Compass Guest Lecture: Dr Vincenzo Gioia and Professor Ruggero Bellio

We are delighted to announce the upcoming Compass Guest Lecture with Dr Vincenzo Gioia (University of Trieste) and Professor Ruggero Bellio (University of Udine).


11am – 12pm: Scalable Estimation of Probit Models with Crossed Random Effects, Professor Ruggero Bellio

1 – 2pm: Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain, Dr Vincenzo Gioia


Scalable Estimation of Probit Models with Crossed Random Effects
Professor Ruggero Bellio, Department of Economics and Statistics, University of Udine (Italy)

This talk illustrates a scalable approach to mixed effects modeling with a probit link and a crossed random effects error structure. Random effects with a crossed structure arise often in social and business applications, a notable setting being that of electronic commerce, with random effects related to customers and purchased items, respectively. In sparsely sampled crossed data the computation for both frequentist and Bayesian estimation can easily grow superlinearly with respect to the sample size, which severely limits the use of these models for very large settings. The proposed method belongs to the class of composite likelihood estimators, and entails the fit of three misspecified reduced models. The resulting estimator is consistent and has an overall computational cost linear in the number of observations. This is a joint work with Art Owen and Swarnadip Ghosh, Stanford University, and Cristiano Varin, Ca’Foscari University of Venice.


Additive Covariance Matrix Models: Modelling Regional Electricity Net-Demand in Great Britain
Dr Vincenzo Gioia, Department of Economics, Business, Mathematics and Statistics University of Trieste (Italy)

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here we forecast the joint distribution of net demand across the 14 regions constituting Great Britain’s electricity network. Joint modelling is complicated by the fact that the net-demand variability within each region, and the dependencies between regions, vary with temporal, socio-economical and weather-related factors. We accommodate for these characteristics by proposing a multivariate Gaussian model based on a modified Cholesky parametrisation, which allows us to model each unconstrained parameter via an additive model. Given that the number of model parameters and covariates is large, we adopt a semi-automated approach to model selection, based on gradient boosting. In addition to demonstrating that adopting a covariate-dependent covariance matrix model leads to substantial forecasting performance improvements, comparable to those obtained by using a full rather than a diagonal static covariance matrix, we explore the model output via accumulated local effects and other visual tools to get insights into how the covariates affect net-demand variability and dependencies. This is a joint work with Matteo Fasiolo, University of Bristol, Jethro Browell, University of Glasgow, and Ruggero Bellio, University of Udine.

The Smith Institute: DataScience@work seminar

About the Smith Institute

Mathematical approaches coupled with data exploration are key to tackling real-world challenges, to finding the solutions that transform industries and enable societies, businesses and governments to thrive.

From improving the performance of railways to meeting Carbon Zero targetsforecasting crop growth to verifying radio spectrum auctions, the Smith Institute has been tackling their clients’ most critical and complex problems with bespoke solutions for over twenty years.

The possibilities of harnessing mathematical tools are infinite; they enable transformation for their clients across a variety of sectors, applying specialist domain knowledge paired with fresh thinking in transportenergydefencesecurityFMCG and radio spectrum.

For further information about The Smith Institute, see their website.

ONS: DataScience@work seminar

Saliha Minhas, Data Scientist at Office for National Statistics (ONS)

Details TBA.

About ONS:

ONS’s main responsibilities are collecting, analysing and disseminating statistics about the UK’s economy, society and population.

For further information about ONS, see their website.

OVO Energy: DataScience@work seminar


Dave Eagon, Lead Data Scientist

Dr Angharad Stell, OVO Energy


  • 2 – 3.30pm Seminar + Q&A in Room G.13, Fry Building
  • 3.30 – 4pm networking with students and colleagues in Common Room, Fry Building. 
Abstract: OVO is a major UK Energy supplier, headquartered in Bristol, providing gas and power to domestic consumers. The Portfolio Management team is responsible for forecasting, valuing and hedging billions of pounds of commodity each year. In this talk we will discuss the challenges faced by our team in recent years (COVID, Cost of Living, Ukraine), the challenges that are still to come and how we use machine learning, statistics and computer science to manage our risks. We will explore what a typical day in our data science team might look like, the decisions we are responsible for and how we make them.
Dave Eagon has a degree in Mathematics and Statistics from Oxford University and was an investment banker before qualifying as an actuary specialising in finance and investments. He joined OVO in 2021 and now leads the OVO trading data science team. Dr Angharad Stell has a degree in Natural Sciences from Cambridge University and a PhD in Atmospheric Chemistry from Bristol University. She spent two years as a Research Associate in the Atmospheric Chemistry Research Group at Bristol University before joining the trading data science team in 2022.

About OVO Energy

OVO Energy was founded in 2009 and redesigned the energy experience to be fair, effortless, green and simple for all customers. OVO is on a mission through its sustainability strategy Plan Zero to tackle the most important issue of our time; the climate crisis, by bringing our customers with us on the journey towards zero carbon living. OVO Energy has committed to being a net zero carbon business and achieve bold science-based carbon reduction targets by 2030, while helping members reduce their household emissions at the same time.

For further information about OVO Energy, see their website and their LinkedIn page.

Compass Conference

We are excited to announce that we will be holding our first Compass Conference on Tuesday 13th September 2022, which will be hosted in the newly refurbished Fry Building, home to the School of Mathematics.

Fry Building
The Fry Building and Voronoi installation

The conference will be a celebratory showcase of the achievements of our students, supervisory teams, and collaborations with industrial partners.


Exact timings are to be confirmed. Welcome refreshments will be served from 9am with talks to start by 10am. The scheduled talks will finish by 5pm and dinner will finish by 8.30pm.

  • Registration, refreshments and welcome talk
  • Lightening talks: 3 min presentations from Compass PhD students
  • Poster viewing session and networking followed by lunch
  • Research talks:
    • Ed DavisUniversal Dynamic Network Embedding (download slides)
    • Ettore FincatoSpectral analysis of the Gibbs sampler with the concept of conductance
    • Alexander ModellSpectral embedding and the latent geometry of multipartite networks (download slides)
    • Hannah SansfordImplications of sparsity and high triangle density for graph representation learning
    • Michael WhitehouseConsistent and fast inference in compartmental models of epidemics via Poisson Approximate Likelihoods
    • Alessio ZakariaYour Favourite Optimizer may not Converge: Click here to see more
  • Special guest lecture: John Burn-Murdoch, Interactive Data Journalist at the Financial Times.


For further information, please contact

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