Attendee Bios

Julian Kunkel (OC)

Lecturer

University of Reading

Julian's research interest is revolving around High-Performance Computing and high-performance storage. Additionally, he is interested in the integration of ML techniques into HPC workflows.

Previously, he worked as postdoc in the research department of the German Climate Computing Center (DKRZ).

David Hogg (OC)

University of Leeds

David’s research is in AI, and particularly on activity analysis from image sequences. He works extensively across disciplinary boundaries, applying AI in medicine, biology, engineering design and environmental science. He is Director of the UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care.

Shannon Jackson (OC)

Senior Business Administrator

Met Office

Shannon is a key point of contact for the Met Office Science Partnerships team and provides support to the Met Office Academic Partnership. Shannon’s main role in this workshop is to provide event support as a member of the organising committee.

Katie Norman (OC)

Nowcasting Science Manager

Met Office

Katie manages newly-formed Nowcasting Science Team at the Met Office. The team's aim is to exploit the full potential of conventional and novel approaches to observing our environment to improve 0-2 hour weather forecasts and downstream decision-making, independently of conventional numerical weather prediction.

Marc Deisenroth (OC)

University College London

Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at UCL. Marc’s research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making, with applications in climate science, robotics, and sustainable development.

Sam Adams (OC)

Scientific Systems Manager

Met Office Informatics Lab

Sam is the manager for Data Science Research at the Met Office Informatics Lab. She has worked for many years as a scientific software engineer and has post-doctoral research experience in biologically inspired computing, encompassing Machine Learning and AI. Her current research interests are in unsupervised learning and generally in applying Machine Learning to weather and climate data.

Duncan Watson-Parris (OC)

University of Oxford

Duncan is an atmospheric physicist working at the interface of climate research and machine learning. His work combines global aerosol models with novel observational constraints, using cutting-edge machine learning techniques, to reduce model uncertainties and improve projections of climate change.

Richard Everson (OC)

University of Exeter

Richard is a professor of Machine Learning and director of the Institute for Data Science and Artificial Intelligence at the University of Exeter. His research interests focus on machine learning and optimisation and the interactions between them. Particular interests in nowcasting are in using unconventional and less well calibrated data sources.

Kate Robson-Brown (OC)

University of Bristol

Kate is Director of the Jean Golding Institute for Data Science and Data Intensive Research, and her research explores how living tissues respond to changing and extreme environments, with the development of methodologies for the capture, computational modelling, analysis and interpretation of data describing complex material and structural characterisation.

John Eyre

Met Office

John Eyre is a Met Office Fellow and former Head of Satellite Applications, and he is now attached to the Space and Nowcasting Applications and Satellite and Surface Assimilation Sections. His personal research include: infra-red and microwave radiative transfer modelling, retrieval of atmospheric variables from satellite observations, assimilation of remotely-sensed observations into NWP models, and the design of future observing systems.

Neven Fuckar

University of Oxford

Ph.D. in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA. Presently MSC fellow working on weather and climate dynamics, forecast and attribution of extreme events at the Environmental Change Institute, University of Oxford.

Robert Plant

University of Reading

Bob's research interests largely concern physics-dynamics interactions: i.e., model parameterizations and their influences on dynamics. Of particular interest is atmospheric convection, ranging from idealized process studies to its representation in numerical models, to its role in synoptic-scale systems. Other interests include boundary layer modelling, the use of ensembles and predictability. He has explored ML for convective cloud populations in collaboration with PNNL.

Suman Ravuri

DeepMind

Suman Ravuri is a Research Scientist at DeepMind. His research interests focus on creating robust ML algorithms for large-scale real world datasets, and recently on deep generative models and their evaluation. In particular, he is interested in improving precipitation nowcasting, which pose interesting challenges for standard ML algorithms.

Dann Mitchell

University of Bristol

Dann is the Bristol Met Office Joint Chair in Climate Hazards, and is interested in how machine learning can be utilised to better simulate extreme events at local scales, including hazards such as heat-related mortality.

