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 is 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

2020

• 14th January 2020: Data Assimilation using Second-Order Sensitivities - proposed by Zainab Titus

• 21st January 2020: group discussion about Non-intrusive reduced order modeling of nonlinear problems using neural networks

• 28th January 2020: group discussion about Deep Fluids: A Generative Network for Parameterized Fluid Simulations

• 4th February 2020: Human in the loop: Design with Machine Learning - proposed by Pan Wang

• 11th February 2020: group discussion about Low-dimensional recurrent neural network-based Kalman filter for speech enhancement

• 18th February 2020: group discussion about End-to-end Optimized Image Compression with Attention Mechanism

• 25th February 2020: group discussion about Deep Kalman Filters

• 3rd March 2020: group discussion about SD-GAN: Structural and Denoising GAN 

• 10th March 2020: Can we use machine learning to predict global patterns of climate change?  - proposed by Laura Mansfield 

• 17th March 2020 to 8th September 2020: The meetings have been cancelled because of Covid-19

• 15th September 2020: Machine learning fluid dynamics modelling for urban air pollution - Latent GAN - proposed by Jamal Afzali  

• 22nd September 2020: Improving Econophysical Systems for blockchain and cryptocurrencies using Data Assimilation - by Pratha Khandelwal

• 29th September 2020: Urban air pollution forecasts generated from latent space representation - by César Quilodrán

• 6th October 2020: Increasing the visibility of low-voltage networks through data analytics - by Ronald Monterroso

• 13th October 2020: Artificial Neural Network at the service of Data Assimilation (and vice versa)  - by Rossella Arcucci

• 20th October 2020: Correcting public opinion trends through machine learning and data assimilation - by Robin Hendrickx

• 27th October 2020:  Predicting the spatial variation of COVID-19 infections using generative adversarial networks  - by Yaqi Li and Applying Convolutional Neural Networks to Data on Unstructured Meshes - by Yuling Li 

• 3rd November 2020: Unstructured Convolutional Autoencoders for Big Data Assimilation - by Maxime Redstone Leclerc

• 10th November 2020: GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series - by Edward De Brouwer  (Recorded Meeting Available)

• 17th November 2020: How self-organizing maps can help us understand atmospheric blocking - by Carl Thomas (Imperial College London) (Recorded Meeting Available)

• 24th November 2020: Error analysis of reduced-order modelling  - by Dunhui Xiao (Swansea University) (Recorded Meeting Available)

• 1st December 2020: Differentiable Physics Simulations for Deep Learning Algorithms -  by Nils Thuerey (TUM) (Recorded Meeting Available )

• 15th December 2020: Predicting multidimensional data via tensor learning - by Giuseppe Brandi (King's College London) (Recorded Meeting Available)

2021

• 5th January 2021: Machine learning for weather predictions - by Peter Dueben (ECMWF)

• 19th January 2021: Accelerated Gaussian Convolution in a Data Assimilation scenario  - by Pasquale De Luca (University of Salerno) (Recorded Meeting Available)

• 26th January 2021: A Neural Implementation of the Kalman Filter - by Robert Wilson (University of Arizona) (Recorded Meeting Available) 

• 2nd February 2021: Graph-Based Generative Adversarial Networks for Data Generation in High Energy Physics  - by Raghav Kansal (UC San Diego) (Recorded Meeting Available)

• 9th February 2021: Turbulence Enrichment with Physics-informed Generative Adversarial Network - by Akshay Subramaniam (NVIDIA) (Recorded Meeting Available) 

• 16th February 2021: NVIDIA SimNet: an AI-accelerated multi-physics simulation framework - by Oliver Hennigh (NVIDIA) (Recorded Meeting Available)

• 23th February 2021: Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution - by Matteo Bohm (Sapienza University of Rome) (Recorded Meeting Available)

• 2nd March 2021: Towards self-adaptive building energy control in smart grids - by Juan Gómez Romero (Universidad de Granada) (Recorded Meeting Available)

• 9th March 2021: Towards practical global field reconstruction from sparse sensors with deep learning - by Kai Fukami (UCLA) (Recorded Meeting Available)

• 16th March 2021: Utilization of autoencoder-based nonlinear manifolds for fluid flow forecasts driven with long short-term memory - by Taichi Nakamura (Keio University) (Recorded Meeting Available )

