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Submit Date Name Organisation Statement of capabilities/facilities
2020-06-29. Jasmin Kevrić International Burch University The team at International Burch University includes professors and researchers from the Department of Information Technologies, Electrical and Electronics Engineering, and Genetics and Bioengineering + Ph.D. candidates.

The team members have more than 10 years of experience in developing and applying machine learning (ML) algorithms in various fields, such as deep learning, image and signal processing, natural language processing, as well as evaluation of the ML algorithms output performance. More than 30 research papers have been published and several research projects have been prepared in the field of ML.

One of the current researches includes conducting detailed comparative empirical and analytical studies of available measures, benchmarking models including architectures, optimization, hyperparameters, computational budget, and using more than one measure to address the solution for evaluating the uncertainty of the ML algorithm output.



2020-06-29. Almir Badnjevic Verlab Verlab can contribute in development and validation of machine learning algorithms.
2020-06-29. Almir Badnjevic Verlab Verlab can contribute in development and validation of machine learning algorithms.
2020-06-26. Francesca Pennecchi INRIM INRIM team has experience in mathematical/statistical models for metrology and uncertainty evaluation as well as in virtual experiments and numerical tools for simulation of electromagnetic phenomena. It is interested in contributing to the theoretical development of uncertainty propagation through ML methods (SRT objective 1) and in the development of case studies in the field of digital pathology, nanoparticle and magnetic nanostructures characterization, and interferometry applications (SRT objective 4). Its participation into projects like QUIERO (coordination), EMUE, RaCHy and MATHMET could lead to cross-fertilization of competences on Machine Learning tools.
2020-06-26. Andrea Ferrero Politecnico di Torino We work on the modellisation and simulation of aerospace propulsion systems with particular attention to the prediction of turbulent flows. We have experience in the development of data-driven models for the closure of the Reynolds-averaged Navier-Stokes (RANS) equations. In particular, we investigated the use of field inversion by means of an adjoint approach and artificial neural networks to develop transition models for turbomachinery flows: we focused on the robustness and predictive capability of the final turbulence model. In collaboration with INRiM we started to investigate the uncertainty propagation from experimental data to the data-augmented model in order to quantify the reliability of the final model and to improve the learning procedure.
2020-06-25. Alen Bošnjaković Institute of Metrology of Bosnia and Herzegovina IMBiH team has an experience on projects like EMUE, Met4FoF, MedalCARE and QUIERO. We participated in work related to machine learning on Met4FoF and MedalCARE project. Also, we have significant knowledge in statistical data analysis and in the use of innovative methods for writing project reports. IMBiH can be reliable partner in completing activities in this project, and for data analysis in most of them.

IMBiH team include:
• Alen Bosnjakovic, an expert advisor and technical manager at the Laboratory for mass and related quantities (part for pressure) of the Institute of Metrology of Bosnia and Herzegovina.
• Vedran Karahodzic, an expert assistant at the Institute of Metrology of Bosnia and Herzegovina and an assistant at the Faculty of Electrical Engineering, University of Sarajevo.
2020-06-25. Vedran Karahodžić Institute of Metrology of Bosnia and Herzegovina IMBiH team has an experience on projects like EMUE, Met4FoF, MedalCARE and QUIERO. We participated in work related to machine learning on Met4FoF and MedalCARE project. Also, we have significant knowledge in statistical data analysis and in the use of innovative methods for writing project reports. IMBiH can be reliable partner in completing activities in this project, and for data analysis in most of them.

IMBiH team include:
• Alen Bosnjakovic, an expert advisor and technical manager at the Laboratory for mass and related quantities (part for pressure) of the Institute of Metrology of Bosnia and Herzegovina.
• Vedran Karahodzic, an expert assistant at the Institute of Metrology of Bosnia and Herzegovina and an assistant at the Faculty of Electrical Engineering, University of Sarajevo.
2020-06-24. Loďc Coquelin LNE
2020-06-23. Nicolas Fischer LNE expert in the field of statistics, uncertainty and machine learning
2020-06-23. Frank De Jonghe EY I lead our Quantitative & Analytics offering in EMEIA, about 360 quant modellers that have as one of the key expertises so-called Model Validation (providing an opinion on the adequacy of models) and Model Governance/Model RIsk MAnagement. This is currently mainly applied to the Financial Industry.

Within our firm, we have developed an offering Trusted AI, which is leveraging this experience to the broader domain of Machine Learning and AI, across industries.

Using our field experience, we can probably best help to translate principles in realistic field procedures, including documentation/evidence standards. Also, there will also be significant expert judgement, in view of the business use of the algorithm, that needs to be properly framed.



2020-06-23. Frank De Jonghe EY I lead our Quantitative & Analytics offering in EMEIA, about 360 quant modellers that have as one of the key expertises so-called Model Validation (providing an opinion on the adequacy of models) and Model Governance/Model RIsk MAnagement. This is currently mainly applied to the Financial Industry.

Within our firm, we have developed an offering Trusted AI, which is leveraging this experience to the broader domain of Machine Learning and AI, across industries.

Using our field experience, we can probably best help to translate principles in realistic field procedures, including documentation/evidence standards. Also, there will also be significant expert judgement, in view of the business use of the algorithm, that needs to be properly framed.



2020-06-19. Yaochu Jin University of Surrey I have been working on the following research topics that may be related to this trustworthy machine learning:
1) Extraction of interpretable rule trained neural networks. By including an interpretability term into the loss function during neural network training, transparent symbolic or fuzzy rules can be extracted from neural networks.
2) Generation of robust machine learning models that are insensitive to adversarial attacks. Multi-objective neural architecture search techniques are applied to search for both accurate and explainable machine learning models.
3) Privacy preserving machine learning. Federated machine learning and learning over encrypt data are two approaches to preserve the privacy of the client data. In the former approach, data is stored on local devices and does not need to be transmitted to the server, while the latter enables machine learning model to learn over data encrypted using homomorphic encryption methods.
2020-06-19. Nicolas BOUSQUET Sorbonne Université Research in theoretical and applied statistics, machine learning and deep learning
Research in the fundamental treatment of epistemic and aleatory uncertainties in computer and data-driven models and the development of methodologies
PhD students, internships, post-doctoral and short-term contract researchers can be welcome and supervised.

2020-06-19. Wenwu Wang University of Surrey Expertise on supervised and unsupervised learning, signal processing, sparse and low-rank models, multimodal fusion, and Bayesian inference.
2020-06-18. Peter Harris NPL Member of NPL's Data Science Department, with interests in machine and deep learning, and their use in measurement applications.
2020-06-17. Haji AHMEDOV TUBİTAK UME, National Metrology Institute of Turkey We have expertise in optimization and statistical analysis and have experience in developing classical measurement techniques. We can contribute to the theoretical studies as well as help in developing the measurement and analysis techniques to meet the first objective given in PRT.
2020-06-15. Wojciech Samek Fraunhofer HHI Deep Learning, Trustworthy ML, Interpretability and Uncertainty Quantification
2020-06-15. Wojciech Samek Fraunhofer Heinrich Hertz Institute
2020-06-15. Andrew Thompson NPL Expertise in the fields of statistics and machine learning
2020-06-12. Clemens Elster PTB Expertise in the field of statistics and machine learning
2020-06-12. Jörg Martin PTB Expertise in the field of statistics and machine learning
2020-06-10. Nieves Medina CEM I would like to formally express our interest in joining the consortium of the project, and to the best of our ability to contribute to the JRP. We are now linked to MATHMET. We can offer some experience in Python with Tensorflow and Keras as well as a a broad experience in mathematics applied to metrology.
2020-06-03. JOAO ALVES SOUSA IPQ Due to lack of available funds (commitment) we had to opt for another project under the MATHMET umbrella, were disappointed for not joining this project on “Trustworthy machine learning”.
However, now that the list of SRT has become available the situation has changed. Some of the projects in other domains (e.g., mass, optics, etc.) did not make it to SRT and there are funds available to be involved in other projects. In fact, having been asked by MSU on projects we would consider a priority to be involved in, we have mentioned this project on machine learning.
Therefore, I would like to formally express our interest in joining the consortium of the project, and to the best of our ability to contribute to the JRP. You know of our links to MATHMET in all matters related to uncertainty evaluation, and we have collaborated in the recent past with the NPL in this topic with Alistair and others. This will be a good opportunity to build on that and be on track in this very relevant topic.

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