Dapeng Wang

University of Leeds

I am a senior bioinformatics research officer at LeedsOmics at the University of Leeds and I am particularly interested in utilising machine learning and deep learning techniques to investigate the evolution of the complicated biological systems through the integration of a broad range of various omics datasets.

Sacha Lapins

University of Bristol

I am a final-year PhD student in the School of Earth Sciences at the University of Bristol. My research focuses on machine learning applications in seismology and volcanic monitoring, primarily focusing on automating time-consuming / computationally expensive signal processing tasks and parameter estimation for real-time volcanic hazard monitoring.

Jack Kelly

Open Climate Fix

Jack is a computer scientist who is terrified by climate change. He did a PhD on using machine learning to disaggregate domestic electricity demand. Then he worked at Google DeepMind on forecasting wind power production. In early 2019, Jack left DeepMind to co-found Open Climate Fix, a non-profit focused on using open science to mitigate climate change. Jack also spends half his time consulting for National Grid Electricity System Operator (ESO) on using ML to predict renewable generation.

Peter Dueben

ECMWF

Peter is the Coordinator of machine learning and AI activities at ECMWF and holds a University Research Fellowship of the Royal Society that allows him to follow his research interests in the area of numerical weather and climate modelling, machine learning, and high-performance computing.

Dr Leonid Bogachev

University of Leeds

BSc/MSc Mathematics (Distinction), PhD Probability/Statistics (Moscow State University). Expertise: Probability, Statistical Physics, Statistics (extreme value theory). Over 50 peer-reviewed papers. Associate Editor for Statistics & Probability Letters. Awards: Royal Society Fellowship; Leverhulme Research Fellowship; ZiF Research Group; PI on NTI grant “Scalable Machine Learning for Data Stream Forecasting of Extreme Values” (2019-2021).

Jamie Taylor

The University of Sheffield

Jamie works closely with the forecasting team at National Grid ESO to develop methodologies and services for modelling and monitoring solar PV generation and deployment across the GB transmission network,. Jamie also leads the technical development of Sheffield Solar's commercial PV forecast service, which combines ML, statistical modelling, traditional NWP and PV generation data to forecast nationally and regionally aggregated PV outturn.

Claire Bartholomew

Met Office (& University of Leeds)

Claire is a scientist within the aviation team at the Met Office, undertaking data analysis to better understand and improve how meteorological data is used within the aviation industry, ranging from low cloud around airports to high altitude ice crystals. She is currently studying for a PhD at the University of Leeds, investigating machine learning methods for convective nowcasting and their application to the aviation sector.

Charlie Kirkwood

University of Exeter

Charlie’s PhD research, in collaboration with the Met Office, focuses on developing statistical and machine learning approaches for improving weather forecasting. His core interests are in nature and machine learning, and how we can use machine learning to improve how we interact with nature. Charlie's skills and interests have been honed through previous experience in data science research roles at the British Geological Survey and Walgreens Boots Alliance.

Emily Vosper

University of Bristol

I am currently studying for a PhD which draws together ideas from the fields of Artificial Intelligence and Climate Change Science.

Neil Leiser

University College London

I received the Meng degree in civil and environmental engineering from Imperial College London in 2019 and spent a year abroad at the University of Queensland in Australia focusing on research related to traffic prediction. I am currently following a MSc program in data science and machine learning at University College London. My research interests include machine learning, deep learning, traffic prediction and climate science.

Miguel Rico-Ramirez

University of Bristol

I am currently a Senior Lecturer in Radar Hydrology and Hydroinformatics within the Department of Civil Engineering at the University of Bristol. I am actively doing research around four key research themes: Quantitative Precipitation Estimation (QPE), Quantitative Precipitation Forecasting (QPF), Propagation of QPE/QPF uncertainty into hydrological/hydraulic models and Earth observations of the hydrological cycle. More about my research activities at: https://orcid.org/0000-0002-8885-4582

Valerio Maggio

University of Bristol

Valerio Maggio is a Senior Research Associate in Data Science and Machine Learning at University of Bristol, and member of the Dynamic Genetics Lab. His research effort is focused on methods and software for reproducible machine/deep learning for biomedicine and environment. He is also a Cloud Research Software Engineer as part of the Microsoft initiative for Higher Education and Research.

