Machine Learning Seminar

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.


2021.04.07Hussein RappelThe Alan Turing InstituteProbabilistic modeling and identification of intercorrelated random fields with bounds: Application to linear elastic struts and fibersdetails
2021.03.31Hichem OmraniLuxembourg Institute of Socio-Economic ResearchPredicting air pollution using remote sensing and sensors measurementdetails
2021.03.24Gabriele PozzettiCERATIZITAn Industry view on ML: CERATIZIT technology landscape and the art of collaborating with an industrial partner.details
2021.03.17Damian Mingo NdiwagoUniversity of LuxembourgUncertainty precision and reliability of Ecohydrological models: Bayesian model selectiondetails
2021.03.10Igor PoltavskyiUniversity of LuxembourgMachine learning for molecular simulationsdetails
2021.02.24Carlotta MontorsiLuxembourg Institute of Socio-Economic ResearchTree based algorithms: implementing Conditional Inference Trees and Forest for old age Frailty Index predictionsdetails
2021.02.17Minh Vu ChauUniversity of LuxembourgData-driven constitutive laws for hyperelasticity modeling in eigenspace using algorithmic differentiation coupling fenics pytorch model application to rvesdetails
2021.02.10Zhiqiang ZhongUniversity of LuxembourgReinforcement Learning based Meta-path Design for Heterogeneous Graph Neural Networkdetails
2021.02.03Saurabh Deshpande & Milad ZeraatpishehUniversity of LuxembourgPractical implementation session on Neural Networksdetails
2021.01.27Arif Sinan UsluUniversity of LuxembourgThe effect of self-paced neurofeedback on EEG learning: An experimental setupdetails
2021.01.20Milad ZeraatpishehUniversity of LuxembourgBayesian neural networks and MC Dropout; ways to measure uncertainty in Deep learningdetails
2021.01.13Lars BeexUniversity of LuxembourgUnsupervised learning to select modes for reduced-order models of hyperelastoplasticity: application to RVEs details
2020.12.16Erkan OterkusUniversity of StrathclydeA Physics-guided Machine Learning Model Based on Peridynamicsdetails
2020.12.09Eleni KoronakiUniversity of LuxembourgFrom partial data to out-of-sample parameter and observation estimation with Diffusion Maps and Geometric Harmonicsdetails
2020.12.02Vivek OOmmenIndian Institute of Technology MadrasThe effectiveness of PINNs in solving inverse heat transfer problemsdetails
2020.11.25Anina ŠarkićUniversity of LuxembourgMachine Learning in Wind Engineeringdetails
2020.11.18Diego KozlowskiUniversity of LuxembourgMachine Learning on graphsdetails
2020.11.11Tittu MathewIndian Institute of Technology MadrasBayesian uncertainty quantification and model selectiondetails
2020.11.04Arnaud MazierUniversity of LuxembourgDecision Trees methods, an overview of the white-boxesdetails
2020.10.28Cosmin AnitescuBauhaus-Universität WeimarMethods Based on Artificial Neural Networks for the Solution of Partial Differential Equationsdetails
2020.10.21Eleni KoronakiUniversity of Luxembourg“Dinky, Dirty, Dynamic & Deceptive Data (1)”: An overview of hybrid machine learning and equation-based modellingdetails
2020.10.14Saurabh DeshpandeUniversity of LuxembourgData Driven Hyper-elastic Simulationsdetails
2020.10.07Vasilis KrokosUniversity of Cardiff & Synopsys-SimplewareBayesian Neural Networks for uncertainty estimation on regression problemsdetails