​We run a seminar series welcoming talks covering a broad range of topics in machine learning and its applications to science from both internal and external speakers. To signup for email alerts and joining details for upcoming seminars please contact Samuel Jackson​​.​

The seminars run on Thursdays at 13:00 and can be accessed through Zoom.

Upcoming Seminars

• 23/03/23 at 1pm - Dr. Guy R. Davies, University of Birmingham - Hierarchically modelling many stars - methods built on machine learning emulation and probabilistic programming techniques.
  
• 30/03/2023 at 3pm - Dr. Mathew Cherukara, Advanced Photon Source - HPC+AI@Edge Enabled Materials Characterization
  
• 27/04/23 - Dr. Hatem Helal, Graphcore - TBC
  
• 11/05/23 - Dr. Ligang He, University of Warwick - ​TBC
  

Past Seminars

• Professor Thia Kirubarajan, University of McMaster – Learning to track and tracking to learn
  
• Dr Jeyan Thiyagalingam – STFC –  Tech  updates
  
• Dr Keith Butler – STFC – Describing a crystal to a computer
• Dr Tyrone Rees – STFC – Introduction to regularisers
• Dr Nate Barlow – Imperial College – Machine learning for phase discrimination in lipid systems
• Dr Melanie Vollmar – Diamond Light Source – Machine learning for molecular crystallography
• Carolina Cuesta-Lazaro  and Arnau Quera-Bofarull  – University of Newcastle – Xnet for medical image analysis
• Dr Sanghamitra  Mukhopadhyay – ISIS – Bayesian analysis of QENS data
• Dr Albert Bartok-Paratay – STFC/University of Warwick – Gaussian approximation potentials
• Dr Dhammika Widanalage – University of Warwick – Machine learning for battery life prediction
• Maria Vicente, Dr Bryan Carpenter, and Professor Mark Sullivan – University of Southampton – Using machine learning for analyzing telescope data
• Dr Jola Mirecka – STFC – Deep and shallow learning for Cryo-EM
• Dr Chris Allen – Diamond Light Source/Oxford University – Machine learning and electron microscopy
• Dr Jens Jensen – STFC - Low-rank matrix completion and ML methods
• Dr Tingting Mu – University of Manchester – Representation learning and  applications
• Samuel Jackson – STFC – Cloud masking: A machine learning approach
• Dr Andrew McCluskey – Diamond Light Source/University of Bath - Bayesian analysis of SAXS
• Dr Keith Butler – STFC – Understanding inelastic neutron scattering with deep neural networks
• Dr Kazuki Morita – Imperial College - Machine learning dielectric properties of condensed matter
• Dr Felix Zhou – Oxford University - Semantic segmentation of cells
• Professor Ofer Lahev – University College London – Machine learning for astronomy
• Dr Stephen McGough – University of Newcastle – How can machine learning improve simulation
• Dr Volker Deringer – Oxford University – Gaussian process modelling of materials
• Dr Anders Markvardsen – ISIS – A Bayesian tutorial
• Patrick Austin – STFC – Denoising electron microscopy data
• Professor Shallom Lappin – Kings College – Natural language processing
• Professor Richard Graham – University of Nottingham – Gaussian processes for interatomic potentials
• Dr Kuangdai Leng – STFC/Oxford University – Machine learning for geophysics
• Dr Muhammad Firmansyah Kasim – Oxford University – DENSE neural network search
• Dr Joost van Amersfoort – Oxford University – Deep uncertainty quantification
• Dr Jari Fowkes – STFC/Oxford university - Bayesian generative models for probabilistic pattern mining and source code summarisation​
  
• Dr Tom Begley - Faculty.ai - causation and Correlation in Machine Learning
  
• Dr. Daniel Davies - UCL - Machine Learning for Materails Screening Studies
  
• Dr. Vignesh Gopakumar - Culham Centre for Nuclear Fusion - Machine Leanring for Imaging in Fusion Reactors
  
• Dr. Maxim Ziatdinov - Oak Ridge National Laboratory - Machine Learning Guided Microscopy
  
• Dr. Felipe Oviedo - Microsoft - Physics Informed ML for Materials Design
  
• Dr. Xiaogang Yang - DESY - Tomogrpahic Reconstruction with a GAN
  
• Mr. Donovan Webb - University of Bath - The CryoEM Pipeline and ML
  
• Dr. Tianran Zhang - Kings College London - From detection to forecasting, how machine learning can improve satellite wildfire research
  
• Drs. James Walsh and Neil Dhir - Alan Turing Istitute - Project Odysseus and Exiting Lockdown
• Dr. Kasaara Hosseini - Alan Turing Institute - Living With Machines
• Dr, Daniel Pelt - CWI Amsterdam - Mixed Scale Dense Neural Networks for Scientific Imaging
• Dr. Dani Ushizima - Lawrence Berkeley National Laboratory - Machine Learning and Data Analytics: polymer films and beyond
  
• Dr. Antony Vamvakaros - Finden Ltd. - Machine Learning for X-ray Tomography
  
• Dr. Alexandre Szenicer - University of OXford - Machine learning across the spatial scales in geophysics: from tracking elephants to fixing satellites
  
• Dr. Alan Lowe - UCL - Learning the Rules of the Cellular Game
  
• Dr. Carlos Oscar Sorazo - National Centre ofr Biotechology (CNB) - Machine learning algorithms for image processing in CryoEM
  
• Dr. Julian Zimmerman - ETH Zurich - Machine Learning for Diffractive Imaging
  
• Drs. Dima Molodenskiy and Alexey Kikhney - EMBL Hamburg - Application of neural networks to small angle scattering data analysis
  
• Dr. Simon Knowles - Graphcore - The architecture of Graphcore's evolved "Colossus" IPU and its value in real applications
  
• Dr. Nathanial Trask - Sandia National Laboratory - Structure preserving deep learning architectures for SciML
  
• Mr. Siu Lun (Jason) Yeung and Samuel Jackson - SciML - Machine Learning for Pulse Shape Discrimination
  
• Dr, Kai Han - University of Bristol - Semantic and Geometric Learning for Holistic Visual Understanding
• Dr. Ben Moseley - University of Oxford - Physics-informed neural networks: their advantages, shortcomings and the latest ideas in the field
  
• Prof. Ross King - Chalmers University/Alan Turning Institute - Automating Science 
• Dr. Attila Szabo, University of Oxford - Neural Wavefunctions and the Sign Problem
•  Prof. Tony Hey - Opportunities and Challenges from AI and ML for the Advancement of Science, Technology and the Missions of the US Department of Energy’s Office of Science
  
•  Dr. Tamilarasan Sabapathy
• Dr. Ce Zhang
  
•  Prof. Charlotte Deane - University of Oxford ​
  
•  Dr. Sony Malhotra - SciML 

•  Dr. Teodoro Laino - IBM Research 
  

•  Dr. Phil Maffettone - Brookhaven National Lab 
  
• Dr. Saiful Khan - University of Oxford
• Dr. Shijing Sun - Massachusetts Institute of Technology 
  
• Dr. Nong Artrith - Utrecht University 
  
• ​​Mr. Matt Amos - Lancaster University
  
• Dr. Tony Hey, SciML, STFC - HPC-AI Convergence: Exascale Computing and AI for Science
• Dr. Shantenu Jha, Brookhaven National Laboratory- Zettascale computing on Exascale Platform
  

•  Dr. Mallikarjun Shankar​, Oak Ridge National Laboratory -  Frontiers of Supercomputing and Scalable Patterns for Deploying Learning

• Prof. Jason McEwen​, University College London - ​Geometric deep learning on the sphere: spherical CNNs and scattering networks
• ​Dr. Joe Zuntz​, University of Edinburgh - The LSST-DESC 3x2pt Tomography Optimization Challenge​
• Dr. Line Pouchard, Brookhaven National Laboratory- FAIR Data and Reproducibility for AI Applications in Scientific Research​
• Dr. Kamal Choudhary, ​​NIST, USA - NIST-JARVIS Infrastructure to Enable Deep Learning and Quantum Computation Methods for Improved Materials Design​​
• Dr. Scott Hosking, BAS - AI for Prediction and Digital Twinning of the Polar Regions.
• Dr. Rama Vasudevan, Oak Ridge National Laboratory​​ - Automated and autonomous experiments: where machine learning, computing, and simulations can couple to produce ‘smart’ characterization tools of the future
• Dr. ​Peter Steinbach, Helmholtz Zentrum Dresden​​​​ - ​Neural Posterior Estimation for Inverting Beamline Simulations - A report of a self-guided discovery.​
  

• Prof. David Hogg​, University of Leeds, UK - ​Applications of Machine Learning

• Prof. Zachary Ulissi​, Carnegie Mellon University, USA - Continued Progress towards Generalizable Machine Learning Models in Computational Catalysis

• Prof. ​Reinhard Maurer, University of Warwick, UK - Deep learning surrogates of electronic structure for quantum dynamics and molecular design

• Prof. Ingo Waldmann​, UCL, UK 

• Dr. Luca Gelisio, European XFEL, Germany - Data analysis at the European XFEL

• Dr. Luca M. Ghringelli​, Fritz Haber Institute of the Max Planck Society, Germany
  

• Dr. Murali Emani​, Argonne National Laboratory, USA - Advancing Next Generation Scientific Applications with Novel AI Accelerators

• ​Dr. Yong Liu​, Plunk Inc., USA - Scalable Machine Learning in AI Startups: Success Stories and Lessons Learned

• Dr. Rosa Badia,  Barcelona Supercomputing Center - HPC, data analytics and AI: enabling their convergence through workflow methodologies

• Dr. Sutanay Choudhury, Pacific Northwest National Laboratory - Fast and Accurate Prediction of Potential Energy Functions for 3D Molecular structures.

• Professor. Jonathan Hirst, University of Nottingham - Machine learning for sustainable chemistry