Joelle Buxmann

Met Office

Working at the Met Office, Joelle’s main interest is remote sensing of aerosols and trace gases. In 2013, she went on a winter expedition to Antarctica on the research Vessel Polarstern in collaboration with the University of Heidelberg. She worked on detection of Halogen oxides and sea salt aerosols in correlation with ozone depletion and mercury deposition. She graduated in 2012 with a PhD in Physics. Her work included Smog chamber studies of atmospheric halogen chemistry at Bayreuth Center of Ecology and Environmental Research. Besides her laboratory work she conducted a number of field campaigns.

Lucia Deaconu

University of Oxford

My research interests are in atmospheric science, with a main focus on anthropocentric aerosols and their impact on clouds and climate. I have a comprehensive understanding of different measurement techniques as well as experience with uncertainty quantification, statistical analysis and atmospheric modelling. Additionally, I am interested in the social and environmental impact of climate change, especially of vulnerable population and ecosystems.

Steven Boeing

University of Leeds

Most of my work involves detailed simulations of cumulus clouds and their environment. I am interested in the influence of turbulence on cloud dynamics and rainfall, the ways in which precipitation invigorates convection, and novel computational methods. I have also been involved in the Yorkshire Integrated Catchment Solutions Programme (iCASP), where I worked on the evaluation of rainfall forecasts for surface water flooding with Ben Rabb, Cathryn Birch, Kay Shelton and others.

Mike Protts

Met Office

Mike is a Lightning Detection Engineer for Observations R&D at the Met Office. He is currently working on the firmware for the new lightning detection sensor, and investigating how additional atmospheric information can be extracted from the data captured.

Stefano Maffei

University of Leeds

I am a Research Fellow at the University of Leeds. My main purpose is to produce end-member forecast for the geomagnetic field that can inform and improve existing model to predict extreme space weather events. During my PostDoc (CU Boulder) and PhD (ETH-Zurich) I studied the rapidly rotating dynamics of the Earth’s outer core. I graduated in Physics at the University of Bologna with a master's thesis oceanographic data assimilation.

Chun Hay Brian Lo

University of Reading

Brian is a student at the University of Reading, completing his MSc degree in Atmosphere, Oceans and Climate. He will start his PhD coming October on "Detecting severe weather with radars for observations-based nowcasting" supervised by Dr Thorwald Stein, under the SCENARIO programme at the Department of Meteorology at the University of Reading. He is interested in learning more about how machine learning can be applied to nowcasting severe weather phenomena.

Ben Pickering

University of Leeds

I am an observational meteorologist finishing a PhD in evaluation of radar precipitation type products. My interests lie in novel observations such as using drones for vertical profiling, crowdsourcing observations from home weather stations and autonomous vehicles, computer vision to identify atmospheric features, and the assimilation of observations into dynamical or machine learning models.

Leif Denby

University of Leeds

MSci in Physics and MPhil Scientific Computing from Cambridge. PhD in Atmospheric Physics from Cambridge on studying dynamics of convective clouds with large-eddy simulations. Currently PDRA on GENESIS and EUREC4A-UK projects. Focussing in GENESIS on triggering of convection by studying coherent boundary layer structures which trigger clouds and in EUREC4A-UK on observational and modelling studies of coherent boundary layer structures and applying deep neural networks to studying convective organisation.

Daniel Galea

University of Reading

I am a PhD student at the University of Reading looking into applying Deep Learning techniques to meteorology, currently detecting weather phenomena in model data.

Georgy Ayzel

University of Potsdam

Post-doctoral research fellow at the University of Potsdam. I am interested in nowcasting and forecasting techniques in the field of hydrometeorology.

So Takao

University College London

So Takao is currently a research assistant at Imperial College London working on stochastic models for ocean modelling. He will be joining UCL as a research fellow in September to work on Machine Learning for Climate Science.

