Masoud Jalayer
PhD in Industrial Engineering
masoud.jalayer@utu.fi ORCID-tunniste: https://orcid.org/0000-0001-8013-8613 |
AI-based Condition Monitoring; Human-Robot Interactions; Digital Twinning; Generative Artificial Intelligence; Uncertainty Quantification
Since August 2024, I have been working as a Postdoctoral Fellow at the University of Turku, focusing on applying AI to the battery circular economy in the SmartCycling project. Earlier in 2024, I served as a Visiting Professor at the University of Alberta, where I worked on generative AI-based maintenance scheduling and conducted uncertainty analysis for AI-based fault diagnosis. From March 2023 to August 2024, I was an Assistant Professor in Machine Learning at Politecnico di Milano. There, I taught courses on Machine Learning, Business Analytics and Big Data, while conducting research and supervising graduate students on multimodal AI and human-centered digital twins.
Between 2021 and 2023, I pursued postdoctoral research at the University of Victoria, Dept. of Mechanical Engineering and Dept. of Electrical and Computer Engineering, where I led teams of graduate students on projects involving digital twinning, adaptive scheduling, and explainable AI for fault diagnosis. I held a MITACS postdoctoral fellowship at Unilever Canada Co., working on AI-based demand and shipment forecasting and unsupervised learning for customer segmentation.
My academic journey includes a Ph.D. in Industrial Engineering from Politecnico di Milano (2017-2021), where I specialized in applying AI to fault detection and diagnosis in manufacturing systems. During this time, I also studied machine learning at the University of Zurich. I worked at École Polytechnique Fédérale de Lausanne (EPFL), focusing on fault diagnosis of rotating machinery under noisy conditions, PV energy generation forecasting, and developing generative algorithms for automatic visual inspection of rare defects.
- Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions (2025)
- Computers and Industrial EngineeringVirtual and Physical Prototyping
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Enhanced geometrical control in cold spray additive manufacturing through deep neural network predictive models (2025)
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - (2025)
- arXiv.orgLecture Notes in Mechanical Engineering
(O2 Muu julkaisu ) - A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence (2024)
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - A Model Identification Forensics Approach for Signal-Based Condition Monitoring (2024)
- Lecture Notes in Mechanical Engineering
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - ConvLSTM-based Sound Source Localization in a manufacturing workplace (2024)
- Computers and Industrial Engineering
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty (2024)
- arXiv.org
(O2 Muu julkaisu ) - Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models (2024)
- Machines
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )