr Federico Botta

In this talk, I will present my experience of working with data scientists in 10 Downing Street and the Office for National Statistics. Full details
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28 September 202214:00

Using adjoint to accelerate inference in linear systems

As part of this year's IDSAI Research Seminar Series, Richard Wilkinson, Professor of Statistics, Faculty of Science, University of Nottingham will be presenting on using adjoint to accelerate inference in linear systems. Full details
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22 September 202214:00

Using Machine Learning to Create an Early Warning System for Welfare Recipients

Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. Full details
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30 June 202218:00

The Ethics of Artificial Intelligence

As Artificial Intelligence continues to impact many areas of our lives, we’re bringing together a world leading group of experts from health, tech, philosophy, and academia to discuss ethical questions that must be confronted. From driverless cars to dementia treatment, how can we ensure machines do the right thing? Will they inherit our own biases and unfairness?. Full details
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27 June - 1 July 20229:00

IDSAI Data Science Week 2022

IDSAI invites you to join in celebrating the best of Data Science and AI at Exeter by hosting a week long series of talks, research project presentations and training opportunities at the Streatham and Penryn Campuses. Full details
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9 June 202214:00

Machine Learning Techniques for Remote Sensing

This talk will present some new machine learning techniques for remote sensing. Full details
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1 November 202115:00

Presenting the Turing Fellow Research Projects - Prof Tim Lenton and Prof Gavin Shaddick: Data Science for Sustainable Development: Environment, Climate and Health

PLEASE CLICK THIS LINK TO REGISTER. Full details
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11 October 202115:00

Presenting the Turing Fellow Research Projects - Prof Sabina Leonelli: The Evidence Base of Artificial Intelligence: Supporting Data Fairness to Enhance Research Quality

PLEASE CLICK THIS LINK TO REGISTER. Full details
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28 June - 2 July 20219:30

IDSAI Data Science Week 2021

PLEASE CLICK THIS LINK FOR FULL DETAILS AND TO REGISTER. Full details
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24 May 202115:00

Presenting the Turing Fellow Research Projects - Peter Challenor

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24 May 202115:00

Presenting the Turing Fellow Research Projects - Prof Peter Challenor: Uncertainty Quantification (UQ) for Black-Box Computational Models with Application to Machine Decisions

PLEASE CLICK THIS LINK TO REGISTER. Full details
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