Maryna Lukach

NCAS, University of Leeds

Before I joined NCAS in February 2018, I worked extensively with weather radar data during my time at the Royal Meteorological Institute of Belgium (RMI) in 2011 - 2018. I obtained my PhD degree at the University of Antwerp, Belgium, in 2016. My doctoral research was focused on theoretical and applied aspects of the multivariate generalised rational interval interpolation with a direct application to the field of weather radar data analysis. My interest in atmospheric science lead me to complete the postgraduate studies program in Weather and Climate Modeling at the University of Ghent, Belgium, in 2012 - 2014. My other studies include a master degree in Computer Science from the University of Antwerp, Belgium (2006) and a master degree in Mathematics from Donetsk State University, Ukraine (1999).

Matt Clark

Met Office

Matt Clark has worked in Observations R&D at the Met Office since 2005. His work has focussed mainly on developing analysis techniques for novel observations, conducting instrument trials and inter-comparisons, assessing the impact of microclimate on temperature observations, and analysis of severe convective storms. Matt is currently studying for a PhD at the University of Leeds, under supervision of Prof. Doug Parker, on the topic of cold-frontal tornadoes.

Steve Tobias

University of Leeds

I am Professor of Applied Mathematics at Leeds University. I am interested primarily in fluid dynamics, primarily the interaction of turbulence and mean flows. I am also interested in using data-driven methods for model reduction, parameter estimation, prediction and sub-grid parameterisation.

Andres Diaz-Pinto

University of Leeds

Andres Diaz-Pinto received his B.Sc. In Electronic engineering at Pontificia Universidad Javeriana in Colombia. In 2012, he moved to Turin, Italy, where he started his MSc. in Telecommunications Engineering with the Doppia Laurea program at the Politecnico di Torino. During his MSc., he was a visiting researcher at the San Diego State University (SDSU) in the USA. During his PhD, he was a visiting researcher at the Friedrich-Alexander-Universitat Erlangen-Nurnberg, Pattern Recognition Lab, Germany and a visiting researcher at the Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB) in Sheffield, UK. In 2019, Andres was awarded his PhD Cum Laude at the Universitat Politecnica de Valencia where he was working on Computer Vision and Deep Learning techniques for automatic image classification and segmentation. His research interests are in the areas of Machine Learning, Deep Learning and Image analysis.

Dawn Harrison

Met Office

With a background in radar meteorology, Dawn currently leads the Observations Networks Design Team. She uses information about observations requirements, emerging capabilities and observations impacts to determine plans and priorities for the evolution of ground-based observing networks.

Rachel Prudden

Met Office Informatics Lab

Rachel is a Researcher in the Informatics Lab, with a focus on applying Machine Learning to Atmospheric and Earth Science. She is also undertaking a part-time PhD at the University of Exeter, investigating probabilistic super-resolution of weather models at convective scales.

Matthew Lehnert

Met Office

Matthew is a Deputy Chief Meteorologist at the Met Office and is part of the operational team responsible for delivering the UK's National Severe Weather Warnings Service, providing internal guidance to other meteorologists and advice on high impact weather to UK Government. He is also the Project Delivery Lead and SME for the Nowcasting Project which is tasked with improving 0-2-hour forecasts and warnings, initially around summertime thunderstorms.

Albert Klein Tank

Met Office

Albert is currently Director of the Met Office Hadley Centre for Climate Science and Services and holds a Professorship in Climate Services at Wageningen University, Netherlands. Before joining the Met Office he was Head of Research and Development for Observations and Data Technology at the Royal Netherlands Meteorological Institute (KNMI) where he worked for 25 years. Albert completed his PhD at Utrecht University, Netherlands, in 2004 on the topic of “Changing temperature and precipitation extremes in Europe’s climate of the 20th Century”. Albert has expertise in climate datasets, current and historical climate trends, and climate change scenarios for the future and has coordinated a number of EU science projects. He was a Coordinating Lead Author in Working Group I (WGI) of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report published in 2013, and a Lead Author in WGI of the IPCC Fourth Assessment Report published in 2007.

Alberto Arribas

Informatics Lab, Met Office

Alberto Arribas is a Met Office and Alan Turing Institute Research Fellow; the Head of Met Office Informatics Lab; and a Professor at the University of Exeter Institute of Data Science and AI.

