Virtual Seminar Series
The amount of digital data that exists in the world is growing at a rapid rate with data being generated continuously from multiple sources by companies, users and devices in a huge velocity, volume and variety. Targeted training in managing, analysing, and interpreting large, complex datasets and high rates of dataflow in areas such as Astrophysics and nuclear and particle physics is provided by this CDT on Big Data.
To offer the students (and its staff) an opportunity to broaden their horizons in big data science, LIV.DAT invites researchers from various other organisations to speak at our virtual seminar series. Here the students learn about Big Data challenges and applications outside their own focus area, as part of their continued development.
These virtual seminars are held using conferencing tool Zoom and are open to students, staff and anyone else who is interested. Recent seminars covered areas such as “cardiovascular risk prediction using big data” and “artificial intelligence in nuclear power generation applications”.
The details of scheduled and a number of past seminars are listed below.
Registration for the LIV.DAT Virtual Seminar Series - Autumn 2021 is now open. For more information and how to register please follow this link.
Tuesday 7 December 2021 | 15:00 (Europe/London) - Dr Wesley Tansey
Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center (USA)
"Modeling, testing, and adaptive experimental design in high-throughput cancer drug screens"
Tuesday 8 March 2022 | 13:00 (Europe/London) - Professor Rose Luckin
Learner Centred Design, University College London Knowledge Lab
"Is education AI-ready"
October 2021 - Dr Stefano Albrecht
Head of the Autonomous Agents Research Group, University of Edinburgh
“Deep Reinforcement Learning for Multi-Agent Interaction”
June 2021 - Dr Myriam Neaimeh
School of Engineering, Newcastle University & Data-Centric Engineering Group, Turing Institute
"Applying data science methods to modernise transport and electricity infrastructures"
May 2021 - Professor Shirley Ho
Cosmology X Data Science Group, Flatiron Institute, New York (USA) &
Department of Astrophysical Sciences, Princeton University (USA)
“Machine learning the Universe: Opening the Pandora Box”
April 2021 - Dr Anne O’Carroll
Remote Sensing Scientist, EUMETSAT, Darmstadt (DE)
“Combining satellite data with ocean surface measurements: Sea Surface Temperature (SST) observations”
March 2021 - Professor Stephen Fairhurst
School of Physics and Astronomy, Cardiff University & Data Intensive CDT (Cardiff, Bristol, Swansea)
“Analysis of gravitational waveforms to better understand black holes”
February 2021 - Professor Simon Maskell
Dept. of Electrical Engineering and Electronics, University of Liverpool
“SMC-Stan: A Scalable and Flexible Software tool for Better Bayesian Inference”
December 2020 - Dr Jana Kemnitz
Senior Data Scientist, Distributed-AI-Systems Research Group Siemens
“Industrial Data Science, Machine-, Transfer- and Federated Learning”
November 2020 - Professor Paul Watson
Computer Science and Director of the Digital Institute, Newcastle University
“A Declarative Approach to Distributed Stream Processing”
October 2020 - Professor Brant Robertson
Dept. of Astronomy and Astrophysics, University of California (UCSC)
“Morpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data”
July 2020 - Dr Graeme West
Department of Electronic and Electrical Engineering, University of Strathclyde
“Artificial Intelligence in nuclear power generation applications”
June 2020 - Dr Jessica Barret
MRC Biostatistics Unit, University of Cambridge
“Cardiovascular risk prediction using big data: A statistician’s perspective”
May 2020 – Dr Patrick Parkinson
Department of Physics and the Photon Science Institute, University of Manchester
“Big-data for nano-electronics”