Find Academic Supervisors

Jim HowardView profile

Lecturer

• Algorithms
• Machine Learning
• Machine Learning System Design and Operations
• +3 more

Jim Howard is an Instructor in Software Engineering and Data Science at the University of St. Thomas. With over 15 years of industry experience, he currently serves as a Principal Engineer of Machine Learning at Laurel.AI. His expertise spans algorithm development, machine learning system design, and the application of AI in legal contexts and public policy. Howard holds a PhD in Computer Science from the Colorado School of Mines and a BS in Computer Science and Mathematics from Rose-Hulman Institute of Technology. Research Interests: Machine learning infrastructure optimization Generative models for policy creation Legal implications of AI deployment Production system design for ML models Teaching: In Fall 2025, he teaches SEIS 765 Machine Learning Operations (MLOps), focusing on bridging ML development and operational deployment through collaborative practices and tooling. Industry Experience: Previously worked with Fortune 500 firms like 3M, Land O'Lakes, and Meta, specializing in scalable ML implementation.

Patricia LagoView profile

Professor

• Software Engineering
• Software Architecture
• Software Sustainability
• +4 more

Patricia Lago is a Professor at the Vrije Universiteit Amsterdam, where she leads the Software and Sustainability (S2) research group in the Computer Science Department. She serves as a member of the IEEE ICSA and ECSA Steering Committees, and is the Steering Committee Chair of the ICT4S conference series. Dr. Lago is also the initiator of the Computer Science Master Track in Software Engineering and Green IT, and the director of the Master Information Sciences program. Dr. Lago earned her PhD in Control and Computer Engineering from Politecnico di Torino and a Master in Computer Science from the University of Pisa, both in Italy. Her research focuses on creating software engineering knowledge that makes software better, smarter, and more sustainable. She emphasizes industrial relevance and practical application, focusing on the 'real' needs of practice through collaboration with partners from both private and public sectors. Her specific research areas include software architecture, sustainability, software design and modeling, and software quality assessment. Dr. Lago's recent publications demonstrate a strong trend toward sustainable software engineering, with particular emphasis on energy efficiency, green architectural patterns, and sustainability assessment frameworks. Her work bridges theoretical research with practical applications, especially in areas like robotics, machine learning systems, and cloud computing, where sustainability considerations are increasingly critical. Dr. Lago co-founded the Green Lab, a collaborative space where researchers, students, and companies work together to measure the energy footprint of software solutions. She has been actively involved in numerous conferences and workshops related to software sustainability, serving in leadership roles including as NIER Co-Chair at ICSE 2021.

• Machine Learning operations (MLops)
• Data Science
• Software Engineering

Amal AKLI is a Researcher affiliated with the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), specifically within the Department of Computer Science. She is part of the Security Design and Validation research group (SERVAL), led by Prof. Yves Le Traon. Amal holds a Master’s degree in computer science from the National School of Computer Science in Algeria (2022). Her research focuses on Machine Learning operations (MLops), data science, and software engineering, with a particular emphasis on applying AutoML and few-shot learning techniques to improve software development and testing processes. Her recent publications explore parameter-efficient fine-tuning of pre-trained code models and predicting flaky tests using few-shot learning. While no scientific awards are explicitly mentioned, her work contributes to advancing automated testing and machine learning integration in software engineering. Amal's research aligns with the broader goals of the SERVAL team in enhancing software design and validation methodologies.

Bestoun S. Ahmed Al-BeywaneeView profile

Professor

• Quality Assurance of Machine Learning Systems
• Software Testing
• Trustworthy AI Systems
• +5 more

Bestoun S. Ahmed Al-Beywanee is a Professor in AI and Software Engineering at the Department of Mathematics and Computer Science, Karlstad University, Sweden. He joined the university as a Senior Lecturer in 2019, was promoted to Associate Professor in 2020, and to Professor in 2023. His roles include teaching advanced courses such as AI Engineering, Automated Software Engineering, and Software Testing Fundamentals. His research focuses on software quality assurance, trustworthy AI systems, and MLOps, with a strong emphasis on combinatorial testing, IoT systems, and applied optimization techniques. Education: BSc (Electrical and Electronic Engineering, University of Salahaddin-Erbil, 2004); MSc (University Putra Malaysia, 2009); PhD (Software Engineering, University Sains Malaysia, 2012). Postdoctoral research at the Swiss AI Lab IDSIA (2015) and positions at Salahaddin University and Czech Technical University further enriched his expertise. Research interests span Quality Assurance of machine learning systems, software testing methodologies, trustworthy AI, IoT system reliability, and optimization algorithms. He has pioneered frameworks like PatrIoT for IoT testing and contributed to MLOps robustness. His work integrates machine learning with anomaly detection, adaptive systems, and industrial applications. Recent articles highlight advancements in data-driven heat pump management, MLOps robustness, and edge-cloud AR/VR optimization. Collaborations include projects on digital twins, smart manufacturing, and industrial IoT. His contributions bridge theoretical research with practical industrial solutions, emphasizing system reliability and AI ethics.

Olga RistićView profile

Associate Professor

• Software Testing
• Information Technologies
• Modeling and Simulation
• +5 more

Olga Ristić is an Associate Professor at the Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. Her office is located at Svetog Save 65, Čačak (Office No. 237), and she can be contacted via phone (+381 32 302-714) or email. She holds a Diploma in Technics and Informatics (1997), a Master's in Technical Sciences (2006), and a PhD in Information Technologies and Systems (2016), all from the University of Kragujevac. Her research focuses on: Software testing methodologies and quality assurance Modeling and simulation of complex systems Optimization algorithms for industrial applications Educational technology and IT pedagogy Mobile applications and information systems reliability Her recent publications emphasize interdisciplinary approaches, with strong trends in machine learning applications for cybersecurity, optimization in Industry 4.0 systems, educational technology innovations, and sustainable energy management. She frequently employs simulation techniques and algorithm development across diverse domains. She has led or contributed to eight Ministry of Science-funded projects: Curriculum development for IT education programs Software quality assurance frameworks Industrial system reliability modeling Food supply chain optimization Deregulated energy distribution systems At the Faculty of Technical Sciences, she coordinates courses across all academic levels, including Data Structures, Software Testing, Mobile Applications, and Quality of Software. She also conducts professional development seminars on database design and ISTQB certification.

• Software engineering
• Operating systems
• Man-computer interaction
• +6 more

Seppo Sierla is a University Lecturer at Aalto University's Department of Electrical Engineering and Automation within the School of Electrical Engineering. His expertise spans software engineering, automation systems, and human-computer interaction. He has been repeatedly recognized for his teaching excellence, notably receiving multiple Recognition Awards for bachelor-level courses such as Automation Systems 1 (2019-2020), Automation Software Projects (2018-2019), and Automation 1 (2017-2018). These awards highlight his impactful contributions to curriculum development and student engagement in automation education. His research focuses on industrial automation technologies, including digital twins, reinforcement learning applications in energy systems, and process optimization. Notable projects include physics-informed modeling in steel manufacturing and energy-efficient district heating systems. He also explores cloud-based MLOps frameworks and renewable energy time-shifting strategies for photovoltaic systems. Key Research Themes : Industrial process automation, digital twin integration, reinforcement learning for energy systems, and sustainable manufacturing. Recent Contributions : Over a dozen peer-reviewed publications since 2021, addressing topics ranging from steel production modeling to HVAC frequency control. Dr. Sierla's work bridges academic research and industrial applications, emphasizing practical implementations in sectors like manufacturing and energy. His teaching and research synergize to advance automation education and technological innovation.

Petra HeckView profile

Senior Lecturer

• AI Engineering
• Machine Learning
• MLOps (Machine Learning Operations)
• +3 more

Dr. Petra Heck is a Senior Lecturer at the Faculty of ICT , Fontys University of Applied Sciences, specializing in AI Engineering and Machine Learning. She obtained her MSc in Computer Science from TU Eindhoven (2011) and a PhD in Software Engineering from TU Delft (2016) focusing on 'Quality of Just-in-Time Requirements'. Academic Roles : Lecturer in Software Engineering (2012-2016), Senior Researcher in AI & Big Data lectorate (2016-present) Research Focus : Building production-ready machine learning systems, with a postdoctoral focus (2019-2021) on practical ML engineering challenges. Her work emphasizes: Transdisciplinary collaboration involving non-ICT specialists Knowledge transfer to industry and education Shaping the emerging field of AI Engineering as a formal discipline Her publications highlight: Systematic approaches to ML operations (MLOps) Testing and debugging ML applications Evolving quality models for AI systems Defining AI engineer competencies through job market analysis She supervises students in practice-oriented projects, such as Real-time communication app (2014), and contributes to educational programs for AI engineers.

Alireza FurutanpeyView profile

Researcher

• Edge Computing
• Edge Intelligence
• Distributed Inference
• +3 more

Alireza Furutanpey is a University Assistant (PreDoc Researcher) at TU Wien's Distributed Systems Group within the Institute of Information Systems Engineering, holding a Master's degree with distinction. His academic role combines research in distributed systems with teaching responsibilities for core computer science courses. His educational background includes: Bachelor of Science (BSc) Master of Science (Dipl.-Ing.) with distinction Furutanpey's research centers on Edge Computing and Edge Intelligence, specializing in Distributed Inference, Neural Data Compression, and AI-Systems integration. He pioneers techniques for neural feature compression in satellite/edge environments and develops frameworks for federated learning orchestration under communication constraints. His work bridges theoretical AI with practical system implementation, focusing on resource-constrained scenarios where bandwidth and computational efficiency are critical. Analysis of his 15+ publications reveals dominant trends in neural compression for distributed systems (60%), federated learning optimization (25%), and serverless edge frameworks (15%). Key contributions include solving satellite downlink bottlenecks through feature compression and enabling adaptive inference in heterogeneous edge networks, with methodologies increasingly incorporating generative modeling and robustness against adversarial attacks. Scientific Awards: None documented in available sources. He actively supervises master's theses, guiding students on adversarial machine learning, neural compression, and image retrieval systems. His research is supported through major projects: AloTwin (2023-2025) focusing on edge intelligence, INTEND (2024-2026) on industrial IoT, and TEADAL (2022-2025) on federated learning. He serves as a reviewer for 15+ IEEE/ACM venues including IEEE Transactions on Mobile Computing and ICDCS. Furutanpey operates within TU Wien's Distributed Systems Group, which specializes in edge-cloud continuum research. The team develops tools like faas-sim for serverless edge simulation and explores quantum-classical hybrid architectures, maintaining strong industry collaborations in industrial IoT and satellite communications.

George KousiourisView profile

Associate Professor

• Cloud Computing
• IoT Platforms
• Performance Engineering
• +5 more

George Kousiouris is an Associate Professor at the Department of Informatics and Telematics , Harokopio University of Athens . He holds a Ph.D. in Cloud Computing from the National Technical University of Athens (2012) and a Dipl. Eng. in Electrical and Computer Engineering from the University of Patras (2005). His research focuses on Cloud Platforms , Serverless Computing (FaaS) , IoT Infrastructure , and Performance Engineering . He has led major EU-funded projects such as H2020 PHYSICS (lead architect), BigDataStack , and CloudPerfect , contributing to cloud service benchmarking, FaaS frameworks, and edge-cloud collaboration. His work emphasizes practical applications in healthcare, smart agriculture, and urban network analysis. Over 70 publications highlight his expertise in cloud resource optimization , service-level agreements , and data-driven infrastructure management . His recent work explores sustainable computing , human-AI collaboration , and conversational AI for MLOps . Key Projects: PHYSICS, BigDataStack, CloudPerfect, SLALOM, COSMOS Research Highlights: FaaS performance benchmarking, IoT event processing, hybrid-cloud workflows Awards/Grants: Multiple EU H2020 and FP7 project leadership roles He advises on cloud migration methodologies (e.g., ARTIST framework) and contributes to regulatory compliance frameworks like GDPR via semantic ontologies.

• Software Systems Engineering
• Industrial Internet of Things (IIoT)
• MLOps
• +9 more

Carsten Wenzel M.Sc. is a researcher at the Software Systems Engineering (SSE) department of the Institute of Computer Science at the University of Hildesheim. His work focuses on advanced software engineering practices within Industry 4.0 , including MLOps , Industrial IoT , and Cyber-Physical Production Systems . He contributes to projects like DevOpt , which explores controlled emergence in distributed systems, and IIP-Ecosphere , developing AI-integrated platforms for industrial applications. His research also intersects with Smart Energy Systems and Edge Computing , emphasizing interoperability and performance optimization. Key Research Areas : Model-driven realization of industrial digital twins Integration of machine learning operations in manufacturing Scalability of Industry 4.0 platforms Performance analysis of IIoT systems Emergent system control architectures Collaborative Projects : DevOpt : Combines distributed and centralized control for adaptive energy networks IIP-Ecosphere : Creates open-source IIoT platforms for cross-company AI-driven production HAISEM-Lab : Focuses on hardware-optimized AI applications and developer training Infrastructure & Outreach : Works with partners like Siemens AG , pdv-software GmbH , and Phoenix Contact on demonstrators at events such as Hannover Messe and ETFA 2024 . The department maintains tools like EASy-Producer for product line engineering and MetricHaven for quality analysis.

• Machine Learning
• System Architecture
• MLOps
• +6 more

Stephen John Warnett is a researcher affiliated with the Faculty of Computer Science, specializing in Software Architecture. His work focuses on architectural design decisions for machine learning systems, particularly MLOps and RLOps frameworks. Research Interests: Primary areas include Machine Learning, System Architecture, and Reinforcement Learning. Key subtopics involve architectural design decisions, quality assessment, understandability, data integration, and deployment practices in cyber-physical systems. Publications Trends: Recent work emphasizes model-driven metrics for MLOps architectures, understandability in machine learning systems, and decision-making support for training strategies in reinforcement learning. Scientific Awards: Best Paper Award (2022)

Andreas MarfurtView profile

Lecturer

• Natural Language Processing
• Machine Learning Operations
• Interpretable AI
• +2 more

Andreas Marfurt is a Lecturer at Lucerne University of Applied Sciences and Arts (HSLU) in the School of Computer Science and Information Technology, where he has been teaching and conducting research since September 2022. His academic background includes a PhD from EPFL focused on Interpretable Representation Learning for Abstractive Summarization , a postdoc at Idiap Research Institute, and BSc/MSc degrees in Computer Science from ETH Zurich. Research Focus: Marfurt specializes in Natural Language Processing (NLP) and Machine Learning Operations (MLOps), with particular interests in: Developing reliable NLP systems for document summarization and question answering Minimizing hallucinations in language models Practical applications of retrieval-augmented generation (RAG) Multimodal learning systems Interpretable AI and model evaluation He contributes to several research projects including: Music DNA - Personalised Event Matching P2Sr Profila Privacy Simplified reloaded ARGUMENT REPRESENTATION LEARNING (ARL) IAC (Intelligent Automation Components) As part of HSLU's NLP research group, Marfurt develops specialized systems for legal question answering, political argument analysis, and document summarization that emphasize reliability and interpretability.

• Edge Computing
• Maritime Communication Systems
• Machine Learning Operations (MLOps)
• +5 more

Andrei-Raoul Morariu is a researcher at the Faculty of Science and Engineering, Åbo Akademi University , specializing in maritime digitalization, edge computing, and machine learning operations. Affiliation : Åbo Akademi University, Finland Research Themes : Edge analytics, URLLC standards, spectral analysis of maritime systems, MLOps Research Highlights : Developed frameworks for digitalization in Roll-on/Roll-off (RoPax) ports to enhance value creation and sustainability. Investigated ultra-reliable low-latency communication (URLLC) standards for onboard maritime equipment. Applied spectral analysis to engine room diagnostics and control room systems. Explored MLOps integration in maritime cyber-physical systems. Collaborations : Worked with international teams on projects like EDGE: Edge Analytics for Smart Diagnostics in Digital Machinery Concept (2018–2021). His work aligns with UN Sustainable Development Goals, particularly sustainable cities and climate action.

Sijia MaView profile

Assistant Professor

• Sharing Economy
• E-commerce
• Social Media
• +3 more

Sijia Ma is an Assistant Professor of Information Management in the Department of Management at Tilburg School of Economics and Management (TiSEM), Tilburg University. She holds a Ph.D. in Information Systems from the Guanghua School of Management, Peking University. Her research focuses on the economics of platform businesses, including sharing economy dynamics, omnichannel retail strategies, and social media impact analysis. She has published in journals such as Electronic Commerce Research and Applications and presented at conferences like ICIS and PACIS. Her research explores topics like the societal and industrial impacts of platforms, including how digital platforms influence urban development and consumer behavior. She also investigates the lifecycle of machine learning-based solutions and their alignment with business practices. Her work bridges theoretical insights with practical implications for digital business models. Dr. Ma teaches courses on data science research methods, business AI trends, and digital platforms. She actively contributes to the academic community through her research on platform governance, social media communication dynamics, and the ethical dimensions of emerging technologies. Her teaching portfolio includes modules such as Data Science Research Methods Business AI & Emerging Trends Digital Platforms Information Management MSc Thesis Guidance

Engin ErenView profile

Research Fellow

• Machine Learning
• Deep Learning
• Experimental Particle Physics
• +3 more

Engin Eren is a Software Developer at Helmholtz Imaging since 2023, with a background in experimental particle physics and machine learning research. He previously worked at DESY, contributing to proton structure measurements and CMS group operations at CERN, followed by a private-sector stint as a DevOps engineer. PhD in experimental particle physics (2018), focusing on the Compact Muon Solenoid (CMS) experiment at DESY. Postdoctoral research at DESY (2018-2021) under the Helmholtz AI grant, specializing in Machine/Deep Learning R&D and MLOps for imaging detectors in future linear collider experiments. Current role involves advancing imaging pipelines, deep learning applications, and fostering collaborations with research units at Helmholtz Imaging. His research interests span Machine Learning , Deep Learning , Imaging Detectors , and Scientific Computing , with a focus on integrating AI into experimental physics workflows. Scientific Awards: Helmholtz AI grant (fellowship for postdoctoral research). Engin supports Helmholtz Imaging's IT infrastructure and leads innovation in deep learning pipelines. He is based at DESY's Hamburg campus (Notkestrasse 85, D-22607 Hamburg) and collaborates with DESY IT – Research and Innovation in Scientific Computing (RIC) .