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Federico Deuschle

Federico studied Acoustic Engineering at the Universidad Nacional de Tres de Febrero (UNTREF) in Buenos Aires, Argentina.

He obtained his diploma on Acoustics engineering in 2018, and started to work in automotive industry as NVH test engineer to analyze different NVH solutions for powertrain, brakes and tire noise issues.

Currently, he is employed by Siemens Industry Software NV as a Research Engineer and enrolled as PhD student at KULeuven. He has joined as a part of the Marie Curie Fellowship for the MOIRA Project to apply AI/ML techniques for sensor anomaly detection during testing for several industries, such as, automotive, aerospace and wind turbines.

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Hamid Shiri

Hamid Shiri studied Mechanical Engineering at Shiraz University. He obtained his Bachelor in 2016 and his Master in 2019. He performed his Master thesis activity at Dynamics and Vibration laboratory of Shiraz University, where he worked on the development of Condition monitoring of rotating systems based on acoustic and vibration signals by using data fusion.

Currently, he is  enrolled as PhD researcher at Wroclaw University of Science and Technology(WUST) in the Faculty of Geoengineering, Mining and Geology. Also, He has joined the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA) as Marie Curie Early Stage Researcher to develop novel modeling / analysis / separation approaches for long-term, big Data, heterogeneous processes for condition monitoring with a focus on non -stationary operation and heavy-tailed, non-Gaussian noise .

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Deepti Shriram Kunte

Deepti completed her Bachelor’s in Mechanical Engineering from Maharashtra Institute of Technology, India in 2016. She then worked for two years at Mahindra and Mahindra Ltd., India as a Design Engineer. She graduated with Master’s in Machine Design from KTH Royal Institute of Technology, Sweden in 2020. Her Master thesis involved prediction of subjective sound quality using neural networks.

Deepti is currently working as an Early Stage Researcher with Siemens Industry Software NV, Belgium as a part of the European Training Network on Monitoring Large-Scale Complex Systems. She is working on end-of-line quality testing. She will investigate the use of transfer learning techniques for the same.

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Douw Marx

Douw obtained his Bachelor’s (2018) and Master’s (2020) degrees in Mechanical Engineering from the University of Pretoria, South Africa. His Master’s research at the Centre for Asset Integrity Management at the University of Pretoria focused on the hybrid diagnostics and prognostics of planetary gearboxes.

After the completion of his Master's degree, he worked in industry to develop methods for electrode length prediction in electric arc furnaces.

Douw is currently appointed as a Marie Curie Early Stage Researcher at LMSD, KU Leuven. His research interests as part of the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA) include developing novel techniques for the extraction and exploitation of hidden information in heterogeneous data.

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Jenny YANG

Jenny is a DPhil student at Balliol College, University of Oxford, and a Marie Curie Early Stage Researcher. As part of the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA) she will carry out machine learning research for analysing electronic health record (EHR) data, including time-series of physiological data, blood test data, medications/interventions, and clinical diagnoses.

Before coming to Oxford, Jenny completed her BASc in Engineering Physics and MSc in Bioinformatics at the University of British Columbia, where her thesis focused on using computer vision to analyse cancer histology images. Jenny has completed multiple internships in designing and implementing computational models for precision medicine and patient care, including biomarker analysis for depression at the NINET Lab, CRISPR/Cas9 activity analysis at Stanford University, genomic marker analysis for cancer at Canada’s Michael Smith Genome Sciences Centre, and metagenomic analysis for respiratory diseases at Fusion Genomics.