The Informatics Lab is the major innovation department at the UK Met Office. It combines scientists, technologists and designers to make environmental science and data useful across multiple sectors. The team works with the likes of DeepMind, Microsoft and NASA to build prototypes and create new approaches and tools to solve problems.

In the past Alberto has led the development of world-leading weather and climate forecasting systems, published over 60 academic papers and been an editor for leading scientific journals, whilst lecturing and being a committee member for organisations such as the World Meteorological Organisation and the USA National Academy of Science.

Theo Economou

University of Exeter

I'm an applied statistician and hold a joint position between Uni. of Exeter and the Met Office. Interested in probabilistic approaches to nowcasting and forecasting, particularly in the context of big data.

Dr Saptarshi Das

University of Exeter

Saptarshi is an interdisciplinary AI and data scientist with background in control and power engineering. His present research interests include dynamical systems and control theory, big data analytics, machine learning, computational intelligence, signal processing, and fractional calculus in diverse applications in energy and environment. Since September 2017, he is a Lecturer in the Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, UK. He has co-authored 2 books and more than 95 research papers in refereed scientific journals, conferences and book chapters.

Henry Odbert

Met Office

Henry leads science development and support for Met Office Surface Transport Forecasting, including establishing the role of weather and climate information in the Future of Mobility. He is interested in ML applications that will exploit the increasing variety and volume of data for weather-related decision support, particularly as transport becomes more connected and more automated.

Tinkle Chugh

University of Exeter

Tinkle Chugh is Lecturer in Computer Science at the University of Exeter. His main research interests are in using different types of unconventional data sources in calibrating citizen weather stations, Bayesian optimisation and multiple criteria decision making.

Dr Xiaoyu Xiong

University of Exeter

Obtained PhD in Machine Learning at the University of Glasgow in 2017. Currently working on developing big data methods for improving windstorm footprint prediction at the University of Exeter. My research interests focus on using statistical and machine learning models as a tool for solving problems in the real world, such as making better decisions under uncertainty whilst taking account of the accuracy, efficiency and scalability of the models.

Ben Lloyd-Hughes

University of Reading

Ben is Lead Data Scientist at the Institute for Environmental Analytics, based at the University of Reading. He has worked on projects from tyre design through to insurance, construction and retail. Lately, he has been applying machine learning in the field of renewable energy generation and dispatch. Physicist, statistician, and programmer with a passion for sustainability and equality; Ben’s mission is to improve quality of life throughout the world via the application of knowledge.

Patrick McGuire

University of Reading

I currently work at the University of Reading on modeling land-surface processes. I worked for significant parts of my time from 1989-2005 on neural networks and machine learning for various applications, including the prediction of maps of atmospheric turbulence above astronomical telescopes a few milliseconds into the future.

Kieran Hunt

University of Reading

I am interested in using machine learning techniques to better forecast potential hydrological hazards over South Asia at a range of lead times.

Niall McCarroll

University of Reading

I'm currently working as a research software engineer at the University of Reading, Department of Meteorology. I have 20 years of experience working in industry on developing machine learning tools. I'm currently interested in the intersection of visualisation and ML/DL. I have a Ph.D in Computer Science from the University of Sheffield.

Maria Athanassiadou

Met Office – Informatics Lab

Maria is a senior research scientist with a background in Mathematics and Meteorology. She joined the Informatics Lab in Summer 2019 and has contributed to the Nowcasting project with DeepMind. Her diverse areas of expertise include gravity waves and complex flows around orography, wind tunnel measurements, atmospheric and dispersion modelling, air quality, large scale dynamics and satellites for environmental applications (volcanic plumes, plant diseases from space).

Alison Stirling

Met Office

Alison leads the ParaCon programme, which aims to improve the representation of convection in models.

Teil Howard

Met Office

I am Head of Science & Technology in the Met Office Business Group. This is the part of the Met Office that provides products and services to industry customers in sectors including aviation, marine, surface transport and energy.

Ryuichi Kanai

University College London

I am a PhD student at the statistical science department at UCL. My research field is about tsunami and earthquake.

Nachiketa Chakraborty

University of Reading

I work on causality, time-series studies and physics

Nawal Husnoo

Met Office

Kuan Li

University of Leeds