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.
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 .
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.
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.
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.
Benjamin Tapia Sal Paz
Benjamin studied Civil engineering at Universidad Nacional de Tucuman (UNT) in Tucuman, Argentina. In 2018 he entered to Instituto Balseiro in Bariloche Argentina where he obtains his diploma on Mechanical Engineering in 2021. His thesis project at Comisión Nacional de Energía Atómica (CNEA) focused in an autonomous robotics platform for warehouse application using controllers synthesis.
Benjamin is currently working as an Early Stage Researcher with Ikerlan, S. Coop. and enrolled as PhD student at Universidad del País Vasco (UPV). As part of the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA) he will work in Multi-sensor quality inspection based adaptive control systems.
Felix completed his Bachelor's in 2015 and Master's in Mechanical Design and Theory at Northeastern University in 2018, China, where he focused on the fault identification and health management of conventional defects for an engineering system.
Since then, he had been working as an overseas product engineer at one telecommunication company for almost four years and gained industrial experiences related to diagnostic and prognostic expertise.
Presently, Felix is employed as a Marie Curie Early Stage Researcher by Cranfield University which is part of the Monitoring Large-Scale Complex Systems (MOIRA) project, where he will fuse and develop a hybrid prognostic methodology for aircraft systems and complete his Ph.D. study at the school of aerospace, transport, and manufacturing.
Achilleas studied Mechanical Engineering at National and Technical University of Athens in Greece. Having chosen specialization in Mechanical Design, he worked on numerous projects in Condition Monitoring, Fault Detection and Automatic Control. His diploma thesis focused on modeling and optimizing an energy system with Deep Learning methods. In addition, he worked as Research Assistant in National Kapodistrian University of Athens, were he improved his knowledge in Data Analysis. The results of his research were published in 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS).
He is currently employed as an Early Stage Researcher at the Laboratoire Vibrations Acoustique (LVA), INSA Lyon, France, as a part of the MOIRA Project, funded by the European Commission through the H2020 “Marie Skłodowska-Curie Innovative Training Networks” program. He is enrolled as a Ph.D. student at the MEGA Doctoral School of INSA Lyon. His research activity focuses on the development of model-based techniques for monitoring individual units in a fleet from a probabilistic approach.
Fadi obtained his Bachelor’s (2019) and Master’s (2021) degrees in Telecommunications Engineering from the Lebanese University Faculty of Engineering (ULFG), Lebanon. His Master’s research at the Doctoral University of ULFG focused on Signal Processing, Artificial Intelligence and Machine/Deep Learning applied in the Communications, Biomedical and Industrial Fields.
He is currently appointed as a Marie Curie Early Stage Researcher at Safran, INSA Lyon. As part of the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA) he will carry out signal processing and deep learning to analyze, identify and model onboard aircraft engines vibration and acoustic signals, in order to monitor its conditions using smartphones/tablets.
Mohammad studied Aerospace Engineering at Azad University of Tehran, Science, and Research branch. He obtained his Bachelor's in 2019 and his Master's in 2021. Having chosen specialization in control systems engineering, he worked on different projects related to the application of sensor fusion and machine learning techniques on autonomous robotics.
Mohammad is currently working as an Early Stage Researcher with AMC VIBRO sp. z o.o., Kraków, Poland as a part of the European Training Network on Monitoring Large-Scale Complex Systems. His research activity focuses on the heterogeneous data fusion for monitoring of mines using robotic devices.
Abdul received his Bachelor’s in Electronics Engineering from COMSATS Institute of Information Technology in 2011. He obtained a Master’s degree in Electrical Engineering from National University of sciences and technology (NUST) with a specialization in Control Systems in 2014. His Master’s thesis focused on the Sampled-Data Control of the Quadrotor Unmanned Aerial vehicle. After his first Master`s degree, he worked almost three years in industry on the maintenance and development of Servo Feedback Systems. He was awarded Erasmus Mundus Scholarship for the European Master on Advanced Robotics (EMARO+) in 2017. He obtained his second Master’s degree in 2019. From October 2020 to September 2021, he worked on plant inspired growing soft robots.
He is currently working as a Marie Sklodowska-Curie scholar at the University of Modena and Reggio Emilia (UNIMORE), Reggio Emilia, Italy under the framework of the European Training Network on Monitoring Large-Scale Complex Systems (MOIRA). His research focuses on hybrid modelling techniques for condition monitoring of motion control applications.
Atabak started his educational career in Mechanical Engineering at the University of Karaj (Iran) and continued with his Master of Science at Sapienza University of Rome (Italy), where he dedicated his studies to Solid mechanics and Joint National Research Center of Italy (CNR-INM). As a Master thesis, he investigated Structural Health Monitoring using both theoretical and experimental approaches.
Soon after his graduation in 2020, he joined the Advanced R&D team as a NVH engineer at Bridgestone EMEA in Rome (Italy) which - as one of the world biggest tire manufacturers - is an international and multicultural industrial company. While there, he gained experience in data analysis and AI.
Atabak is now a member of Marie Curie Early-Stage Researcher Fellowship through joining Fraunhofer Institute for Structural Durability and System Reliability in Darmstadt, Germany. He will develop a methodology based on the Failure-Modes-and-Effects-Analysis (FMEA) and multi-class classification methods to monitor large-scale infrastructure and a novel method for sensor network degradation.