Find Academic Supervisors

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +3 more

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

• Immersive Multimedia Learning
• Computer-Supported Collaborative Learning (CSCL)
• Productive Failure
• +3 more

Dr. Christian Hartmann serves as an Academic Councillor and Research Assistant at the Chair of Teaching and Learning with Digital Media within the TUM School of Education since 2020. With a PhD in Educational Science from Ruhr University Bochum (2020) and prior research assistant experience at Ruhr University's Educational Psychology Chair (2015-2020), he brings extensive expertise in digital learning environments. Bachelor's & Master's in Education and History from Ruhr University Bochum Visiting scholar at Carnegie Mellon University's HCI Institute (2016) His research focuses on immersive learning technologies , including: Vicarious failure mechanisms in education Computer-supported cooperative learning (CSCL) Adaptive learning systems with VR/AR Coherence formation in multimedia environments Key publication trends show significant contributions to: Productive failure analysis (2018-2025) Social VR collaboration tools (2023) Immersive technology's impact on special education (2023) AI integration in science education (2025) As a program committee member for DELFI 2022 VR/AR Learning workshop and reviewer for journals like Learning & Instruction , he actively shapes educational technology research. His work has been presented at major conferences including EARLI (Austria 2025, Germany 2024), DGPs Congress (Austria 2025), and AERA Annual Meeting (USA 2025). Current affiliations include the European Association for Research on Learning and Instruction (EARLI) , International Society of the Learning Sciences (ISLS) , and German Society for Psychology (DGPs) . The Immersive Virtual Reality Lab at TUM School of Education serves as his primary research environment.

• Mass Spectrometry
• Imaging Mass Spectrometry
• MALDI Imaging
• +4 more

Raf Van de Plas is a researcher at Delft University of Technology specializing in mass spectrometry imaging and data analysis. His work focuses on developing advanced techniques for molecular analysis of biological tissues, particularly in kidney research and lipidomics. With a PhD in Mechanical Engineering, he bridges engineering principles with biomedical applications. His research interests include: Mass Spectrometry Imaging MALDI Imaging Techniques Lipid Analysis and Mapping Kidney Tissue Molecular Profiling Computational Data Analysis Biomolecular Imaging Algorithms Dr. Van de Plas has published extensively in high-impact journals with multiple 2025 publications in Science Advances, Nature Communications, Kidney International, and Analytica Chimica Acta. His work demonstrates a clear trajectory toward developing innovative computational workflows for mass spectrometry data while applying these techniques to solve critical biomedical problems, particularly in kidney pathology and lipid metabolism. His scientific contributions include: Development of advanced MALDI matrices for high spatial resolution imaging Creation of msiFlow automated analysis workflows Comprehensive lipid mapping of human kidney tissues Integration of mass spectrometry with immunofluorescence microscopy Dr. Van de Plas actively supervises research work and collaborates internationally across biomedical and engineering disciplines, with significant contributions to both methodological development and clinical applications of imaging mass spectrometry.

Soroush SabbaghanView profile

Associate Professor

• Generative Artificial Intelligence in Education
• Educational Technology
• Teacher Development
• +6 more

Soroush Sabbaghan is an Associate Professor at the Werklund School of Education at the University of Calgary and serves as the inaugural Generative AI Educational Leader in Residence at the Taylor Institute for Teaching and Learning. He also holds the position of Area Chair for Language and Literacy Specialization at the Werklund School of Education. With dual PhDs in Curriculum and Learning from the University of Calgary (2015) and English Language Teaching from Tehran Azad University (2011), his academic journey spans both Canadian and international educational contexts. Dr. Sabbaghan's research focuses on the critical intersection of educational technology, teacher development, and equity, with a particular emphasis on the ethical integration of generative AI in educational settings. His work explores how AI-powered tools can support inclusive and evidence-informed teaching practices across K-12 and higher education environments. He has developed multiple AI applications including REVIA, AI4Equity, CaseCraft, SMARTIE, COERI, and PEARL to enhance teaching and learning experiences while maintaining academic integrity. His scholarly output reveals a clear trajectory toward increasingly sophisticated applications of AI in education, with his most recent publications (2023-2025) centering on ethical frameworks, human-centered AI design, and practical implementations of generative technologies in academic settings. These works collectively address critical tensions between technological innovation and educational values, particularly regarding academic integrity, equity, and professional development. Teaching Award for Experiential Learning (2025) Teaching Award for Teaching in Online Environments (2021) Dr. Sabbaghan has secured significant research funding, including SSHRC grants and multiple University of Calgary Teaching and Learning Grants, to explore AI's impact on education. He actively collaborates with educators across Canada and internationally, serving as Principal Investigator on studies examining co-designed AI tools in teacher education. His leadership extends to directing the Generative AI Resource Development Working Group and co-chairing the Academic Generative AI Working Group at the institutional level. He has also developed numerous AI-powered educational applications through projects like the Math Minds initiative and various teaching innovation grants. As an active member of the AI Community of Practice at the University of Calgary and host of the AI Speaker Series, Dr. Sabbaghan fosters interdisciplinary dialogue about AI's role in education. His work with the International Foundations Program and as Liaison to the Haskayne School of Business demonstrates his commitment to cross-institutional collaboration. His recent development of tools like SMARTIE (an AI-powered course design tool featured in CityNews Calgary) exemplifies his practical approach to translating research into classroom applications.

• Flexible micro/nano electronics
• Printable devices
• Self-powered systems
• +3 more

Dr. Yuanjing Lin is an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology. Her research centers on nanostructured materials and fabrication techniques for printable/wearable electrochemical sensors and energy storage devices, with applications in self-powered systems, health/environmental monitoring, and intelligent robotics. Ph.D., Electronic and Computer Engineering, Hong Kong University of Science and Technology (2014–2018) Visiting Research Student, UC Berkeley (2018) B.Eng., Electronic Science and Technology, Nankai University (2010–2014) Her work bridges flexible electronics, self-powered sensing, and digital healthcare. She has contributed to over 50 publications in top journals like Nature , Nature Nanotechnology , and Science Advances , focusing on wearable biosensors, textile electronics, and energy storage systems. Articles highlight advancements in printable photonic materials, sweat-activated micro-batteries, and self-powered sensing systems, with trends in flexible micro/nano electronics, biomedical applications, and sustainable energy solutions. IAAM Fellow (2025) Nano Letters Early Career Board (2025) Nanoscale Emerging Investigators (2024) SUSTech Excellent Teaching Award (2023) iCanX Summit Best Paper Award (2022) Dr. Lin actively recruits graduate/postdoctoral researchers in sensors, flexible electronics, and biomedical engineering. She serves as Associate Editor for Frontiers in Nanotechnology and editorial board member for Nano Letters , Biosensors , and FlexMat .

Alessandro RaganoView profile

Researcher

• Machine Learning
• Audio Processing
• Quality of Experience (QoE)
• +6 more

Alessandro Ragano is a Postdoctoral Researcher at the Insight Centre for Data Analytics , where he has been investigating Quality of Experience (QoE) aspects of audio archives and developing data-driven approaches for QoE estimation and audio restoration using deep learning since 2018. Education: MSc in Computer Science and Engineering from Politecnico di Milano (Italy) BSc in Computer Engineering from Università Degli Studi di Salerno (Italy) His research integrates machine learning , audio signal processing , and multimedia quality assessment to improve speech enhancement, audio restoration, and perceptual modeling. Recent trends in his publications focus on self-supervised learning , objective quality metrics , and audio dataset generation with applications in speech separation, music representation, and audio inpainting. He actively contributes to open-source tools like Binamix and AQP for audio research and quality evaluation.

Ruwen QinView profile

Associate Professor

• Intelligent transportation systems
• Smart civil infrastructure systems
• Human sensing & analytics
• +8 more

Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.

Massimo MecellaView profile

Professor

• Service-Oriented Computing
• Business Process Management
• Process Mining
• +12 more

Massimo Mecella serves as Full Professor at Sapienza University of Rome's Faculty of Information Engineering, Computer Science and Statistics, where he leads the Processes, Services and Software Management research group and participates in the CINI National Cyber Security Lab. His academic foundation stems from a PhD in Computer Engineering earned at Sapienza in 2002. His research expertise spans service-oriented computing, business process management, and cyber-physical systems with emphasis on service composition, process mining, and adaptive distributed architectures. Key technical domains include IoT integration, digital twin development, and security frameworks for complex software ecosystems. Recent publications (2024-2025) demonstrate a pronounced shift toward Large Language Model (LLM) integration in business processes and smart manufacturing. Dominant themes include LLM-driven process modeling, multimodal quality control systems, and digital twin implementations for energy management and production optimization. Methodological innovations focus on retrieval-augmented generation (RAG) for service discovery and fuzzy cognitive maturity assessments. His distinguished recognition includes: ICSOC 2013 Most Influential Paper Award (2003-2012 decade) 2017 Best Paper Award As research group leader, Mecella directs projects in smart manufacturing, process mining, and cyber-physical security, with funding typically sourced from EU initiatives and industry partnerships in aerospace, healthcare, and energy sectors. His team maintains active collaborations with aerospace manufacturers through the MICS SPOKE8 project and develops frameworks like SAMBA for human-in-the-loop manufacturing systems. The Processes, Services and Software Management group operates within Sapienza's engineering faculty infrastructure, utilizing specialized labs for CPS/IoT testing and digital twin simulation, with strong ties to the CINI Cyber Security Lab for security validation.

Antonio PietrabissaView profile

Associate Professor

• Networked Systems
• Time-delay Systems
• Non-cooperative Control
• +4 more

Antonio Pietrabissa is an Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he earned his degree in Electronics Engineering (2000) and PhD in Systems Engineering (2004). He has been teaching Automatic Control and Process Automation since 2010 and holds the National Scientific Qualification as Full Professor in Systems and Control Engineering (09/G1). Research interests include networked systems, robust control, Markov decision processes, and deep reinforcement learning. His work spans telecommunications, biomedical applications, and space systems, with a focus on federated learning and decentralized control. He has authored ~70 journal papers (Scopus h-index 22) and co-invented a patent for model predictive control in motor disability assistance. Awards include the 2021 Cybersecurity Award and ETRI Journal Best Paper. Current projects are NANCY (6G networks) and CADUCEO (AI-driven medical diagnostics). He is also CEO of Sapienza startup Automation Intelligence and Control (AICO), commercializing AI solutions for space, telecom, and biomedical sectors.

• Sustainable Mobility
• Electrification of Transport Systems
• Shared and Connected Mobility
• +5 more

Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Applied artificial intelligence
• Artificial intelligence
• Deep learning
• +6 more

Flavio Giobergia is a Researcher at the Department of Control and Computer Science (DAUIN) within Politecnico di Torino . His work spans applied artificial intelligence , with a focus on deep learning and machine learning applications. Research Interests : Machine learning under limited label availability, LLM-assisted code refactoring, subgroup performance analysis in ASR models, exoplanet atmospheric reconstruction, and predictive maintenance systems. Teaching : Course owner for Data Science and Machine Learning Lab and Large Language Models at Politecnico di Torino. Projects : Scientific head for the MAD – STANDARD BANKING project (2025–2026) and ImEDA (2023–2024), focusing on anomaly detection and model efficiency. Publications : 15+ recent works on topics including machine unlearning benchmarks, synthetic data for hallucination detection, and drift detection limitations, presented at top conferences like KDD, Interspeech, and IEEE AICT. Collaborations : Active in the SmartData@PoliTO center and co-author with Elena Baralis, Alkis Koudounas, and others.

• Software Engineering
• Formal Methods
• Software Architectures
• +7 more

Patrizia Scandurra is an Associate Professor at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. Her academic appointment in Computer Science (01/B1) spans from 2022 to 2033. She previously held roles as a researcher at the University of Bergamo (2009-2017) and postdoctoral fellow at the University of Milan (2006-2008). She earned her PhD in Computer Science (2006) and Bachelor's degree (2002) from the University of Catania. Research Focus : Software architectures and formal methods for modeling, validation, and verification of software-intensive systems. Specializations : Runtime analysis of self-adaptive, autonomous, and uncertain systems including IoT-Edge-Cloud applications, embedded systems, and system-on-chip. Collaborations : STMicroelectronics, Atego, Bialetti, and ENEA. Conference Involvement : Program/organizing committees for ICSE, ASE, ISSRE, ICSA, ECSA, SEAMS@ICSE, ABZ, SA-TTA@SAC, FAACS@ECSA. Research Projects : Model-driven development for robotics, adaptive architectures for pervasive systems, big data in smart cities, and digital twins for medical systems. Notable Contributions : Development of the ASMETA formal method community tools and frameworks for rigorous system design. She has published over 100 peer-reviewed works in international journals and conferences.

Ruoxi JiaView profile

Assistant Professor

• Machine Learning
• Data Privacy
• Cybersecurity
• +4 more

Ruoxi Jia is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. from UC Berkeley and a B.S. from Peking University. Her research focuses on machine learning, security, privacy, and cyber-physical systems, with recent emphasis on data-centric AI and trustworthy machine learning. Education: Ph.D., Electrical Engineering and Computer Sciences, UC Berkeley (2018) B.S., Peking University Research interests include adversarial machine learning, AI safety, data valuation, and privacy-preserving techniques. Her work addresses challenges in AI ethics, backdoor detection, and scalable model security. Recent publications explore topics like defense mechanisms against poisoning attacks, large model safety surveys, and red teaming strategies. She actively seeks students (PhD, Masters, interns) and emphasizes collaboration through her group. Her work bridges theoretical foundations and practical applications in AI and cybersecurity, with contributions to both technical and ethical dimensions of modern machine learning systems.

Amine MhedhbiView profile

Assistant Professor

• Database Management
• Graph-Structured Databases
• Query Optimization
• +4 more

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.