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DTSTART;TZID=Europe/Stockholm:20250110T131500
DTEND;TZID=Europe/Stockholm:20250110T160000
DTSTAMP:20260522T170617
CREATED:20241112T152923Z
LAST-MODIFIED:20241114T092020Z
UID:7398-1736514900-1736524800@www.software-center.se
SUMMARY:PhD defense: Towards Continuous Development of MLOps Practices
DESCRIPTION:  \n\n\n\n\n. .\n\nCandidate: Meenu Mary John\, Malmö University\nVenue: Hörsal C\, Niagara\, Malmö University \nOpponent:\nDr. Marie Christin Platenius-Mohr\, PhD\, Research Team Manager\, Automation and Software Architecture\, ABB AG Corporate Research\, Germany \nCommittee:\n– Tommi Mikkonen\, Professor of Software Engineering\, Software and communications engineering\, University of Jyväskylä\, Finland\n-Maria Paasivaara\, Professor of Software Engineering\, Software Engineering\, Lappeenranta-Lahti University of Technology\, Finland\n– Stefan Biffl\, Associate Professor of Software Engineering\, Institute of Information Systems Engineering\, Vienna University of Technology\, Austria\n\n\n\nAbstract:\nContext: With digitalisation\, companies that specialise in software-intensive embedded systems are transforming from a business reliant on hardware and products to a business that utilises software\, data and AI (especially Machine Learning (ML) and Deep Learning (DL)). These technologies allow companies to extend their product offerings\, develop new products or services\, and create revenue opportunities. Despite the advancements\, the majority of ML/DL deployments fail within companies. This highlights the need to optimise the end-to-end process of developing\, deploying\, and evolving ML/DL models\, and a strong understanding and collaboration between researchers and practitioners in both the fields of ML/DL and Software Engineering. \nObjective: The research focuses on two main themes:(a) Establishing systematic and structured frameworks for the development\, deployment and evolution of models\, and (b) Exploring how the evolving (changing) needs of software-intensive embedded systems companies utilising ML/DL technologies shape the MLOps (Machine Learning Operationalisation) practises they needed and measures its maturity over time. Based on the themes mentioned above\, we have three primary objectives: (a) Identifying the need for MLOps\, (b) Developing the frameworks for MLOps adoption\, and (c) Standardising and measuring the MLOps practices. \nMethod: The research is conducted in collaboration with companies and employ various research approaches\, including case study\, action research and multi-vocal literature review. Also\, we employ different techniques\, including interviews and observations. \nResults: First\, it identifies the challenges faced and activities conducted by practitioners in companies when developing\, deploying and evolving models. Second\, it derives a conceptual framework that presents three parallel and concurrent activities that companies utilise when developing\, deploying and evolving models.  Third\, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on integrating\, deploying and operationalising ML/DL models. Fourth\, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge.  Five\, it explores how MLOps\, as a practice\, brings together data scientist teams and operations to ensure the continuous delivery and evolution of models. Sixth\, it presents the MLOps framework\, maps companies to the MLOps maturity model\, and validates the MLOps framework and maturity model with other companies. It also presents critical trade-offs that practitioners made when adopting MLOps. Seventh\, it presents an MLOps taxonomy that helps companies determine their maturity stage and provide tailored MLOps practices to advance. \nConclusion: The thesis shows well-structured approach to improve end-to end ML lifecycle. Through the research\, we seek to enable and advance not only experts but also non-experts to effectively approach the development\, deployment\, and evolution of ML/DL models in the current embedded systems industry. This is relevant considering the shortage of highly skilled data scientists.
