Find Academic Supervisors

W. Patrick McCrayView profile

Professor

• History of Science
• History of Technology
• Art and Technology Intersections
• +6 more

W. Patrick McCray is a Professor in the History Department at the University of California, Santa Barbara, specializing in the histories of technology and science since 1945. He holds a Ph.D. from the University of Arizona (1996). His research bridges disciplines, examining intersections of art, technology, and science, with a focus on Cold War collaborations, computing history, and futurism. Currently, he is completing a book titled *README* (MIT Press, 2025), analyzing how computers became culturally pervasive through their representation in books. He is also a Kluge Chair in Technology and Society (2025–2026). McCray’s work spans books like *Making Art Work* (2020), exploring artist-engineer collaborations, and *The Visioneers* (2013), which won the Watson Davis Prize. His honors include fellowships from the AAAS and APS, and roles at the Smithsonian’s Lemelson Center and the Canadian Institute for Advanced Research. His teaching includes courses on technology history, computing, and the atomic age. McCray’s research also addresses environmental history, space exploration, and nanotechnology governance. Key projects include exploring habitability’s intersections with space and environmental science, and studying mountain cultures in the American West. His writing extends to public venues like the *Los Angeles Review of Books*, where he critiques tech culture and innovation narratives. He is a renowned voice on topics like Silicon Valley’s societal impacts and the myth of the 'independent inventor.'

Rong ChenView profile

Professor

• Nonlinear Time Series Analysis
• Multivariate Time Series
• Monte Carlo Methods
• +5 more

Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University, within the School of Arts and Sciences. With a Ph.D. from Carnegie Mellon University, Professor Chen has established himself as a leading researcher in statistical time series analysis, Monte Carlo methods, and their applications across various fields. Professor Chen's research focuses on: Nonlinear and Multivariate Time Series Analysis Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applications in Science, Engineering and Business His research trajectory has evolved significantly over the years, beginning with foundational work on nonlinear time series and moving toward more complex high-dimensional tensor time series analysis. Recent publications show a strong emphasis on matrix and tensor factor models for high-dimensional time series, reflecting the growing importance of analyzing complex structured data in modern applications. His work bridges theoretical statistical innovation with practical problem-solving across finance, engineering, and computational biology. Professor Chen has been recognized for his contributions to the field with prestigious fellowships: ASA Fellow (American Statistical Association) IMS Fellow (Institute of Mathematical Statistics) As Chair of the Department of Statistics, Professor Chen oversees academic programs including the Master in Financial Statistics and Risk Management (FSRM) and the Master in Data Science (Statistics Track) programs. His leadership extends to guiding research directions in the department and fostering collaborations across disciplines. Professor Chen has secured numerous research grants supporting work in time series analysis, statistical computing, and applications in finance, engineering, and bioinformatics. Professor Chen's research group maintains active collaborations with researchers in finance, engineering, and computational biology, applying statistical innovations to real-world problems including financial time series analysis, protein folding studies, HIV infection dynamics modeling, wind power forecasting, and nuclear material detection systems.

Mai ElshehalyView profile

Lecturer

• Information Visualisation
• Human-Computer Interaction
• Visual Analytics
• +7 more

Mai Elshehaly is a Lecturer in Computer Science at City, University of London, where she is affiliated with the giCentre in the Department of Computer Science, School of Science and Technology. She focuses on designing, developing, and evaluating visualisation solutions that support data-driven decision-making, particularly for non-technical users in healthcare and public sectors. Her work involves close collaboration with healthcare practitioners, local authorities, and schools to co-design tools grounded in real-world needs. Her research interests center on information visualisation and human-computer interaction , with a strong emphasis on user-centered and co-design methodologies . She investigates how lived experiences of stakeholders can shape visual analytics systems, especially in healthcare quality improvement, population health management, and digital transformation initiatives. Her approach bridges technical innovation with social impact, promoting equitable access to data insights. Her recent publications highlight trends in narrative visualization, integration of lived experience with electronic records (e.g., CLEVER framework), and adaptable dashboards for healthcare (QualDash). These works are published in top venues such as IEEE VIS, IEEE TVCG, and Computer Graphics Forum, reflecting a consistent focus on usability, evaluation, and real-world deployment of visual analytics tools. Department-level achievement award during PhD at Virginia Tech University-level achievement award during PhD at Virginia Tech Mai has secured research funding from the Bradford Institute for Health Research (BIHR), NIHR, and internal university sources including EPSRC doctoral training funds. She has led and contributed to interdisciplinary projects such as the Digital Makers Programme and Connected Bradford, which involve partnerships across academia, healthcare, and local government. While no formal PhD students are listed, she mentors through collaborative research and advisory roles. She is actively involved in applied research through leadership positions: Co-Director of the Digital Makers Programme, Deputy Director of the Workforce Observatory for West Yorkshire, member of the advisory board at the Wolfson Centre for Applied Health Research, and cross-theme visualisation expert at the Yorkshire and Humber Patient Safety Research Centre. These roles underscore her commitment to translating research into practice within health and education systems.

• Machine Learning
• Time Series Analysis
• Space Weather Prediction
• +4 more

Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.

Igor WojnickiView profile

Professor

• Smart City Systems
• Graph-Based Computational Methods
• Energy-Efficient Lighting Control
• +4 more

Igor Wojnicki is a Professor at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, where he serves as Vice-Dean of the Faculty of Cooperation and Education. His primary affiliation is with the Department of Applied Informatics, where he maintains an active research laboratory focused on knowledge engineering and smart systems. His research spans multiple domains with evolving focus: Early career: Deductive databases and rule-based inference engines (PhD thesis on "A Rule-based Inference Engine Extending Knowledge Processing Capabilities of Relational Database Management Systems") Mid-career: Graph-based knowledge representation and Tabular Trees (XTT predecessor) Current focus: Smart city applications, particularly energy-efficient lighting control systems and graph-based urban data integration His recent publications demonstrate a clear trajectory toward applied urban computing, with over 15 significant papers in the last five years addressing smart city infrastructure optimization. Key themes include dynamic street lighting control, graph-based computational methods for urban environments, and energy conservation in public infrastructure. Wojnicki actively contributes to academic-practical collaboration through initiatives like the Green AGH Campus Project and IBM academic partnerships. His technical leadership includes development of the ReDaReS system for relational database knowledge processing and the Jelly View technology for advanced database queries. His laboratory maintains strong industry connections, particularly with IBM through student internship programs and technology transfer initiatives. The team produces both theoretical frameworks and practical implementations, with notable outputs including the Osiris GUI system and Magellan GPS software for Poland.

• Computer Science
• Artificial Intelligence
• Cybernetics
• +3 more

Dilip Sarkar serves as an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, University of Miami. His academic profile demonstrates a strong research focus across multiple domains of computer science with particular emphasis on theoretical foundations and practical applications. Dr. Sarkar's research interests include: Artificial Intelligence and Cybernetics Parallel Algorithms and Programming Combinatorics Image Processing and Compression Internet of Things Security Machine Learning Applications in Healthcare Quantum Computing Approaches His publication record from 2018-2024 reveals a researcher engaged with cutting-edge computational challenges. Recent work spans quantum tensor networks for time series analysis, security frameworks for single sign-on systems, efficient computation in belief theoretic models, and machine learning applications for traumatic brain injury recovery prediction. His research demonstrates consistent integration of theoretical computer science with practical problem-solving across diverse application domains. As a member of CCS (Center for Computational Science) at the University of Miami, Dr. Sarkar participates in interdisciplinary research initiatives that bridge computational methods with scientific discovery across multiple fields. His technical expertise spans both theoretical frameworks and practical implementations, with notable contributions to image compression techniques, particularly for medical applications, and security protocols for modern web and IoT environments.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.

• Fluid Power Engineering
• Hydraulic Systems
• Tribology
• +4 more

Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.

Aleksandra SarcevicView profile

Professor

• Computer Science
• Human-Computer Interaction
• Healthcare Informatics
• +7 more

Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.

Xiao ChenView profile

Researcher

• Composite Structures
• Wind Energy Systems
• Structural Integrity
• +7 more

Xiao Chen is a Researcher at the Technical University of Denmark (DTU), specializing in advanced testing and digitalization of composite and offshore steel structures for wind energy systems. With a PhD from Nagoya University (2011), his work focuses on structural integrity, fatigue analysis, and Digital Twins for wind turbine blades. PhD in Engineering, Nagoya University (2011) Senior Researcher (2019–present) and Researcher (2017–2018) at DTU Associate Professor (2016–2017) and Assistant Professor (2013–2015) at Chinese Academy of Sciences His research explores nonlinear buckling, fracture mechanics, and Industry 4.0 technologies for structural health monitoring. Recent publications highlight AI-driven damage detection, thermographic analysis, and finite element modeling of composites. He leads projects like QualiDrone and AQUADA-GO, funded by EUDP and VILLUM FONDEN. Key article trends include composite fatigue , digital twins , drone-based inspection , and machine learning for structural monitoring. He received the 2022 Best Presentation Award at an international conference. Projects: Villum Experiment Project DiscoverBlaDE AQUADA-GO QualiDrone DARWIN RELIABLADE RELIfe

Carlos Platero DueñasView profile

Professor

• Biomedical Engineering
• Neuroscience
• Medical Image Processing
• +3 more

Carlos Platero Dueñas is a Full Professor at the Department of Electrical, Electronic and Automatic Engineering and Applied Physics at the Universidad Politécnica de Madrid (UPM), where he has served for 31 years. He leads the research group Tecnologías para Ciencias de la Salud since 2015 and contributes to interdisciplinary research at the intersection of biomedical engineering, neuroscience, and artificial intelligence. Department: Electrical, Electronic and Automatic Engineering and Applied Physics Research Group: Tecnologías para Ciencias de la Salud (Health Science Technologies) Teaching: 34 years of academic experience, including 128 final projects supervised His research focuses on applying computational methods to neurodegenerative diseases , particularly Alzheimer's and Parkinson's, through neuroimaging analysis, predictive modeling, and hippocampal segmentation. Recent work includes AT(N) profiles for dementia prediction and machine learning techniques for clinical data modeling. The 15 most recent publications reveal a strong emphasis on Alzheimer's disease progression , hippocampal segmentation , and predictive analytics using neuroimaging and clinical markers. Key methodologies involve graph cuts algorithms, longitudinal modeling, and label fusion techniques applied to MRI and CT scans. Teaching contributions include: 128 final projects supervised (undergraduate and master's) 2 doctoral theses directed Active participation in university governance through the School Council and Researcher Staff Committee

• Computational Intelligence
• Agent-Based Simulation Systems
• Neuroscience
• +4 more

Darius Plikynas is a Senior Researcher at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University . His research integrates computational intelligence methods with agent-based simulation to model cognitive and social processes. Position: Senior Researcher, Chief Researcher in the Project Address: Akademijos St. 4, room 224, Vilnius Contact: +370 5 210 9333, +370 620 95101 Email: darius.plikynas@mif.vu.lt Personal page: http://www.dariusplikynas.eu Dr. Plikynas' research spans interdisciplinary domains including neuroscience, physics methods, complexity theory, and distributed cognitive systems. He led the 2017–2019 project "Development of a metric, conceptual and simulation model of the social impact of cultural processes" under the LMT Research Group Funding Program. His recent publications (2016–2025) reflect trends in combining machine learning with social science questions (fake news analysis, propaganda detection) and agent-based modeling of cultural/social capital dynamics. Key collaborations include Leonidas Sakalauskas, Rimvydas Laužikas, and Arunas Miliauskas. Scientific supervision includes doctoral students: Andrius Budrionis (University of Tromsø, Norway) Ieva Rizgelienė (PhD topic: "Propaganda detection and classification in social media using hybrid deep learning") He also serves as an expert at Vilnius University and has contributed to projects involving: 2D financial market visualization Neural oscillation-based cognitive modeling Indoor navigation for blind individuals Cultural participation impact on social capital

• Spatial Databases
• Geoinformatics
• Internet of Things (IoT)
• +5 more

Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.

Blaž ZupanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +4 more

Blaž Zupan is a Full Professor at the University of Ljubljana, also affiliated with Baylor College of Medicine in Houston, teaching artificial intelligence and machine learning. He leads a fifteen-member bioinformatics laboratory and has authored over one hundred publications with more than ten thousand citations, reflecting significant impact in computational sciences. His research centers on explainable AI, machine learning, and data visualization, with deep applications in bioinformatics. He pioneered the Orange data mining suite—a visual programming tool for accessible analytics—and bioinformatics platforms like dictyExpress for gene expression analysis and GenePath for genetic network discovery, emphasizing user-friendly interfaces and real-world usability. Analysis of his recent publications reveals a trajectory from foundational AI (e.g., concept hierarchies in 1999) to contemporary bioinformatics, with current work focusing on single-cell genomics, batch-effect correction, and democratizing AI through visual programming. His tools bridge computational methods and biological discovery, particularly in systems biology and genomics. Zupan’s accolades include the Zois Award (2010), two University of Ljubljana Golden Plaques (2011, 2019), a Fulbright Scholarship (2013), and six student-voted Best Teacher Awards (2008–2017). He was named among the Top 100 Most Influential Innovators of Central and Eastern Europe (2016) by Res Publica, Financial Times, and Google. He secures substantial research funding, currently leading projects like KATARINA (computer science education for all), DALI4US (data literacy for primary schools), DANIO-ReCODE (genomics of regeneration), and L2-60154 (explainable AI for gene expression). Past projects span AI systems (2009–2020), drug discovery, and systems biology, demonstrating sustained interdisciplinary impact. As head of the Bioinformatics Laboratory, he fosters collaboration between computer science and biology, developing open-source tools that empower researchers globally. His consulting for industry and public organizations extends his influence into data-driven decision support and AI training.

Andrés BruhnView profile

Professor

• Computer Vision
• Optical Flow
• Scene Flow
• +5 more

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.