Skills for Interdisciplinary Research

To acknowledge the variety of sectors where data science research is relevant, in March 2021, the Compass students are undertaking a series of workshops led by the Bristol Doctoral College to explore Skills for Interdisciplinary Research.  Using the Vitae framework for researcher development, our colleague at BDC will introduce Compass students to the following topics:

Workshop 1: What is a doctorate? A brief history of doctorates in the UK, how they have changed in the past two decades, why CDTs?, what skills are needed now for a doctorate?

Workshop 2: Interdisciplinarity – the foundations. A practical case study on interdisciplinary postgraduate research at Bristol.

Workshop 3: Ways of knowing, part 1 – Positivism and ‘ologies! Deconstructing some of the terminology around knowledge and how we know what we know. Underpinning assumption – to know your own discipline, you need to step outside of it and see it as others do.

Workshop 4: Ways of knowing, part 2 – Social constructionism and qualitative approaches to research. In part 1 of ways of knowing, the ideal ‘science’ approach is objective and the researcher is detached from the subject of study; looking at other approaches where the role of research is integral to the research.

Workshop 5: Becoming a good researcher – research integrity and doctoral students. A look at how dilemmas in research can show us how research integrity is not just a case of right or wrong.

Workshop 6: Getting started with academic publishing. An introduction on the scholarly publishing pressure in contemporary research and it explores what that means in an interdisciplinary context.

Student Research Topics for 2020/21

This month, the Cohort 2 Compass students have started work on their mini projects and are establishing the direction of their own research within the CDT.

Supervised by the Institute for Statistical Science:

Anthony Stevenson will be working with Robert Allison on a project entitled Fast Bayesian Inference at Extreme Scale.  This project is in partnership with IBM Research.

Conor Crilly will be working with Oliver Johnson on a project entitled Statistical models for forecasting reliability. This project is in partnership with AWE.

Euan Enticott will be working with Matteo Fasiolo and Nick Whiteley on a project entitled Scalable Additive Models for Forecasting Electricity Demand and Renewable Production.  This project is in partnership with EDF.

Annie Gray will be working with Patrick Rubin-Delanchy and Nick Whiteley on a project entitled Exploratory data analysis of graph embeddings: exploiting manifold structure.

Ed Davis will be working with Dan Lawson and Patrick Rubin-Delanchy on a project entitled Graph embedding: time and space.  This project is in partnership with LV Insurance.

Conor Newton will be working with Henry Reeve and Ayalvadi Ganesh on a project entitled  Decentralised sequential decision making and learning.

The following projects are supervised in collaboration with the Institute for Statistical Science (IfSS) and our other internal partners at the University of Bristol:

Dan Ward will be working with Matteo Fasiolo (IfSS) and Mark Beaumont from the School of Biological Sciences on a project entitled Agent-based model calibration via regression-based synthetic likelihood. This project is in partnership with Improbable

Jack Simons will be working with Song Liu (IfSS) and Mark Beaumont (Biological Sciences) on a project entitled Novel Approaches to Approximate Bayesian Inference.

Georgie Mansell will be working with Haeran Cho (IfSS) and Andrew Dowsey from the School of Population Health Sciences and Bristol Veterinary School on a project entitled Statistical learning of quantitative data at scale to redefine biomarker discovery.  This project is in partnership with Sciex.

Shannon Williams will be working with Anthony Lee (IfSS) and Jeremy Phillips from the School of Earth Sciences on a project entitled Use and Comparison of Stochastic Simulations and Weather Patterns in probabilistic volcanic ash hazard assessments.

Sam Stockman  will be working with Dan Lawson (IfSS) and Maximillian Werner from the School of Geographical Sciences on a project entitled Machine Learning and Point Processes for Insights into Earthquakes and Volcanoes

Responsible Innovation in Data Science Research

This February our 2nd year Compass students will attend workshops in responsible innovation.

Run in partnership with the School of Management, the structured module constitutes Responsible Innovation training specifically for research in Data Science.

Taking the EPSRC AREA (Anticipate, Reflect, Engage, Act) framework for Responsible Innovation as it’s starting point, the module will take students through a guided process to develop the skills, knowledge and facilitated experience to incorporate the tenets of the AREA framework in to their PhD practice. Topics covered will include:
· Ethical and societal implications of data science and computational statistics
· Skills for anticipation
· Reflexivity for researchers
· Public perception of data science and engagement of publics
· Regulatory frameworks affecting data science

New advanced computing hardware for Compass students

As part of UKRI NPIF Talent Funding 2019/2020, Compass applied for additional hardware and now has an operational GPU node*, specifically designed for the needs of statistical and machine learning analysis and with priority for COMPASS students.

This will enable our students to more rapidly train a wide variety of powerful, state of the art models in machine learning. GPUs have been essential to the rise of deep learning which, in the past decade, has revolutionised machine learning, rendering previously impossible tasks in image and natural language processing surmountable.

*: Technical specifications: 4 x Nvidia RTX 2080Ti graphics cards 11GB memory, dual quad core Xeon 4112 CPUs, 96 GBytes RAM, 10 Gbit ethernet, 1 TB local drive.

JGI event: Data Science Seminars

The Jean Golding Institute runs an annual series of Data Science Seminars

Upcoming seminars (if you are interested in attending you can sign up with Eventbrite using the links below):

DeepMind UK scientist to tutor Compass students

Taylan Cemgil, Research Scientist, DeepMind UK will speak at the Jean Golding Institute Data Seminar Series and an exclusive talk specifically for Compass students on Representation Learning.

Harvard Prof delivers guest lectures to Compass students

We’re are delighted to welcome Pierre Jacob, Associate Professor of Statistics at Harvard University, to the University of Bristol in March.

Pierre will be delivering lectures for the COMPASS CDT students but all staff in the School of Maths are welcome to attend.

Title: Couplings and Monte Carlo

In his lectures, Pierre will cover couplings, total variation and optimal transport. He will describe the use of couplings in Monte Carlo methods, such as coupling from the past, diagnostics of convergence, and bias removal.

This event is sponsored by COMPASS – EPSRC Centre for Doctoral Training in Computational Statistics and Data Science.

Compass students take part in electricity demand forecasting hackathon

Dr Jethro Browell, Research Fellow at the University of Strathclyde, and Dr Matteo Fasiolo, Lecturer at the University of Bristol, ran a regional electricity demand forecasting hackathon for students in the COMPASS Centre for Doctoral Training yesterday.

Visiting Research Fellow Dr Browell gave students an overview of how the Great Britain electricity transmission network has changed during the last decade, with particular focus on the consequences of the increased production from small-scale renewable sources, which appear as “negative demand”.

Dr Fasiolo then introduced a dataset containing electricity demand and weather-related variables, such as wind speed and solar irradiation, from 14 regions covering the whole of Great Britain. He proposed an initial forecasting solution based on a simple Generalized Additive Model (GAM), which he used to forecast the demand in each region.

The hackathon started, with the “Jim” team being the first to propose an improved solution, based on a more sophisticated GAM model, which beat the initial GAM in terms of forecasting accuracy.

The “AGang” team then produced an even more sophisticated GAM, which took them to the top of the ranking. In the meantime, the “D&D” team was struggling to make their random forest work, and submitted a couple of poor forecasts. Toward the end of event, “AGang” produced a couple of improved GAM solutions, which further strengthened their lead.

While Dr Fasiolo and Dr Browell were wrapping up the event and preparing to award the winners, the “D&D” team caught everyone by surprise by submitting a forecast which beat all others by a margin, in terms of forecasting accuracy. Their random forest was far better than the GAMs at predicting demand in Scotland, where wind production is an important factor and the dynamics are quite different relative to the other regions.

Congratulations to the top three teams:

  1. D&D:  Doug Corbin and Dom Owens
  2. AGang: Andrea Becsek, Alex Modell and Alessio Zakaria
  3. Jim:  Michael Whitehouse, Daniel Williams and Jake Spiteri

Winning team “D&D” said:  “Given physical measurements, such as wind speeds and precipitation, as well as calendar data, we first performed a minor amount of feature engineering. Given the complex nature of the interactions between the variables, and large amount of data available, we opted to fit random forest models. These performed feature selection for us and provided some robustness from outlying observations.

“However, the models took a long time to fit. Despite parallelising the model fitting across the regions, we only just got our predictions in before the deadline. Thankfully, our model consistently outperformed the other approaches.

“Everyone taking part had a great time learning about the challenges of energy modelling, and we thrived under the pressure of friendly competition.”

Dr Browell added: “Computational statistics and data science is driving innovation in the energy sector and the technologies they enable will play a huge role in the decarbonisation. I was pleased to be able to expose the COMPASS cohort to this application and hope that they will be inspired to apply their expertise to energy and climate problems in the future.”

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