URL:https://www.software-center.se/event/mlops-practices/
LOCATION:Malmö University\, Malmö\, Sweden
CATEGORIES:PhD defense
ORGANIZER;CN="Helena Holmstr%C3%B6m Olsson":MAILTO:helena.holmstrom.olsson@mau.se
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20241218T140000
DTEND;TZID=Europe/Stockholm:20241218T170000
DTSTAMP:20260522T170617
CREATED:20241114T092501Z
LAST-MODIFIED:20241114T201651Z
UID:7426-1734530400-1734541200@www.software-center.se
SUMMARY:PhD defense: Advancing Edge Intelligence - Federated and Reinforcement Learning for Smarter Embedded Systems
DESCRIPTION:. .\n\nCandidate: Hongyi Zhang\nVenue: Jupiter 473\, Chalmers University of Technology \nOpponent:\nXavier Franch\, Professor Informatics\, UPC\, Spain. https://www.upc.edu/gessi/personalpages/xfranch/ \nCommittee: \n\n\nChristian Kästner\, Associate Professor of CS\, CMU\, USA.\n\n\nHenry Muccini\, Professor CS\, University L’Aquila\, Italy.\n\n\nLuis Cruz\, Assistant professor CS\, TU Delft\, Netherlands\n\n\n\n\n\n\n\nAbstract:\nContext: The rapid growth of embedded devices and edge computing has brought new opportunities for creating intelligent systems. However\, these systems face challenges such as limited computational power and the need to protect user privacy. As a result\, there is a need for machine learning methods that can scale effectively\, maintain privacy\, and adapt to changing conditions in embedded applications.\n \nObjective: This thesis focuses on improving the performance of machine learning models in embedded systems by using federated learning and reinforcement learning. The main goal is to develop methods that allow edge devices to work together without sharing raw data\, which helps maintain privacy. Another goal is to make these systems more adaptable to dynamic environments\, so they can perform better under changing conditions. Additionally\, the research seeks to improve the efficiency of communication and computation across devices.\n \nMethod: The research uses a mix of case studies\, simulations and real-world experiments. Federated learning is applied to allow edge devices to train models without centralizing the data\, keeping sensitive information local. Reinforcement learning is used to help devices learn how to make better decisions by interacting with their environment. These two methods is tested in different scenarios to evaluate improvements in model accuracy\, resource use\, and adaptability.\n \nResults: The results of this thesis highlight significant advancements in federated learning (FL) and reinforcement learning (RL) for embedded systems. A comprehensive literature review identified six key challenges and open research questions in FL\, emphasizing the need for efficient communication\, scalability\, and privacy preservation. Case studies in telecommunications and automotive applications demonstrated that FL\, particularly with asynchronous aggregation protocols\, improves model performance\, reduces communication overhead\, and speeds up training in real-time\, dynamic environments. Novel algorithms\, such as AF-DNDF and deep RL approaches\, further enhanced decision-making capabilities and adaptability in applications like autonomous driving and UAV base station deployment for disaster scenarios. The development of frameworks like EdgeFL provided practical solutions to overcome FL’s implementation challenges\, offering scalable\, low-effort alternatives. Overall\, the integration of FL and RL into embedded systems resulted in improved model accuracy\, resource utilization\, and adaptability\, making these approaches highly suitable for real-world industrial use cases.\n \nConclusion: This research advances the field of edge intelligence by providing a practical approach to deploying machine learning models that are scalable\, privacy-focused\, and adaptive in embedded systems. The work demonstrates clear improvements in performance and offers a foundation for future research\, which could explore more complex learning approaches and apply these techniques to a wider range of embedded systems.
URL:https://www.software-center.se/event/smarter-embedded-systems/
LOCATION:EDIT Jupiter 473\, Hörselgången 5\, Hörselgången 5\, Gothenburg
CATEGORIES:PhD defense
ORGANIZER;CN="Helena Holmstr%C3%B6m Olsson":MAILTO:helena.holmstrom.olsson@mau.se
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20240610T100000
DTEND;TZID=Europe/Stockholm:20240610T130000
DTSTAMP:20260522T170617
CREATED:20240409T180442Z
LAST-MODIFIED:20240506T090935Z
UID:6538-1718013600-1718024400@www.software-center.se
SUMMARY:PhD defense: Synergizing Data Management\, DataOps\, and Data Pipelines for AI Enhanced Embedded Systems
DESCRIPTION:Candidate:\n\nAiswarya Raj Munappy\n\nOpponent:\n\nTenured associate professor Xiaofeng Wang\, University of Bolzano\, Italy\n\nCommittee: \n\nProfessor Maria Paasivaara\, LUT University Finland\nProfessor Daniel Varro\, Linköping University\nProfessor Stefan Wagner\, Technical University Munich\n\n\n\n\n\n\n\n\n  \nAbstract:\nContext: Data management is a critical aspect of any artificial intelligence (AI) initiative\, playing a pivotal role in the development\, training\, and deployment of AI models. A well-structured approach to data management ensures that AI models are trained on reliable data\, comply with ethical standards\, and contribute positively to decision-making processes in embedded systems. \nObjectives: This thesis is structured around three primary objectives. The first objective is to comprehensively understand and address the data management challenges associated with embedded systems. Building upon this understanding\, the second objective is to explore the data management practices that can help alleviate the data management challenges. Finally\, the third objective aims to develop and validate the implementation approaches for enhanced data management. \nMethod: To achieve the objectives\, we conducted research in close collaboration with industry and used a combination of different empirical research methods like interpretive case studies\, literature reviews\, and action research. \nResults: This thesis presents six main results. First\, it identifies and categorizes data management challenges\, solutions\, and limitations. Second\, it presents a stairway model delineating the stages of the evolution towards DataOps. Third\, it proposes a model for evaluating the maturity of data pipelines and identifies determinants to assess the impact of machine learning (ML) on data pipelines. Fourth\, it identifies the differences between unidirectional and bidirectional data pipelines and the significance\, benefits\, and challenges of bidirectional data pipelines. The thesis also provides a roadmap for the smooth migration from unidirectional to bidirectional data pipelines. Fifth\, it presents and validates the conceptual model of an end-to-end data pipeline for ML/DL models. It also discusses how to balance the need for robustness with the complexity of the pipeline. Finally\, it presents and validates fault-tolerant data pipelines and an AI-powered 4-stage model for automated fault recovery in data pipelines. \nConclusion: In essence\, this research contributes insights and practical guidance for addressing data management challenges in AI-enhanced embedded systems. The identified challenges\, solutions\, and proposed models pave the way for future research and industry practices\, aiming to streamline data operations\, enhance the reliability of DL models\, and promote efficient data management in evolving technological landscapes of AI-enhanced embedded systems. \n  \nHotels\nAs a participant in the defense and in the Digital Product Management Week you have a discount for the hotels in the list below: \n\nGothia Towers\, Standard room: 1590kr\, use the this link for the conference discount\nRadisson Blu Riverside Hotel:- 15% Code to be used: Corporate account id\, 57072\nComfort Hotel Göteborg: 15%: Code to be used: Chalmers Logi\nClarion Hotel Pier: 15%: Code to be used: Chalmers Logi\nRiverton:  -15%: Code to be used: DPMWEEK
URL:https://www.software-center.se/event/synergizing-data-management/
LOCATION:EDIT Jupiter 473\, Hörselgången 5\, Hörselgången 5\, Gothenburg
CATEGORIES:PhD defense
ORGANIZER;CN="Aiswarya Raj Munappy":MAILTO:aiswarya@chalmers.se
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20221202T133000
DTEND;TZID=Europe/Stockholm:20221202T170000
DTSTAMP:20260522T170617
CREATED:20221102T065238Z
LAST-MODIFIED:20221103T151228Z
UID:5234-1669987800-1670000400@www.software-center.se
SUMMARY:Public defense: Robbert Jongeling’s doctoral thesis
DESCRIPTION:The public defense of Robbert Jongeling’s doctoral thesis in Computer Science will take place at Mälardalen University\, room Gamma (Västerås Campus) at 13:30 on 2nd December 2022\, title: \nLightweight consistency checking for advancing continuous model-based development in industry\nThe faculty examiner is Professor Antonio Vallecillo\, UMA\, Spain. \nThe examining committee consists of Professor Ileana Ober\, IRIT/University of Toulouse\, France; Dr Henrik Lönn\, Volvo\, Sweden and Professor Manuel Wimmer\, JKU\, Austria. \nReserve is Professor Alessandro Papadopoulos\, MDU\, Sweden. \nSummary\nThe engineering of modern software‐intensive systems requires collaboration between many people in many different disciplines. To effectively design and communicate\, these engineers create a large number of development artefacts throughout the system development\, such as models\, informal diagrams\, and textual\ndescriptions. These artefacts describe the intended structure and behaviour of systems. Contradictions between the artefacts or misunderstandings based on the understandings of engineers and what is captured in the artefacts can lead to unmet requirements\, bugs\, overruns of development budget and time\, or even system\nfailures. \nInconsistencies between these development artefacts occur continuously throughout the system’s development and maintenance\, simply due to the incremental nature of the development of artefacts. To prevent the inconsistencies causing the earlier mentioned bad outcomes\, it is often desirable to check to what extent various development artefacts are consistent with each other\, that is\, if they contain contradicting information about the same aspects of the system. \nThe potential informality and incompleteness of models and other development artefacts\, in combination with a strife for shorter development iterations\, presents challenges to consistency checking approaches. In this thesis\, we consider model‐based development settings in which short development cycles are aspired. We show a tight coupling between introducing short development cycles and the need for increased support for consistency checking across models and other artefacts. In particular\, we identify the need for lightweight consistency\nchecking approaches\, which we break down into four required aspects. \nThis thesis supplements the literature on consistency checking by providing industrial perspectives in the following two ways. First\, we survey industrial settings and present the various needs for consistency checking based on a broad range of settings and scenarios. Moreover\, we use these industrial settings to define in what aspects a consistency checking approach should be lightweight to enable its industrial adoption. Second\, we include detailed experiences of iintroducing lightweight consistency checks in industrial settings. In summary\, this thesis presents contributions to consistency management in complex industrial settings to facilitate the adoption of continuous model-based development. \nRead more at the MDU web site:\nhttps://www.mdu.se/en/malardalen-university/calendar/2022/the-public-defense-of-robbert-jongelings-doctoral-thesis-in-computer-science \nVideo: “From informal architecture diagrams to flexible blended models” by Robbert Jongeling\nThis video is a short summary of the paper “From informal architecture diagrams to flexible blended models” by Robbert Jongeling from Mälardalen University. Authors are Robbert Jongeling\, Federico Ciccozzi\, Antonio Cicchetti\, and Jan Carlson. The paper was published at the 16th European Conference on Software Architecture (ECSA 2022) and awarded the Best paper award. \nView the video here: https://youtu.be/Pia7zZFp9RE \n  \n 
URL:https://www.software-center.se/event/public-defense-robbert-jongelings-doctoral-thesis/
LOCATION:Virtual meeting\, Sweden
CATEGORIES:PhD defense
ORGANIZER;CN="Jan Carlson":MAILTO:jan.carlson@mdh.se
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