Find Academic Supervisors

• Magnetic Resonance Imaging
• Neuro-Oncology
• Brain Tumor Imaging
• +3 more

Full Professor at Erasmus University Medical Center (Erasmus MC) in the Department of Radiology & Nuclear Medicine. Specializes in advanced imaging techniques for neurological disorders, particularly brain tumors. Her research focuses on magnetic resonance imaging (MRI), functional ultrasound, and AI-driven diagnostic methods in neuro-oncology. Dr. Smits has supervised 13 academic works and contributed over 255 publications, including high-impact studies on tumor progression differentiation and brain metastasis analysis. Her work bridges clinical practice and cutting-edge imaging technologies. Research interests include: Magnetic Resonance Imaging, Neuro-Oncology, Brain Tumor Imaging, and Development of Innovative Diagnostic Tools. Key contributions involve improving diagnostic accuracy through perfusion MRI and functional ultrasound, as well as integrating deep learning into medical imaging workflows. Major contributions: Mobile functional ultrasound brain imaging, data-centric AI for brain metastasis analysis Publications focus on MRI techniques for glioblastoma, dynamic susceptibility contrast imaging, and clinical applications Active in multidisciplinary collaborations, contributing to both academic and clinical advancements in neurological imaging.

Marcus HilbrichView profile

Professor

• Distributed Systems
• Software Engineering
• Cloud Computing
• +5 more

Marcus Hilbrich is a Professor in Computer Science with extensive academic experience across multiple institutions. His primary affiliation is Chemnitz University of Technology, where he held roles including Assistant Professor, Acting Professor of Software Engineering, and Research Assistant. He also served as Scientific Coordinator at Humboldt University of Berlin (SFB 1404 FONDA) from 2022–2024. Education: He earned a Doctor of Engineering (Dr.-Ing.) from Dresden University of Technology in 2015, focusing on job/user-centric monitoring in distributed environments. His diploma thesis (2008) explored performance analysis of parallel simulations using the SUN Niagara II architecture. Research Interests: Hilbrich specializes in distributed systems, software engineering, cloud computing, and workflow management. He investigates microservices architecture, resilience engineering, and scalable monitoring techniques. His work bridges theoretical foundations (e.g., formal methods for workflows) with practical applications in HPC and data analysis. Publications: He has authored/co-authored over 15 peer-reviewed articles, including key contributions on ßMACH software management guidance, validity constraints in data workflows, and microservices design patterns. His research often addresses interdisciplinary challenges in computational systems and software lifecycle management. Projects: Active involvement in third-party funded projects like SFC (Cloud Computing for Secure Financial Transactions), ECOUSS (parallel simulations), and WisNetGrid (knowledge networks in grids). His work often integrates performance analysis and system scalability. Labs/Teams: Central to his work is the SFB 1404 FONDA collaborative research center, focusing on foundational aspects of distributed heterogeneous environments.

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +2 more

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

• Spatial Data Infrastructures
• Geospatial Information Ecosystem
• Artificial Intelligence
• +3 more

Dr. Lesley Arnold holds an Adjunct Associate Professor position at Curtin University's School of Earth and Planetary Sciences within the Faculty of Science and Engineering. She specializes in governmental geospatial information policy, working globally with organizations like the United Nations and World Bank to strengthen geospatial management for sustainable development. As a lead author of the UN-GGIM Integrated Geospatial Information Framework (IGIF), she develops strategies for spatial data reform, open data initiatives, and SDI policies. Her career includes executive roles at Landgate, where she managed WA's geospatial programs, and leadership positions in intergovernmental committees such as ICSM and AuScope. Education: PhD in Spatial Science (Curtin University, 2005), BSc (First Class Honours) in Cartography (Curtin, 1996), and a Diploma in Cartography (Wembley Technical College, 1985). Awards include the SSSI Fellowship and D.B. Johnston Award for spatial sciences excellence. Research focuses on Spatial Data Infrastructures (SDIs), geospatial ecosystems powered by AI, data supply chains, and integrated geoinformation management. She has authored papers on semantic web technologies, geospatial provenance models, and decision-making frameworks for coastal management. Professional roles include Board Directorships at AuScope and AURIN, and executive leadership in global geospatial initiatives. Her work emphasizes collaboration across governments, academia, and industry to build sustainable geospatial data ecosystems.

Lena Maier-HeinView profile

Professor

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +3 more

Prof. Lena Maier-Hein is a full professor at Heidelberg University and managing director of the National Center for Tumor Diseases (NCT) Heidelberg. She leads the division of Intelligent Medical Systems (IMSY) at the German Cancer Research Center (DKFZ) and oversees the cross-topic program 'Data Science and Digital Oncology'. Her research focuses on machine learning in biomedical imaging, particularly surgical data science and computational biophotonics. She chairs the Surgical Data Science initiative and serves on editorial boards for journals like Nature Scientific Data and IEEE TPAMI. Her awards include the 2024 German Cancer Award, 2013 Heinz Maier-Leibnitz Prize, and European Research Council grants. She advocates for trustworthy AI in healthcare, co-developing frameworks like Metrics Reloaded and TRIPOD+ AI. Her work bridges academic, clinical, and industrial sectors through initiatives like the FeTS challenge. Key contributions include advancing photoacoustic imaging, surgical AI systems, and validation methodologies. She emphasizes ethical AI deployment and interdisciplinary collaboration to address clinical challenges.

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +12 more

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

• Computer Science
• Artificial Intelligence
• Robotics

Gonzalo Esteban Costales is a researcher affiliated with the University of Leon from 2012 to 2025, specializing in Computer Science and Artificial Intelligence within the Robotics research group. His work bridges haptic simulation, robotics, and educational technology. Education: Ph.D. in Computer Science (2020), University of Leon, with a thesis on haptic simulators for expert knowledge transfer . Research Interests: Haptic simulation for surgical training and rehabilitation Robotics security in industrial environments Educational technology frameworks Version control analytics for student performance Open-source vulnerability detection tools Key Article Trends: Recent publications focus on haptic workspace optimization, threat modeling for robotic production plants, and security enhancements in continuous integration workflows. His work spans simulation design, cybersecurity, and educational applications of robotics.

Weihua ZhouView profile

Associate Professor

• Health Informatics
• Medical Image Analysis
• Machine Learning
• +3 more

Weihua Zhou is a Tenured Associate Professor at Michigan Technological University in the College of Computing, with affiliations in Applied Computing, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, and Mathematical Sciences. He holds a PhD from Southern Illinois University Carbondale, an MS and B.Eng. from Wuhan University, and completed postdoctoral research at Emory University. Academic Positions : Tenured Associate Professor (2025–present), Tenure-Track Assistant Professor (2019–2025) at Michigan Tech; Nina Bell Suggs Endowed Professorship (2015–2019) at the University of Southern Mississippi. Research Interests : Focuses on medical imaging and health informatics , particularly machine learning applications in cardiovascular diagnosis , osteoporosis risk stratification , and senile dementia early detection . Additional work includes radiomics for COVID-19 severity assessment , federated learning in medical segmentation , and deep learning for proximal femur strength prediction . Scientific Awards : USM Nina Bell Suggs Endowed Professorship, USM College of Arts and Sciences Scholarly Research Award, American Heart Association Research Leaders Academy (2017, 2018), USM Butch Oustalet Distinguished Professorship Research Award. Lab & Tools : Leads the Medical Imaging & Informatics Lab (MIILab-MTU) and contributes to the NSF/MRI GPU Cluster. Open-sourced tools include KD4COVID19 for radiomics analysis and ECGTools for ECG classification.

• Visual Analytics
• Temporal Data Analysis
• Data Cleansing
• +3 more

Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.

Rüdiger von Eisenhart-RotheView profile

Professor

• Regenerative Medicine
• Osteoarthritis Research
• Osteo-Oncology
• +7 more

Prof. Dr. Rüdiger von Eisenhart-Rothe is a full Professor and Chair of Orthopedics at the Technische Universität München (TUM), leading the Department of Orthopedics and Sports Orthopedics at the Klinikum rechts der Isar. His research focuses on regenerative medicine, osteo-oncology, endoprosthetics, and the application of machine learning in surgical planning. He completed his medical studies at LMU Munich and business administration at the University of Hagen, followed by a habilitation in orthopedics at Frankfurt’s Friedrichsheim Hospital. Notably, he received the Perthes Prize twice (2003, 2010) for contributions to shoulder and elbow surgery. His work integrates advanced imaging techniques, virtual planning, and biomaterial research to address challenges in joint replacement, infection control, and sarcoma management. Recent projects emphasize AI-driven diagnostic tools and personalized surgical approaches. Prof. von Eisenhart-Rothe collaborates with interdisciplinary teams to advance clinical outcomes in orthopedic surgery, particularly in knee and hip arthroplasty. Awarded the Perthes Prize twice, his contributions span academic leadership, clinical innovation, and translational research at TUM’s School of Medicine and Health. Current initiatives include optimizing prosthetic alignment via 3D modeling and evaluating synovial biomarkers for infection diagnosis.

• 6G Networks
• Telemedicine
• Medical Robotics
• +3 more

Nicolai Kröger is a researcher at the Chair of Communication Networks (Prof. Kellerer) at the Technical University of Munich (TUM). He holds an M.Sc. in Electrical and Computer Engineering from TUM, where his thesis focused on P4 switch performance modeling using queuing theory. His current research centers on 6G networks for critical telemedicine applications, particularly within the 6G-Life project, emphasizing end-to-end communication for medical robotics and surgical systems. He contributes to projects like the 6G Future Lab Bavaria and collaborates with the MITI group at Rechts der Isar Hospital to develop medical testbeds requiring high availability and low latency. Kröger also supervises student theses on 5G/6G security, network optimization, and in-network computing. His technical expertise spans programmable networks (P4), SDN, and performance analysis of network devices. He serves as a supervisor for student projects and internships, including implementations of medical testbeds and security analyses of cellular broadcast messages. His work bridges academic research with practical applications, aiming to advance communication networks for healthcare and future 6G systems.

• Model-Driven Engineering
• Robotics Software Engineering
• Software Architecture
• +3 more

Federico Ciccozzi is an Associate Professor in Computer Science at Mälardalen University's School of Innovation, Design and Engineering, where he leads the ASSO research group and the VR ORPHEUS project. He also serves as Head of Research Education in Computer Science and Electronics at the university. His academic journey includes a M.Sc. in Global Software Engineering (via the GSEEM program) and a Ph.D. in Computer Science and Engineering from Mälardalen University (2014), followed by promotion to Docent (Associate Professor) in 2017. Education: M.Sc. in Global Software Engineering (GSEEM program, joint between Mälardalen, L'Aquila, and Amsterdam) Ph.D. in Computer Science and Engineering (Mälardalen University, 2014) Docent (Associate Professor) in Computer Science (Mälardalen University, 2017) His research focuses on model-driven engineering, robotics software engineering, and software architecture. He has pioneered work in blended modeling approaches, EAST-ADL extensions, and formal verification of complex systems, particularly in robotics and automotive domains. Recent projects emphasize industrial collaborations, such as optimizing ROS2 multi-robot systems and enhancing safety-critical software through model-based methodologies. Research Contributions: Developed frameworks for transforming surface languages into augmented EAST-ADL models Advanced blended modeling techniques across JetBrains MPS and Eclipse tools Examined consistency management in industrial model-driven development Edited special issues on model-driven engineering and low-code development His work bridges academic innovation and industrial practice, with notable projects like the Rubus Component Model for vehicular systems. He actively organizes workshops (e.g., RoSE, ASYDE) and contributes to standardization efforts like the Portable Test and Stimulus Standard. Labs/Teams: Leads the ASSO research group, focusing on advanced software engineering methodologies and robotics systems development.

• Service-Oriented Architectures
• Data Management
• Cloud Computing
• +5 more

Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.

Kim JelfsView profile

Professor

• Computational Materials Chemistry
• Physical Chemistry
• Materials Engineering
• +4 more

Prof. Kim Jelfs is a Professor in Computational Materials Chemistry at Imperial College London's Department of Chemistry, part of the Faculty of Natural Sciences. Her research focuses on computational design of functional molecular materials, particularly porous organic cages, polymer membranes, and materials for energy storage. She leads the Jelfs Group, collaborating with synthetic teams to bridge theory and experiment. Key affiliations include the Institute for Digital Molecular Design and Fabrication (DigiFAB), the Artificial Intelligence Network, and the Thomas Young Centre. Her work is funded by the Royal Society, EPSRC, ERC, and industry partners. Research interests span materials engineering, theoretical chemistry, and AI-driven discovery. Notable projects involve developing porous liquids, ion-selective membranes for batteries, and computational workflows for material screening. She co-directs DigiFAB and contributes to Imperial's Data Science Institute. Her group emphasizes interdisciplinary approaches to accelerate material innovation.

Alberto L. Sangiovanni-VincentelliView profile

Professor

• Cyber-Physical Systems
• Design Automation
• Embedded Systems
• +7 more

Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.