Find Academic Supervisors

Ullrich MartinView profile

Professor

• Railway Engineering
• Transport Systems
• Fault Diagnosis
• +7 more

Ullrich Martin is a Professor and Head of the Institute of Railway and Transport Engineering at the University of Stuttgart, Germany. His work spans railway systems, infrastructure modeling, and transport policy. Key areas include fault diagnosis, simulation-based optimization, and capacity evaluation. Contact: Pfaffenwaldring 7, 70569 Stuttgart, Germany Office Hours: By agreement Research Trends: Recent publications focus on GAN-based data augmentation for railway fault detection, high-speed maglev systems optimization, and early instability detection in ballast tracks. His team explores discrete element modeling, operational risk analysis, and machine learning applications for track condition monitoring. Infrastructure Innovation: He drives digitalization in railway planning through graph-based knowledge databases and automated compliance tools. Projects address dwell time forecasting, electromagnetic suspension design, and accessibility solutions like the Sinn²-App for visually impaired passengers. Operational Modeling: His work includes tabu search algorithms for dispatching, reinforcement learning for timetable calibration, and multi-scale simulation frameworks. He investigates interactions between track irregularities and vehicle dynamics, with a focus on safety and efficiency.

• Intelligent Transportation Systems
• Connected and Automated Vehicles
• Highway Traffic Safety
• +3 more

Niloufar Shirani is an Assistant Research Professor in the Department of Civil and Environmental Engineering at the University of Connecticut's College of Engineering. Her research focuses on intelligent transportation systems, traffic safety, and sustainable transportation, with expertise in crash modeling, connected/automated vehicles, and transportation-public health intersections. Dr. Shirani earned her Ph.D. from the University of Alabama in 2020. Her educational background emphasizes transportation engineering methodologies and statistical analysis applied to real-world infrastructure challenges. Her primary research areas include Intelligent Transportation Systems (ITS), Driving Simulator Studies for connected and automated vehicles, Highway Traffic Safety, Crash Modeling and Analysis, Transportation and Public Health, and Sustainable Transportation. She employs advanced data-driven techniques like mixed logit modeling, unsupervised classification, and spatial analysis to investigate driver behavior, infrastructure safety, and policy impacts. Analysis of her recent publications reveals a dominant focus on traffic safety analytics, particularly distracted driving, automated vehicle interactions, and infrastructure-related crash risks. Her work frequently leverages Connecticut and Alabama case studies, demonstrating strong state transportation department collaborations. She also explores critical intersections between transportation systems and public health outcomes, including accessibility services and environmental sustainability. Dr. Shirani actively contributes to advancing behavioral safety tools for the Connecticut Department of Transportation and utilizes driving simulator facilities to evaluate human factors in emerging vehicle technologies. Her research directly informs transportation policy and infrastructure design through evidence-based safety performance functions and risk assessment frameworks.

Lihui WangView profile

Professor

• Sustainable Manufacturing
• Human-Robot Collaboration
• Brain Robotics
• +5 more

Lihui Wang is a Professor and Chair of Sustainable Manufacturing at KTH Royal Institute of Technology in Sweden. He leads the Centre of Excellence in Production Research (XPRES) and holds leadership roles in organizations like CIRP and NAMRI/SME. His research focuses on human-robot collaboration, digital twin technology, and Industry 5.0 . He has authored over 750 publications and received numerous awards, including the SME Gold Medal (2024). Education: PhD and MSc from Kobe University (Japan), 1993 and 1990 BSc from China, 1982 Research Interests: Prof. Wang’s work spans real-time monitoring, brain robotics, sustainable production systems , and cyber-physical systems . His recent efforts emphasize AI-driven smart manufacturing , including predictive maintenance and human-centric collaboration frameworks. His labs explore applications like mixed-reality assembly guidance and adaptive robotic systems . Awards & Recognition: 2024 SME Gold Medal 2020 '20 Most Influential Professors in Smart Manufacturing' Eight NRC Institute Awards (2002–2005) Fellowships: CAE, CIRP, ASME, SME, AET Grants & Labs: Leads projects like SMART (Predictive Maintenance for Pharma) and co-leads the Swedish Production Academy . His labs include the Human-Robot Collaboration Group and AI for Manufacturing Initiative .

• Statistical Data Analysis
• Computer Vision
• Digital Health
• +5 more

Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.

• Software Engineering
• Computer Security
• Machine Learning
• +5 more

Yun Lin is a researcher affiliated with Shanghai Jiao Tong University, with a former position at the National University of Singapore's School of Computing. Their work focuses on software engineering, computer security, and machine learning. Key research interests include formal methods for program verification, adversarial machine learning, phishing detection, and deep learning applications in security. Lin has collaborated extensively with institutions like NUS and the Institute of High Performance Computing, producing over 70 publications since 2014 across venues such as ICSE, USENIX Security, and NeurIPS. Research interests span security (e.g., phishing detection, smart contract vulnerabilities), program analysis (bug localization, delta debugging), and AI-driven software development (code editing recommendations, LLM integration). Their contributions address challenges in software reliability, adversarial robustness, and scalable security solutions. Prominent publications include works on automated program refinement with LLMs, phishing webpage identification via deep vision models, and conflict resolution in version control systems. Lin’s collaborations emphasize interdisciplinary approaches blending formal methods with machine learning. No awards or grants are explicitly listed in the provided text.

• Natural Language Processing
• Multimedia Networking
• Software Defined Systems
• +6 more

Jason Quinlan is a Lecturer in the Department of Computer Science at University College Cork (UCC), Ireland. He holds a PhD in Computer Science and has extensive experience in teaching undergraduate modules such as Introduction to Programming (CS1117), Problem-Solving (CS1022), and Team Software Project (CS3305). His current role includes coordinating the CSIT SOLAS support hub for first-year students. Previously, he served as a Senior Post-Doc Researcher focusing on Intelligent Video Delivery at the Edge for 5G networks with the SFI iVID Project and CONNECT institute. Research interests span Natural Language Processing , Video Streaming Optimization , Software Defined Networking , and 5G Infrastructure . He led the development of frameworks like D-LiTE (DASH performance evaluation) and GoDASH (Go-based HAS framework). His work emphasizes QoE/QoS metrics, adaptive streaming algorithms, and network simulation tools. Key achievements include publishing over 30 peer-reviewed articles (e.g., on DASH streaming, 5G dataset analysis), securing €21,744 in research grants, and supervising 2 PhD, 15 Master’s, and numerous undergraduate students. Collaborations span institutions like UC Riverside (USA) and Université Clermont Auvergne (France). He actively contributes to conferences like ACM Multimedia Systems and IEEE LANMAN. Teaching and outreach activities include the MISL Summer of Code initiative mentoring over 60 students since 2018, and completing certifications in Project Management (PRINCE2, Scrum) and Higher Education Teaching. He has reviewed for top journals including Multimedia Systems Journal and IEEE Transactions on Multimedia, and serves on program committees for ACM MM and NetSoft conferences.

Pooja RaniView profile

Research Fellow

• Empirical Software Engineering
• Green Software Engineering
• Natural Language Processing
• +4 more

Pooja Rani is a Senior Researcher at the Software Evolution and Architecture Lab (SEAL), Department of Informatics, University of Zurich. She holds a PhD from the University of Bern (2022) and an M.E. in Software Systems from BITS Pilani (2017). With industry experience at Samsung, VMware, and People Interactive Ltd., she bridges theoretical and practical software engineering. Her research focuses on: Empirical Software Engineering, particularly developer practices in code comprehension and maintenance. Green Software Engineering, including energy anti-patterns and sustainability tooling. NLP and machine learning applications in software evolution, documentation quality, and polyglot environments. AI-driven approaches for debugging, testing, and optimization. Her publications emphasize energy efficiency in software systems, AI-assisted development tools, code comment analysis, and metamorphic testing. Recent work explores LLMs for sustainable coding, object-centric debugging, and autonomous system validation. She actively advises students, with ongoing supervision of 15+ BSc/MSc theses on topics like comment quality, energy anti-patterns, and polyglot development. She collaborates internationally with institutions including TU Delft, Inria, and the University of Victoria.

Saeid HabibiView profile

Professor

• Control Systems
• Automotive Engineering
• Hybrid Vehicles
• +6 more

Dr. Saeid Habibi is a Tier I Canada Research Chair and a full Professor in the Department of Mechanical Engineering at McMaster University. He leads the Centre for Mechatronics and Hybrid Technologies (CMHT), specializing in advanced automotive research, including battery management systems, autonomous driving, and electric vehicle technologies. His research focuses on control systems, state estimation, and fault detection/diagnosis, with applications in automotive, aerospace, and robotics sectors. Education: B.Sc. (Hons) in Control Engineering, University of Dundee, 1986 Ph.D. in Control Engineering, University of Cambridge, 1990 Research Interests: Dr. Habibi’s work spans control systems, hybrid vehicles, battery management, and mechatronics . He has pioneered the Smooth Variable Structure Filter (SVSF) for state estimation and fault detection. His interdisciplinary expertise bridges academia and industry, with collaborations in automotive electrification, fluid power, and actuation systems. Key projects include the development of prognostic tools for battery health and the creation of advanced test equipment commercialized globally. Key Contributions: Founder and CEO of EECOMOBILITY Inc., a startup focused on AI-driven vehicle diagnostics. Director of the CMHT lab, which houses cutting-edge automotive research facilities. Recipient of prestigious awards, including the ASME and CSME Fellowships, and multiple NSERC grants. Awards & Recognition: 2022 Dean’s Doctoral Mentoring Honour Roll 2015 ASME Fellowship 2012 IEEE Transportation Electrification Best Paper Prize Teaching & Mentorship: Dr. Habibi teaches advanced courses in mechatronics and hybrid vehicle design. He has mentored over 100 graduate students, many of whom now hold leadership roles in industry and academia. His NSERC CREATE initiative promotes interdisciplinary education in vehicle electrification. Labs & Partnerships: CMHT collaborates with industry leaders like Ford, Honda, and Bombardier, focusing on electrified powertrains, battery diagnostics, and autonomous systems. The lab supports large-scale projects funded by NSERC and Ontario’s Research Fund.

Fabio PrevidiView profile

Professor

• Control Systems
• Mechatronics
• Aerospace Engineering
• +5 more

Fabio Previdi is a Full Professor in Control Systems at the Department of Management, Computer and Industrial Engineering, University of Bergamo. His career spans roles from Research Assistant at the University of Glasgow (1999-2000) to Associate Professor (2006-2017) before assuming his current position. He holds a PhD in Control Systems from Politecnico di Milano (1999), MSc in Electronic Engineering (1993) and Physics (2008) from Politecnico di Milano and University of Milan. Research focuses on control systems, mechatronics, and aerospace applications. Key areas include electromechanical actuators, fault detection, system identification, and biomedical engineering. Over 150 peer-reviewed papers highlight contributions to topics like robust control, machine learning for industrial systems, and pandemic modeling. Publications (2023-2020) address cutting-edge topics such as jamming detection filters for aircraft actuators, agent-based pandemic simulations, and kernel-based system identification. Awards: Not explicitly mentioned but notable contributions recognized through extensive publication record.

• Electrical Engineering
• Energy Systems Optimization
• Building Energy Efficiency
• +5 more

Benoit Delinchant is a Professor at Université Grenoble Alpes, affiliated with the National School of Energy, Water and the Environment (Ense3) and the Department of Electrical Engineering and Industrial Computing. He serves as co-head of the MAGE research team (Models, Methods and Methodologies Applied to Electrical Engineering) at G2ELab since 2015 and co-facilitator of the Thematic Group for Complex Systems within GdR SEEDS. His educational leadership includes roles as Director of Studies and co-head of the GREEN thematic program at GS@UGA. Education: 2011: Habilitation to Direct Research (HDR), University of Grenoble 2003: Ph.D. in Electrical Engineering, INP Grenoble 2000: DEA in Electrical Engineering (Master's equivalent), INP Grenoble 2000: Engineering Degree in Electrical Engineering, INP Grenoble His research develops methods for modeling, simulation, and optimization of electrical energy systems, spanning electromagnetic components to multi-energy networks and building efficiency. Key focus areas include: Automatic modeling strategies for energy system design Uncertainty management in complex energy networks Optimization techniques for building-grid interactions Human-in-the-loop control systems His work integrates computational intelligence with practical applications in sustainable urban systems. Recent publications emphasize data-driven approaches to energy flexibility, carbon-aware systems, and hybrid modeling. Dominant themes include machine learning applications for load forecasting, lifecycle assessment methodologies, and experimental validation of demand response strategies. Cross-cutting focus on sustainability links building-scale optimizations with grid-level decarbonization challenges. Scientific Awards: No awards mentioned in source materials. Academic Supervision & Projects: Supervised 19 completed PhD theses and 6 ongoing doctoral candidates Led 4 study engineers, 6 post-docs, and managed 28+ Master's internships Major projects: Singapore NRF CREATE DesCartes (2021-26), PEPR TASE HyMES (2022-27), H2020 COLLECTiEF (2021-25), Grenoble IDEX OTE Observatory (2022-25), ANR COSIMPHI/INTENS/PRECCISION Leads research within G2ELab's MAGE team, collaborating with ENEDIS, EDF, ENGIE, Schneider Electric, CEA, and CSTB. Coordinates international partnerships including Brazil (UFSC) and Vietnam (USTH) for energy transition research.

