The aim of the Machine Learning Seminar series is to harbor presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.
The seminar is aiming to bring together young and more experienced researchers from various disciplines and to exchange ideas on Machine Learning techniques. The seminar is run under the auspices of the DRIVEN PRIDE project that is funded by the FNR and led by Andreas Zilian, as well as the widening participation DRIVEN project funded by H2020 and coordinated by Stéphane Bordas. It also welcomes talks by researchers from a wider collaborative network, including, but not restricted to, early stage researchers in RAINBOW ITN as well as current and incoming individual Marie Skłodowska Curie fellows.
The usual format is the following: a short presentation (15-20min) followed by a longer discussion (30-40min). The usual time is Wednesdays, 10:00 a.m. (CET).
If you are interested to join, please contact Jakub Lengiewicz.
|2021.04.07||Hussein Rappel||The Alan Turing Institute||Probabilistic modeling and identification of intercorrelated random fields with bounds: Application to linear elastic struts and fibers||details|
|2021.03.31||Hichem Omrani||Luxembourg Institute of Socio-Economic Research||Predicting air pollution using remote sensing and sensors measurement||details|
|2021.03.24||Gabriele Pozzetti||CERATIZIT||An Industry view on ML: CERATIZIT technology landscape and the art of collaborating with an industrial partner.||details|
|2021.03.17||Damian Mingo Ndiwago||University of Luxembourg||Uncertainty precision and reliability of Ecohydrological models: Bayesian model selection||details|
|2021.03.10||Igor Poltavskyi||University of Luxembourg||Machine learning for molecular simulations||details|
|2021.02.24||Carlotta Montorsi||Luxembourg Institute of Socio-Economic Research||Tree based algorithms: implementing Conditional Inference Trees and Forest for old age Frailty Index predictions||details|
|2021.02.17||Minh Vu Chau||University of Luxembourg||Data-driven constitutive laws for hyperelasticity modeling in eigenspace using algorithmic differentiation coupling fenics pytorch model application to rves||details|
|2021.02.10||Zhiqiang Zhong||University of Luxembourg||Reinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Network||details|
|2021.02.03||Saurabh Deshpande & Milad Zeraatpisheh||University of Luxembourg||Practical implementation session on Neural Networks||details|
|2021.01.27||Arif Sinan Uslu||University of Luxembourg||The effect of self-paced neurofeedback on EEG learning: An experimental setup||details|
|2021.01.20||Milad Zeraatpisheh||University of Luxembourg||Bayesian neural networks and MC Dropout; ways to measure uncertainty in Deep learning||details|
|2021.01.13||Lars Beex||University of Luxembourg||Unsupervised learning to select modes for reduced-order models of hyperelastoplasticity: application to RVEs||details|
|2020.12.16||Erkan Oterkus||University of Strathclyde||A Physics-guided Machine Learning Model Based on Peridynamics||details|
|2020.12.09||Eleni Koronaki||University of Luxembourg||From partial data to out-of-sample parameter and observation estimation with Diffusion Maps and Geometric Harmonics||details|
|2020.12.02||Vivek OOmmen||Indian Institute of Technology Madras||The effectiveness of PINNs in solving inverse heat transfer problems||details|
|2020.11.25||Anina Šarkić||University of Luxembourg||Machine Learning in Wind Engineering||details|
|2020.11.18||Diego Kozlowski||University of Luxembourg||Machine Learning on graphs||details|
|2020.11.11||Tittu Mathew||Indian Institute of Technology Madras||Bayesian uncertainty quantification and model selection||details|
|2020.11.04||Arnaud Mazier||University of Luxembourg||Decision Trees methods, an overview of the white-boxes||details|
|2020.10.28||Cosmin Anitescu||Bauhaus-Universität Weimar||Methods Based on Artificial Neural Networks for the Solution of Partial Differential Equations||details|
|2020.10.21||Eleni Koronaki||University of Luxembourg||“Dinky, Dirty, Dynamic & Deceptive Data (1)”: An overview of hybrid machine learning and equation-based modelling||details|
|2020.10.14||Saurabh Deshpande||University of Luxembourg||Data Driven Hyper-elastic Simulations||details|
|2020.10.07||Vasilis Krokos||University of Cardiff & Synopsys-Simpleware||Bayesian Neural Networks for uncertainty estimation on regression problems||details|