• 23rd March 2021: Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility - by Cristóbal Pais Martínez (UC Berkeley) (Recorded Meeting Available )

• 30th March 2021: The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks - by Salva Rühling Cachay (TU Darmstadt) (Recorded Meeting Available)

• 13th April 2021: Deep Learning Predictive Modelling in Combination with Data Assimilation and Applications to Geophysical Dynamics - by Fangxin Fang (ICL) (Recorded Meeting Available)

• 20th April 2021: Rare events and their optimization - by Vishwas Rao (Argonne National Laboratory) (Recorded Meeting Available)

• 27th April 2021: MLDADS 2021 Webinar - Alberto Racca (MLDADS 2021 Videos Available)

• 4th May 2021: A Tale of Three Implicit Planners and the XLVIN agent - by Petar Veličković (DeepMind) (Recorded Meeting Available)

• 11th May 2021: Identify the dynamics of climate models using data assimilation and analog predictions - Pierre Tandeo (IMT Atlantique) (Recorded Meeting Available)

• 18th May 2021: MLDADS 2021 Webinar - Alban Farchi and Dennis Knol (MLDADS 2021 Videos Available)

• 25th May 2021: MLDADS 2021 Webinar - Blas Ko and Ahn Khoa (MLDADS 2021 Videos Available)

• 1st June 2021: MLDADS 2021 Webinar - Pasquale de Luca and Muzammil Hussain Rammay (MLDADS 2021 Videos Available)

• 8th June 2021: Data-driven optimization for systems engineering - by Antonio Del Rio Chanona (Imperial College London) (Recorded Meeting Available)

• 15th June 2021: Learning Physical Simulations with Graph Networks - by Álvaro Sánchez González (DeepMind) (Recorded Meeting Available)

• 22nd June 2021: Generative model-based super-resolution and quality control for cardiac segmentation - Shuo Wang (Fudan University) (Recorded Meeting Available)

• 29th June 2021: SliceGAN: Generating 3D structures from a 2D slice with GAN-based dimensionality expansion - Steven Kench (Imperial College London) (Recorded Meeting Available)

• 14th September 2021: Coupling of deep neural networks and physical invariants for turbulent flow surrogate modeling - Didier Lucor (CNRS, Paris-Saclay University) (Recorded Meeting Available)

• 21st September 2021: Physical inductive biases for learning simulation and scientific discovery - Peter Battaglia (DeepMind) (Recorded Meeting Available)

• 28th September 2021: The importance of discretization drift in deep learning -  Mihaela Rosca (DeepMind) (Recorded Meeting Available)

• 12th October 2021: Martian Atmosphere Reconstruction through a Long Short-Term Memory Network - Davide Amato (ICL) (Recorded Meeting Available)

• 26th October 2021: Adversarial Perturbations in the wild and their applications - Stefano Marrone (UNINA) (Recorded Meeting Available)

• 2nd November 2021: Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels - Yi Zhou (Adobe research) (Recorded Meeting Available)

• 9th November 2021: Exactly solvable models for high-dimensional machine learning problems - Bruno Loureiro (EPFL) (Recorded Meeting Available)

• 16th November 2021: Data-driven and learning-based approaches for the modeling, forecasting and reconstruction of geophysical dynamics - Said Ouala (IMT Atlantique) (Recorded Meeting Available)

• 23rd November 2021: Statistical physics of stochastic gradient descent - Francesca Mignacco (EPFL) (Recorded Meeting Available)

• 30th November 2021: Assisting Sampling with Learning: Adaptive Monte Carlo with Normalizing Flows  - Marylou Gabrié (NYU/Flatiron Institute) (Recorded Meeting Available)

• 7th December 2021: Bridging Data Assimilation and Deep Learning - Arthur Filoche (Sorbonne University) 

2022

• 11th January 2022: OceanIA: AI and machine learning for understanding the ocean and climate change - Luis Martí (Inria Chile) (Recorded Meeting Available)

• 18th January 2022: Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics - Steven Brunton (University of Washington) (Recorded Meeting Available)

• 25th January 2022: Twitter as an alternative data source for international migration studies - Jisu Kim (Max Planck Institute) (Recorded Meeting Available)

• 1st February 2022: The Future of Finance and Economics: The crossroad between Models, Data, and Artificial Intelligence - Irena Vodenska (Boston University) (Recorded Meeting Available)

• 8th February 2022: The frontier of Simulation-Based Inference - Gilles Louppe (University of Liege) (Recorded Meeting Available)

• 15th February 2022: Bridging the gap between simulations and real data - domain adaptation for deep learning in physics and astronomy - Aleksandra Ćiprijanović (FNAL) (Recorded Meeting Available)

• 22nd February 2022: Useful Inductive Biases for Deep Learning in Molecular Science - Max Welling (University of Amsterdam, Microsoft Research) (Recorded Meeting Available)

• 1st March 2022: Predicting material properties with the help of machine learning - Bingqing Cheng (Institute of Science and Technology Austria) (Recorded Meeting Available)

• 8th March 2022: Graph Neural Networks for Charged Particle Reconstruction at the Large Hadron Collider -  Savannah Thais (Princeton University) (Recorded Meeting Available)

• 15th March 2022: The importance of vegetation and drought for global fire prediction - Alexander Kuhn-Regnier (Imperial College London) (Recorded Meeting Available)

• 29th March 2022: Gaussian processes, missing data, and optimal transport -  Felipe Tobar (Universidad de Chile) 

• 5th April 2022: Bayesian Inference in Physics-Based Nonlinear Flame Models  - Maximilian Croci (University of Cambridge)

• 10th May 2022: Coreo-Graph - Mariel Pettee (Lawrence Berkeley National Laboratory)

• 17th May 2022: Tackling Fairness, Change, and Polysemy in Word Embeddings - Felipe Bravo (Universidad de Chile) (Recorded Meeting Available)

• 24th May 2022: Augmenting the prediction of extubation failure using measures of complexity - Sandip Varkey George (UCL) (Recorded Meeting Available)

• 31st May 2022: Physics-Informed Deep Learning: Learning from Small Data - Lu Lu (University of Pennsylvania ) (Recorded Meeting Available)

• 13th September 2022: Physics-Informed Neural Networks in Medicine - Marta Varela Anjari (ICL) (Recorded Meeting Available)

• 20th September 2022: Interpretable and structure-preserving data-driven methods for physical simulations - Youngsoo Choi (Lawrence Livermore National Laboratory)(Recorded Meeting Available)

• 27th September 2022: Ultrasound Imaging Synthesis - Miguel Xochicale (King's College London)

• 4th October 2022: Autoregressive long-context music generation with Perceiver AR - Cătălina Cangea (DeepMind) (Recorded Meeting Available)

• 11th October 2022: Rigid & Non-Rigid Multi-Way Point Cloud Matching via Late Fusion - Tolga Birdal (Imperial College London/Stanford University)

• 18th October 2022: Overview of machine learning approaches at GSK.ai - Emma Slade (GSK) (Recorded Meeting Available)

• 25th October 2022: Topological Deep Learning - Cristian Bodnar (University of Cambridge/Microsoft)

• 1st November 2022: Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems - Romit Maulik (Argonne National Laboratory)

• 8th November 2022: Storage Policy in Continual Learning: Different Approaches for Different Scenarios -  Julio Hurtado (University of Pisa) (Recorded Meeting Available)

• 22nd November 2022: Equivariant ML from classical physics - Soledad Villar (John Hopkins University) (Recorded Meeting Available)

• 29th November 2022: How Does Amazon Alexa work? - Asmita Poddar (Amazon)

• 13th December 2022: Causal Representation Learning - Johann Brehmer (Qualcomm) (Recorded Meeting Available)

2023

• 17th January 2023: Text-to-Speech @ Amazon Alexa -  Ariadna Sánchez (Amazon)

• 31st January 2023: State-of-the-art AI driven Solar PV Generation Forecasts - Jacob Bieker (Open Climate Fix) (Recorded Meeting Available)

• 7th February 2023: Exploring AI's Role in Fine Art: An Introduction to Explainable Fine Art - Yunfei Fu (iArt) (Recorded Meeting Available)

• 21st February 2023: Graph Representation learning for street networks - Mateo Neira Álvarez (UCL) (Recorded Meeting Available)

• 28th February 2023: Exploring chemical reactions through automation and machine learning - Fernanda Duarte (University of Oxford)

• 7th March 2023: Towards Better Understanding of Contrastive Learning - Yuandong Tian (Meta) (Recorded Meeting Available)

