Find Academic Supervisors

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +13 more

Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.

Frank RudziczView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Healthcare
• +12 more

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

• Software Architecture
• Microservices
• Cloud Computing
• +7 more

Uwe Zdun is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice-Director of Studies for Computer Science and Head of the Research Group Software Architecture. His teaching portfolio includes core courses such as Software Engineering 2, Advanced Software Engineering, and Practical Software Courses for Bachelor's and Master's theses across multiple semesters (2024W-2025S). His research spans software architecture with emphasis on microservices, cloud computing, and DevOps. Key focus areas include architectural design decisions, infrastructure-as-code conformance, security in distributed systems, and the integration of machine learning operations (MLOps/RLOps). He investigates cognitive aspects of architecture practices through controlled experiments and develops model-driven approaches for quality assessment in complex systems. Recent publications (2024-2026) reveal three dominant trends: (1) Security and coupling analysis in infrastructure-as-code deployments, (2) MLOps/RLOps integration for Industry 4.0 cyber-physical systems, and (3) Performance optimization patterns for CI/CD pipelines and autoscaling. His work bridges theoretical architecture models with industrial practice, particularly in microservice ecosystems and reinforcement learning applications. Professor Zdun leads the Research Group Software Architecture at the University of Vienna's Faculty of Computer Science. The group focuses on empirical validation of architectural patterns, tool development for conformance checking, and advancing design decision methodologies in cloud-native and AI-driven systems.

• Cyber-Physical Systems
• Machine Learning
• Self-Adaptive Systems
• +4 more

Dr. Ilias Gerostathopoulos is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute and the Department of Information Management & Software Engineering. He specializes in self-adaptive systems, cyber-physical systems, and machine learning operations (MLOps). His work focuses on software architectures for autonomous systems, decision-making under uncertainty, and experiment-driven adaptation frameworks. He teaches courses such as 'Fundamentals of Adaptive Software' and 'Information Management', emphasizing practical applications of adaptive systems and data-driven decision-making. Gerostathopoulos has been awarded the Best Presentation Award (2021) for contributions to evaluating self-adaptive systems. His research addresses challenges in industrial self-adaptation, MLOps architectures, and robotics. Key research themes include: Architecture-based self-adaptation in robotics and CPS MLOps frameworks and systematic analysis of AI systems Uncertainty management in autonomous systems Experiment-driven learning and tool development Notable contributions include the ExpEngine tool for workflow optimization and the ReBeT framework for robotic systems. His work bridges theoretical software engineering with practical industrial implementations.

Magnus BångView profile

Associate Professor

• Artificial Intelligence
• Human-Computer Interaction
• Automation
• +3 more

Magnus Bång is a Senior Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on advancing human-AI collaboration, automation systems, and AI applications in domains like cyberphysical production, air traffic management, and process industries. He has contributed to interdisciplinary projects involving real-time human-automation interfaces, explainable AI dashboards for industrial processes, and safety-critical systems integration. His work bridges theoretical AI advancements with practical implementations in sectors such as aviation and maritime logistics. Notable collaborations include research with the Swedish Maritime Administration to enhance shipping efficiency through AI and interactive visualization. He actively participates in EU-funded initiatives like the Horizon 2020 projects targeting autonomous systems and air traffic control. Research interests span MLOps for industrial systems, glyph-based communication design for human-automation teams, and operator modeling across traffic management domains. His publications emphasize cross-disciplinary solutions to challenges in automation and human-centric AI design.

• Software Engineering
• Industrial Automation
• Cloud Computing
• +7 more

David Hästbacka is an Associate Professor (tenure track) at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on software engineering, industrial automation, and energy systems, emphasizing system architecture, interoperability frameworks, and dependable IoT solutions. He leads a research group exploring edge and cloud computing, semantic integration, and smart energy systems. Education & Professional Background : While specific educational details are not provided, his academic career includes roles such as Postdoctoral Researcher in the SEMIS project (2017-2020) and extensive involvement in EU-funded initiatives like COCOP (EU H2020) and Horizon Europe projects. Research Projects : Active in high-impact projects like Hedge-IoT (Horizon Europe, 2024-2027), TwinfFlow (Business Finland), and TRINEFLEX (Horizon Europe), with a focus on industrial automation, distributed systems, and energy grids. Past projects include FEMMa (Business Finland), DisMa (Academy of Finland), and Arrowhead (ECSEL). Teaching & Supervision : Specializes in Web/Cloud architectures, IoT systems, and dependable automation technologies. Supervises students in topics like edge computing frameworks and MLOps pipelines. Technical Contributions : Develops frameworks for industrial interoperability (e.g., OPC UA PubSub integration), edge-cloud toolchains, and MLOps methodologies. His work addresses challenges in microservices, Kubernetes distributions, and semantic data integration. Labs & Teams : Leads a research group advancing automation technologies through interdisciplinary collaboration, with partnerships in industry and academia to bridge theory and practice in smart systems.

Jukka K NurminenView profile

Professor

• Efficient Software Systems
• Energy-Efficient Software
• Mobile Peer-to-Peer
• +6 more

Jukka K Nurminen is a Professor of Computer Science at the University of Helsinki (since 2019) and a Research Professor at VTT. He leads the Empirical Software Engineering research group and supervises doctoral students in the Doctoral Programme in Computer Science. His career spans academia and industry, including roles as Adjunct Professor at Aalto University (part-time, 2016-2021) and Principal Scientist at VTT (2016-2019). His research focuses on efficient software systems , particularly energy-efficient software , mobile cloud computing , and data-intensive systems . Recent work addresses AI system testing , ethical decision-making in software , and quantum computing software . His publications highlight trends in quantum algorithms , machine learning for edge computing , and ethical AI . Best Paper Award (2023) Nurminen has supervised 6 PhD theses, 48 MSc theses, and 21 BSc theses. He has secured over 1 MEUR in research funding, including projects like FrameQ and EM4QS for quantum middleware. His teaching innovations include hackathons and summer schools, with excellence recognized in tenure-track evaluation (2018) and adjunct professorship (2015).

Leila MethnaniView profile

Researcher

• Artificial Intelligence Ethics
• Explainable AI (XAI)
• Trustworthy AI
• +5 more

Leila Methnani is a doctoral student at the Department of Computing Science at Umeå University . Her research focuses on ethical and sociotechnical challenges in artificial intelligence, particularly in areas such as AI alignment , trustworthy AI , and human-AI collaboration . She has published extensively on topics including Explainable AI (XAI) , MLOps , and hybrid human-AI systems . Her recent work explores: Trustworthy AI in variable autonomy robotic systems Explainable and counterfactual-based AI interfaces for industrial operators Operationalizing AI ethics through socio-technical assessments Real-time reactive planning with social norms in multi-agent systems She has co-authored publications in journals such as Ethics and Information Technology , ACM Computing Surveys , and Frontiers in Artificial Intelligence . She is reachable via email at leila.methnani@umu.se .

• Software Engineering
• Requirements Engineering
• Embedded Systems
• +2 more

João Miguel Lobo Fernandes is a Full Professor at the Department of Informatics, School of Engineering, University of Minho. He holds a 5-year degree in Informatics Engineering (1991), a Master's in Informatics (1994), and a Ph.D. in Informatics/Computer Engineering (2000) from Universidade do Minho, with a thesis on An object-oriented methodology for embedded systems development . Research interests: Software Modeling, Requirements Engineering, Embedded Software, Software Process, Bibliometrics International collaborations: University of Bristol, Turku Centre for Computer Science (Finland), Aarhus University (Denmark), Universidade Federal de Santa Catarina (Brazil), and others Key projects: iFlow and DIABO logistics platforms (awarded by APLOG) His 160+ peer-reviewed publications focus on software methodologies, embedded systems, and industry collaborations. Recent work includes applications of machine learning in automotive software, feature modeling, and remote work impact studies. He serves on editorial boards for Journal of Information Technology Research and Open Computer Science . 2016 & 2022 Logistics Excellence Awards (APLOG) Organized international events: ACSD, DIPES, GTTSE, PETRI NETS, ICSOB, MOMPES As an academic leader, he directed the 5-year Systems and Informatics Engineering degree (2004-06), Master's in Informatics Engineering (2011-12), and served on Scientific Council of the School of Engineering (2013-16).

• Artificial Intelligence
• Data Science
• Generative AI
• +7 more

Jan Kirenz is a Professor at Stuttgart Media University of Applied Sciences (HdM Stuttgart) and a freelance Consultant, Lecturer, and Data Scientist specializing in Generative AI and data science. With a strong academic foundation, he earned a Doctorate (Dr. oec. publ.) from the Graduate School of Business at the University of Zurich and a Diploma in Business Administration (Operations Research) from the University of Tübingen. His work bridges the gap between technical complexity and practical application, focusing on transformative AI integration for businesses. His research interests center on Generative AI, Machine Learning, and Digital Transformation, with a particular emphasis on Prompt Engineering, agentic AI, and MLOps. He collaborates with companies such as Mercedes-Benz Group, EnBW, and Alfred Kärcher SE & Co. KG to develop tailored AI strategies and solutions. His practical approach ensures measurable results and actionable strategies for enhancing competitiveness. Jan leverages advanced tools and frameworks, including Google Gemini, OpenAI ChatGPT, Anthropic Claude, Hugging Face, Kubeflow, and cloud platforms like Microsoft Azure and Google Cloud AI. He is active in GitHub repositories related to AI development, with a focus on the Model Context Protocol and other cutting-edge technologies. His current projects prioritize practical AI applications for business processes, from prototyping to deployment.

Fatih NayebiView profile

Lecturer

• Information Systems
• Data Science
• Machine Learning
• +5 more

Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +4 more

Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.

• Software Engineering
• Machine Learning Operations (MLOps)
• Cyber-Physical Systems
• +4 more

Prof. Dr. Klaus Schmid is a Professor in the Department of Software Systems Engineering at the University of Hildesheim, part of the Faculty of Mathematics, Natural Sciences, Economics, and Computer Science. His research focuses on Machine Learning Operations (MLOps), software product lines, adaptive systems, and variability modeling. He leads projects such as EXPLAIN and ReGaP, emphasizing explainable AI and industrial MLOps integration. His work addresses challenges in Cyber-Physical Production Systems (CPPS), including data management, model calibration, and domain knowledge integration. Key research interests include MLOps architecture design, variability modeling transformations (e.g., UVL to IVML), and incremental verification techniques for software product lines. He has published extensively in venues like IEEE ETFA, IEEE Software, and SPLC conferences. Notable achievements include a Best Paper Award for work on control patterns in self-adaptive systems. Collaborations with industry partners highlight his focus on bridging academic research and practical industrial applications. Prof. Schmid’s contributions extend to tool development, such as EASy-Producer for variability-aware software ecosystems, and frameworks for environment modeling in adaptive systems. His research addresses both foundational challenges (e.g., syntax-preserving slicing) and applied topics like MLOps platform comparisons and industrial case studies in Industry 4.0.

Beyza EKENView profile

Assistant Professor

• Software Defect Prediction
• Natural Language Processing
• Machine Learning in Software Engineering
• +2 more

Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.

• Information Processing Systems
• Internet of Things (IoT)
• Edge Computing
• +4 more

Marcello Pietri is a Researcher (td art. 24 c. 3 lett. A) and Contract Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia. His work focuses on Information Processing Systems, with expertise in IoT, Edge Computing, and Digital Twins. He teaches courses like 'Sistemi Informativi' (Information Systems) for the Engineering Management program, emphasizing database design, SQL, and web application development with Python. His research explores advanced topics including Digital Twin integration in Industry 5.0, Fluid Computing in IoT ecosystems, and Smart City data fusion using 5G MEC architectures. He collaborates with institutions like the DIPI Lab (Distributed and Pervasive Intelligence Group), contributing to projects on telecom security, energy forecasting, and human-centric manufacturing systems. Recent publications highlight innovations in operator digital twins for workplace well-being, distributed data mesh models for IoT-edge-cloud systems, and adaptive monitoring algorithms for large-scale cloud environments. His work bridges theoretical advancements with practical implementations, addressing challenges in scalability, real-time data processing, and interdisciplinary collaboration.