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Persoonlijk profiel

Quote

“I am interested in the design and applications of reliable machine learning in industrial systems for optimizing the manufacturing and maintenance processes.”

Research profile

Mike Holenderski is an Assistant Professor at the Eindhoven University of Technology at the System Architecture and Networking group. His research focus is on, machine learning which has been shown to perform well in various domains, some- times even outperforming humans on tasks such as image classification. Usually, the data that the machine learning systems are trained and evaluated on are static and relatively clean. In reality, however, the data is often noisy, corrupted, partially missing and coming from dynamic environments which are changing over time.

Reliable machine learning aims at developing models which are robust to noise and missing data, and can detect and adapt to changes in the processes that they monitor and control. The relevant questions include: How can a system measure its own performance and detect when its predictions are not accurate, e.g. because the behavior of the monitored process has changed or because the input is very different from previous observations? How can it provide safe predictions when it cannot be certain about the accuracy? How can a system adapt to the changes in the environment or when being deployed in a new context? How can a machine learning agent deal with corrupt or missing data?

Academic background

Mike Holenderski received his PhD in 2012 on the topic of real-time systems. Since then he has been working on Machine Learning. His current interest is Reliable Machine Learning, where the goal is to perform machine learning tasks, such as failure prediction or wear estimation, using real industrial data which is often noisy, corrupted, partially missing or coming from dynamic environments which change over time. He is interested in putting the theory in practice, which he has been doing in collaborations with industry through various European and national projects.

Vingerafdruk Duik in de onderzoeksthema's waar Mike J. Holenderski actief is. Deze onderwerplabels komen voort uit het werk van deze persoon. Samen vormen ze een unieke vingerafdruk.

  • 1 Vergelijkbare profielen
Real time systems Engineering en materiaalwetenschappen
Scheduling Engineering en materiaalwetenschappen
Data storage equipment Engineering en materiaalwetenschappen
Visualization Engineering en materiaalwetenschappen
Embedded systems Engineering en materiaalwetenschappen
Servers Engineering en materiaalwetenschappen
Time series Engineering en materiaalwetenschappen
Processing Engineering en materiaalwetenschappen

Netwerk Recente externe samenwerking op landenniveau. Duik in de details door op de stippen te klikken.

Onderzoeksoutput 2005 2019

Business drivers of a collaborative, proactive maintenance solution

Jantunen, E., Akcay, A., Campos, J., Holenderski, M. J., Kotkansalo, A., Salokangas, R. & Sharma, P., 2019, The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance. River Publishers, blz. 7-35 30 blz.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureHoofdstukAcademicpeer review

Open Access
Bestand

A formalization of computational trust

Guven, C., Holenderski, M., Ozcelebi, T. & Lukkien, J., 16 jan 2018, Joint 13th CTTE and 10th CMI Conference on Internet of Things - Business Models, Users, and Networks. Piscataway: Institute of Electrical and Electronics Engineers, Vol. 2018-January, 8 blz. 8260936

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Social sciences
Computer science
Industry
Formalization
Trust model

On the application of machine learning techniques to dataset error detection

Pereira Azevedo, C. A., 24 okt 2018, Eindhoven: Technische Universiteit Eindhoven. 70 blz.

Onderzoeksoutput: ScriptiePd Eng ThesisAcademic

1 Citaat (Scopus)

Predicting machine failures from industrial time series data

Jansen, F. J. M., Holenderski, M. J., Ozcelebi, T., Dam, P. & Tijsma, B., 2018, 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT) . Piscataway: Institute of Electrical and Electronics Engineers, 6 blz. 8394915

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Time series
Recurrent neural networks
Cleaning
Machining
Neural networks

Using artificial neurons in evidence based trust computation

Guven, C., Holenderski, M., Ozcelebi, T. & Lukkien, J., 5 sep 2018, Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Piscataway: Institute of Electrical and Electronics Engineers, blz. 1879-1884 6 blz. 8456153

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Neurons
Fusion reactions
Neural networks
Experiments
Evidence-based

Cursussen

Data analytics for engineers

1/09/17 → …

Cursus

Internet of things

1/09/15 → …

Cursus

Operating systems

1/09/13 → …

Cursus

Scriptie

Active learning in VAE latent space

Auteur: Tonnaer, L., 25 sep 2017

Begeleider: Menkovski, V. (Afstudeerdocent 1), Portegies, J. W. (Afstudeerdocent 2) & Holenderski, M. (Afstudeerdocent 2)

Scriptie/masterproef: Master

Bestand

An access control platform based on blockchain

Auteur: Galaz Garcia, L., 25 sep 2017

Begeleider: Holenderski, M. (Afstudeerdocent 1) & Holenderski, L. (Externe persoon) (Afstudeerdocent 2)

Scriptie/masterproef: Master

Bestand

A nearest neighbor based cold-deck imputation for X-ray tube wear estimation

Auteur: Narayan, N., 27 nov 2017

Begeleider: Holenderski, M. (Afstudeerdocent 1) & Gijsbers, J. (Externe persoon) (Externe coach)

Scriptie/masterproef: Master

Bestand

Blockchain based smart contracts for business process automation

Auteur: Kolhe, S., 26 feb 2018

Begeleider: Holenderski, M. (Afstudeerdocent 1) & Sinitsyn, A. G. (Externe persoon) (Externe coach)

Scriptie/masterproef: Master

Bestand

Content based CT retrieval for pulmonary nodules: deep metric learning based feature extraction

Auteur: Aerts, T., 28 feb 2019

Begeleider: Menkovski, V. (Afstudeerdocent 1), Holenderski, M. (Afstudeerdocent 2) & Veta, M. (Afstudeerdocent 2)

Scriptie/masterproef: Master

Bestand