Jan CarlsonView profile

Professor

• Software Engineering
• Embedded Systems
• Model-Based Development
• +5 more

Jan Carlson is a Professor at Mälardalen University's School of Innovation, Design and Engineering, specializing in the Division of Computer Science and Software Engineering. His primary focus is on software engineering, particularly model-based and component-based development of embedded systems. He earned his M.Sc. from Linköping University (2000) and a doctoral degree from Mälardalen University (2007) with a thesis on event pattern detection for embedded systems. Research interests include allocation optimization, timing analysis, code generation, real-time systems, and safety-critical component reuse. His work often addresses challenges such as cache-aware response time analysis, limited preemption scheduling, and integration of GPU components in embedded systems. He has contributed to projects like SCALL (a component allocator for heterogeneous systems) and Rubus (a GPU-aware component model). His research also explores continuous integration practices in model-based development and decision support systems for component selection. Key contributions include studies on architectural change management, ontology development for systems of systems, and tools for consistency checking between system models and implementations. He has authored over 80 publications, with recent work focusing on performance prediction, resource utilization, and safety-critical system integration. No notable awards are mentioned, though his extensive publication record highlights significant academic impact. Jan Carlson has advised numerous students and research teams but no explicit student names are listed in the provided text. His work is closely tied to industrial collaboration, addressing real-world challenges in automotive, railway, and smart industry domains. He actively participates in workshops and conferences, such as MARCH and ModComp, to advance model-driven engineering practices.

• Ontology
• Semantic Web
• IoT
• +7 more

Stijn Verstichel is a Postdoctoral Researcher at Ghent University within the Faculty of Engineering and Architecture and the Department of Information Technology (EA05) . He is affiliated with IMEC , focusing on ontology-driven solutions for context-aware systems in healthcare, IoT, and railway domains. Research Interests: Ontology, semantic web technologies, IoT, eHealth, context-aware systems, and machine learning. Recent Trends: His 15 most recent articles emphasize semantic frameworks for IoT, predictive maintenance dashboards, time-series data on Solid pods, and ontology-enabled healthcare platforms. Scientific Awards: No awards explicitly mentioned in the provided data.

Moncef ChiouaView profile

Associate Professor

• Chemical Engineering
• Control Systems
• Machine Learning
• +3 more

Moncef Chioua , Associate Professor at Polytechnique Montréal , specializes in Chemical Engineering with a focus on process data analysis , machine learning , and control systems for industrial process monitoring and operator support. His work spans pulp and paper , petrochemicals , and energy generation sectors. Education: Ph.D. in Chemical Engineering (Polytechnique Montréal, 2008) DEA in Control Systems and Digital Signal Processing (INPL, 2004) Engineering Degree in Continuous Process Automation (ESSTIN-INPL, 2003) Research Interests include fault detection , real-time optimization , and interpretable AI for industrial systems. His recent work applies neural networks and statistical methods to chemical processes, stem cell manufacturing, and metallurgical control. Publications highlight trends in alarm data analysis , disturbance classification , and hybrid modeling . Notable collaborations include ABB Corporate Research and FP-Innovations . Scientific Awards: Recipient of NSERC Discovery Grant (2021) totaling $3.9 million for research programs Advising & Grants: Supervised Nguyen, B.'s 2024 Master's thesis on interpretable neural networks for fault detection Principal Investigator for projects in industrial AI, alarm management, and process optimization Labs & Teams: Affiliated with the Institute for Data Valorization (IVADO) and previously contributed to ABB's process optimization group (2008-2020) and PAPRICAN's control systems research (2003-2004).

• Machine Learning
• Visual Analytics
• Explainable AI
• +7 more

Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.

• Semantic Web
• Knowledge Graphs
• Machine Learning
• +3 more

Ahmet Soylu is a researcher at Oslo Metropolitan University , Norway, with a focus on Semantic Web , Knowledge Graphs , and Machine Learning applications in Cloud Computing and Smart Manufacturing . His work bridges semantic technologies with data-driven solutions for industrial contexts, particularly in collaboration with Bosch . Key research themes: Knowledge Graph Embeddings, Cloud Cost Optimization, Industrial Data Analytics Collaborations: Dumitru Roman, Evgeny Kharlamov, Radu Prodan Recent publications (2024–2025) explore hyperbolic knowledge graph embeddings, sustainable LLM inference, and cloud storage optimization. These works integrate semantic modeling with ML for edge-cloud systems and industrial data extrapolation. His methodology emphasizes graph-based approaches for cloud cost modeling, microservice scheduling, and AI innovation discovery in open-source repositories. Applications include welding quality monitoring, maritime supply chain optimization, and semantic ML pipelines.