• 14th March 2023: The Role of Data and ML in Enabling Flexible Clean Energy Resources - Utkarsha Agwan (UC Berkeley) (Recorded Meeting Available)

• 21st March 2023: Auto Arborist: Towards Mapping Urban Forests Across North America - Sara Beery (MIT) (Recorded Meeting Available)

• 28th March 2023: Machine Learning in Climate Action - David Rolnick (McGill University/Mila - Quebec AI Institute) (Recorded Meeting Available)

• 25th April 2023: Optimization-in-the-loop ML for energy and climate - Priya Donti (MIT, Climate Change AI)

• 16th May 2023: Bringing Social Robots into the Home for Long-term Interactions - Nicole Salomons (Imperial College London)

• 23rd May 2023: Use of Knowledge Graph in Alexa - Elizabeth Kwan (Amazon)

• 30th May 2023: Divide (or convolve) and Conquer: Data Comparison with Wiener Filters - Lluis Guasch (Imperial College London)

• 6th June 2023: Weather Forecasting using Deep Learning - A paradigm shift - Lasse Espeholt (Google Deepmind)

• 13th June 2023: WarpPINN: Cine-MR image registration with physics-informed neural networks - Francisco Sahli (Universidad Católica de Chile)

• 20th June 2023: Data-driven Modeling of Unknown Systems with Deep Neural Networks - Dongbin Xiu (The Ohio State University)

• 19th September 2023: Diffusion models for Cultural Heritage Restoration - Lucía Cipolina Kun (Bristol University)

• 26th September 2023:  Neuromancer: Differentiable Programming Library for Data-driven Modelling and Control - Jan Drgona (PNNL)

• 3rd October 2023: Tackling Climate Change with Autonomous Seaweed Farms Hitchhiking on Ocean Currents - Marius Wiggert (UC Berkeley)

• 10th October 2023: Reparametrization invariance in representation learning - Søren Hauberg (Technical University of Denmark)

• 17th October 2023: Is there a place for Representation Learning in Generative AI? - Jakub Tomczak (Eindhoven University of Technology)

• 24th October 2023: CO2 Geological Storage Modeling with Machine Learning - Gege Wen (Stanford University - Imperial College London)

• 31st October 2023: Probabilistic Circuits: Deep Probabilistic Models with Tractable Inference - Robert Peharz (TU Graz)

• 7th November 2023: Beyond compound libraries: A GNN-based approach for phenotype-driven lead design - Guadalupe González (Roche)

• 14th November 2023: Causal Machine learning with uncertainty in Directed Acyclic Graph (DAG) space -  Wenbo Gong (Microsoft Research)

• 21st November 2023: ESA Fellows

• 5th December 2023:         Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering - Guang Lin (Purdue University)

2024

• 16th January 2024: Natural Language Processing for Under-resourced African Languages - David Ifeoluwa Adelani (UCL)

• 23rd January 2024: Constructing custom thermodynamics using deep learning - Qianxiao Li (National University of Singapore)

• 6th February 2024: Fantastic ML x Biology Problems and Where to Find Them - Simon Kohl (Latent Labs)

• 13th February 2024: Title TBC - Sixin Zhang (University of Toulouse)

• 27th February 2024: Explainable Text Classification Framework for Detecting Indicators of Forced Labour - Erick Méndez Gúzman (University of Manchester)

• 12th March 2024: life2vec: Life trajectories in high dimensional spaces - Germans Savcisens (Technical University of Denmark)

• 19th March 2024: Data assimilation Networks - Tao Zhou (Chinese Academy of Sciences)

• 9th April 2024: Title TBC - Johannes Brandstetter (Johannes Kepler University)

• 16th April 2024: Title TBC - Jianzhi Dong (Tianjin University)

• 23rd April 2024: ClimateSet - Julia Kaltenborn (MILA - McGill University)

• 30th April 2024: Title TBC - Leonardo Zepeda Nuñez (Google)

• 7th May 2024: Title TBC - Kate Hodesdon (StabilityAI)

• 14th May 2024: Title TBC - Jesper Dramsch (ECMWF)

• 21st May 2024:

• 28th May 2024: Title TBC - Hannes Stark (MIT)

• 4th June 2024:

• 11th June 2024:

• 18th June 2024:

• 25th June 2024: