Annual workshops

Every year, the DataLearning group organises a workshop on Machine Learning and Data Assimilation for Dynamical Systems (MLDADS), as part of the International Conference on Computational Science (ICCS).

Read more about past editions of the MLDADS workshops below:
London - ICCS 2022
Poland - ICCS 2021
Amsterdam - ICCS 2020
Faro, Portugal - ICCS 2019

 

Weekly meetings

The DataLearning group has weekly meetings (Tuesdays, 4pm GMT), with invited speakers. The meetings are open to everyone - join the mailing list for more information.

See the upcoming event calendar here and view past topics below.

Weekly meeting tabs

2019

  • 19th March 2019: Kickoff Meeting

  • 26th March 2019: Neural Network technologies used for fake news detection - proposed by Julio C Amador Díaz López

  • 2nd April 2019: Fast data assimilation and forecasting the motion of the ocean - proposed by César A Quilodrán Casas

  • 9th April 2019: Integrating Semantic Knowledge to Tackle Zero-shot Text Classification - proposed by Jingqing Zhang

  • 16th April 2019: Adversarial Perturbations in the wild and their applications - proposed by Stefano Marrone

  • 7th May 2019: How to organise Deep Learning research - proposed by Mihai Suteu

  • 14th May 2019: What your network looks like? - proposed by James A Scott-Brown

  • 21st May 2019: Group discussion about the Kalman filter

  • 28th May 2019: 3D Variational DA and Neural Network - proposed by Robin Evers and Lamya Moutiq

  • 4th June 2019: Group discussion about Neural Ordinary Differential Equations

  • 11th June 2019: The DataLearning working group was in Faro (Portugal) for the first MLDADS 2019 workshop at ICCS

  • 18th June 2019: Group discussion about Machine Learning: Deepest Learning as Statistical Data Assimilation Problems

  • 25th June 2019: Optimizing Artificial Neural Networks by using Evolutionary Algorithms for Energy Consumption Forecasting - proposed by Luis Baca Ruiz

  • 2nd July 2019: Group discussion about Simulation-Based Optimization Frameworks for Urban Transportation Problems

  • 9th July 2019: Optimal sensors positioning using Gaussian Processes - proposed by Tolga Dur and Gabor Tajnafoi

  • 16th July 2019: Group discussion about Fixed rank kriging for very large spatial data sets

  • 23rd July 2019: A novel approach to monitor blood glucose using non-invasive body parameters - proposed by Shad A Asinger and Changavy Kajamuhan

  • 30th July 2019: Active network management in low-voltage networks using high-resolution substation data - proposed by Julio Perez Olvera

  • 17th September 2019: Group discussion about Bayesian Statistics in Machine Learning

  • 24th September 2019: Data assimilation technologies for parameter estimation - proposed by Philip Nadler

  • 1st October 2019: Convex Optimization for Parallel Energy Minimization - proposed by Sesh Kumar

  • 8th October 2019: Group discussion about Data assimilation as a deep learning tool to infer ODE representations of dynamical models

  • 15th October 2019: Effective Data Assimilation - proposed by Rossella Arcucci

  • 22nd October 2019: Inferring the unknown: Unifying statistical pre- and post-processing in meteorology with amortized variational inference - proposed by Tobias Finn

  • 29th October 2019: Group discussion about Generalisability of deep models

  • 5th November 2019: Group discussion about FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

  • 12th November 2019: Group discussion about Fine-Tuning Deep Neural Networks in Continuous Learning Scenarios

  • 19th November 2019: Graph Drawing by Stochastic Gradient Descent - proposed by Jonathan Zheng

  • 26th November 2019: Group discussion about tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow

  • 10th December 2019: Domain Decomposition Autoencoder - A neural network for compressing large datasets - proposed by Toby Phillips