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Valentina CecchiView profile

Associate Professor

• Electric power systems modeling and analysis
• Transmission and distribution system optimization
• Grid-enhancing technologies
• +4 more

Valentina Cecchi is Associate Professor of Electrical and Computer Engineering and Associate Director of the same department at the University of North Carolina at Charlotte (UNC Charlotte), where she has been a faculty member since 2010. She previously served as Graduate Program Director and Associate Chair of the department from 2019 to 2024. Education background: Ph.D. in Electrical Engineering, Drexel University, Philadelphia, PA Research interests center on electric power systems modeling and analysis, with particular emphasis on optimization of transmission and distribution system planning and operation, grid-enhancing technologies, dynamic line rating of transmission lines, and the integration of renewable and distributed energy resources. Her work spans power system protection, resilience, data-driven analytics, and pedagogical innovation in power engineering education. A consistent thread in her recent publications (2023-2025) is the application of advanced analytics and machine learning to improve real-time monitoring, protection, and restoration of active distribution networks. A complementary focus is the development and evaluation of modern educational methodologies to prepare students for emerging challenges in power and energy systems. Scientific awards: William States Lee College of Engineering Graduate Teaching Excellence Award (2022) Advising & grants narrative: While specific PhD/Master’s students are not listed in the provided text, Dr. Cecchi’s service as Graduate Program Director and her active publication record with student co-authors suggest significant mentoring activity. She has led NSF-supported curriculum updates and educational research efforts, and her work on distribution system resilience, renewable integration, and protection coordination has been funded by multiple agencies and industry partners. Laboratory & teams: Dr. Cecchi is affiliated with the EPIC building (Energy Production and Infrastructure Center) at UNC Charlotte, specifically office 1224, and contributes to the university’s power and energy systems research infrastructure.

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

Dr. Sikha S Bagui is a Distinguished University Professor in the Department of Computer Science at the Hal Marcus College of Science and Engineering, University of West Florida . She served as the former Chair of Computer Science and was the Founding Director of the Center for Cybersecurity . Her research spans database design, Big Data analytics, machine learning, and cybersecurity . Research Focus: Machine Learning, Data Mining, Network Traffic Analysis, Graph Databases, and Resampling Techniques for Imbalanced Data Awards: Askew Fellow (2018–2021), multiple Excellence in Teaching and Distinguished Research Awards (2001–2024) Contributions: Authored books on databases/SQL (translated internationally), developed the UWF-ZeekData datasets for cybersecurity research, and served as Associate Editor for multiple journals Tools & Frameworks: Active in Hadoop, Spark, Memgraph, and MITRE ATT&CK-based threat modeling Publication Trends: Recent work focuses on MITRE ATT&CK datasets , graph-based cybersecurity , resampling rare attacks , and educational impacts in computing . Her research bridges theoretical and applied domains, including clinical decision support systems and K-12 computer science education. Scientific Awards: Askew Fellow (Reubin O’D. Askew Institute for Multidisciplinary Studies, 2018–2021) Excellence in Teaching and Advising Award (UWF, 2012) Distinguished Research and Creative Activities Award (UWF, 2007, 2012) Excellence in Undergraduate Teaching and Advising Award (UWF, 2001–2006) Leadership & Service: Directed the Center for Cybersecurity, contributed to journals as Associate Editor, and engaged in initiatives like NCWIT Aspirations in Computing and the Association for Women in Computing.

Nicola CarboniView profile

Assistant Professor

• Ontological Modeling
• Cultural Heritage Data Management
• Digital Humanities
• +3 more

Nicola Carboni is an Assistant Professor at the School of Information Sciences at the University of Illinois. His research bridges Ontological Modeling , Cultural Heritage Data Management , and Digital Humanities , focusing on formalizing knowledge structures for heritage objects and visual culture. Key Affiliations : School of Information Sciences (University of Illinois), Visual Contagions Project, CIDOC CRM research. Research Themes : Develops ontological frameworks for spatiotemporal data, cultural heritage documentation, and semantic modeling of art exhibitions. His Ontological Patterns methodology has advanced semantic web applications in cultural heritage, particularly through projects like the Buon Fresco Digital Atlas and Ethiopian Rock Church Documentation . Recent work explores Twentieth-Century Illustrated Periodicals and Art Reproduction Circulation using digital tools. Publications span Journal of Open Humanities Data , Artl@s Bulletin , and conferences like MTSR 2024 , emphasizing Knowledge Graphs , Visual Analysis , and Digital Preservation . Collaborates with researchers including B. Joyeux-Prunel, G. Bruseker, and L. de Luca.

• Human-Machine Interaction
• Digital Twin Technology
• Industrial Augmented Reality
• +6 more

Dorothea Pantförder, Dr.-Ing., is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich, working under Prof. Vogel-Heuser. Her office is located at Boltzmannstr. 15, 85748 Garching b. Munich, where she maintains regular office hours on Mondays from 8:30 a.m. to 9:15 a.m. Her research focuses on human-machine interaction with particular emphasis on digital twin technology, industrial augmented reality applications, and advanced process data visualization techniques. She has pioneered work in 3D visualization for process control systems and has extensively explored how mixed reality technologies can enhance industrial maintenance procedures and operator training in manufacturing environments. Analysis of her publication history reveals a consistent research trajectory centered around making complex industrial processes more accessible through innovative visualization techniques. Her work bridges the gap between theoretical human-computer interaction principles and practical industrial applications, particularly in the context of Industry 4.0 initiatives and cyber-physical production systems. Throughout her career, Pantförder has collaborated extensively with Prof. Vogel-Heuser and other researchers across multiple institutions, contributing to numerous research projects focused on automation technologies, information systems, and human-centered design in industrial contexts. Her work has been supported by various research grants and has contributed to several demonstration projects showcasing Industry 4.0 applications. She has been instrumental in developing visualization techniques that help operators better understand complex production processes, with applications ranging from maintenance support to team knowledge management systems. Her research has evolved from foundational work on 3D process visualization to more recent explorations of digital twins integrated with mixed reality technologies for industrial applications.

Christoph EggerView profile

Assistant Professor

• Cryptography
• Computational Complexity
• Statistical Privacy
• +7 more

Christoph Egger is an Assistant Professor at Chalmers University of Technology in the Department of Computer Science and Engineering, where he works with the Security & Privacy Lab and the Crypto Team. Prior to this position, he was a Marie-Curie Fellow at Institut de Recherche en Informatique Fondamentale (IRIF) from fall 2022 to 2024, researching connections between cryptography and complexity theory. His educational background includes: PhD: "On Abstraction and Modularization in Protocol Analysis" Master's: "An implementation of global caching for the alternation-free coalgebraic μ-calculus" Bachelor's: "Analysing and attacking the I2P Network Database" Dr. Egger's research focuses on cryptography and its connections to computational complexity, statistical privacy, and formal methods. His work spans multiple areas including cryptographic foundations (random oracles, key agreement protocols), privacy-enhancing technologies (ring signatures, information flow techniques), and practical applications in genomic data security. He develops both theoretical frameworks and practical tools like CryptoZoo for cryptographic proof visualization, bridging the gap between theoretical cryptography and real-world security challenges. His recent publications demonstrate a strong focus on cryptographic foundations and privacy technologies, with significant contributions to ring signatures, key agreement protocols, and genomic data security. His work bridges theoretical cryptography with practical applications in blockchain, anonymous communications, and healthcare data management, showing consistent productivity across multiple high-impact venues in security and privacy. Dr. Egger has served on program committees for prestigious conferences including IEEE Computer Security Foundations Symposium (CSF), Proceedings on Privacy Enhancing Technologies (PETS), and Conference on Applied Cryptography and Network Security (ACNS). He currently advises PhD students Lucia Lavagnino at Chalmers and Kirthivaasan Puniamurthy at Aalto University. Previously, he advised Master's students including Julian Brost and Kirthivaasan Puniamurthy. His Marie-Curie Fellowship was cofunded by EU H2020 Marie Sklodowska-Curie Action and FSMP Comunity Service. Dr. Egger is an active member of the Security & Privacy Lab and Crypto Team at Chalmers. He is also a founding member of the FAUST CTF team and has been a Debian Developer for over a decade, contributing to various Free Software projects including the Linux kernel and Git version control system, demonstrating his commitment to both academic research and practical software security.

• Image Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Dilip K. Prasad is a Professor at the Department of Informatics, UiT The Arctic University of Norway. His work bridges Artificial Intelligence and Medical Imaging , with a focus on Interpretable AI , Scalable AI , and Life Science Applications . He has contributed to Maritime Technology and Biomedical Engineering . Ph.D. and B.Tech from Nanyang Technological University and IIT Dhanbad Senior Research Fellow at NTU (2015-2019), Research Fellow at NUS (2012-2015) Industry experience at IBM, Infosys, Mediatek, Philips His research explores Image Processing , Machine Learning , and AI Applications in Biomedicine . Recent work includes Dense Video Captioning , 3D Mitochondrial Modeling , and Physics-Guided Loss Functions . Articles span Neurocomputing , Optics Express , and top AI conferences like CVPR and NeurIPS . Prasad has received the Rolls-Royce Inventor Award (2016) and Best Paper Award (IJCIE 2017) . He has reviewed for 50+ journals and 30+ conferences, serving as Area Chair for NeurIPS 2022-23 and Organizer Chair for ICCV Workshop 2023 .

• Artificial Intelligence
• Medical Imaging
• Machine Learning
• +3 more

Anna Anikina is a PhD Fellow at the Department of Computer Science (DIKU), University of Copenhagen, affiliated with the Image Analysis, Computational Modelling and Geometry section. Her research focuses on applying artificial intelligence and machine learning to analyze eye-tracking data for predicting radiological diagnostic errors. Current position: PhD Fellow in Image Analysis and Computational Modelling University: University of Copenhagen Department: Image Analysis, Computational Modelling and Geometry Anna’s work spans Medical Imaging , Eye Tracking , and Transformers for diagnostic error prediction, as well as Latent-Space Modelling in reinforcement learning systems. Her recent publications highlight applications of Graph Neural Networks and Multi-Agent Systems to biomedical image analysis and visual data processing. Her research outputs include four peer-reviewed publications (2023–2025) addressing radiological decision errors through gaze analysis and advanced machine learning architectures. Collaborations with researchers like B. Ibragimov, C. Mello-Thoms, and others demonstrate her focus on AI-driven healthcare solutions.

Tingjun LeiView profile

Assistant Professor

• Bio-inspired Artificial Intelligence
• Robotics and Autonomous Systems
• Optimization and Evolutionary Computation
• +3 more

Dr. Tingjun Lei is an Assistant Professor at the University of North Dakota's School of Electrical Engineering and Computer Science (SEECS). With a Ph.D. in Electrical and Computer Engineering from Mississippi State University (2023), his research focuses on bio-inspired artificial intelligence , robotics and autonomous systems , and human-autonomy teaming (HAT) . Research Interests : Bio-inspired AI for robot cognition Multi-robot coordination and control Graph-based path planning algorithms Human-autonomy collaborative systems Intelligent transportation frameworks Recent Article Trends span robotics , bio-inspired computation , and machine learning , with specific focus areas in swarm intelligence , cognitive mapping , multi-agent safety , and adaptive navigation systems . Scientific Awards : 2025 Honorable Mentions (IJCNN 2025) 2025 Early Career Scholars Award (UND) 2024 Best Paper Awards (Biomimetic Intelligence & Robotics, ASEE ECE Division) 2022-2023 ECE Best Graduate Researcher (Mississippi State) Students : Ashwin Mukunda Devanga (Ph.D.), Samuel Steen (Ph.D.), and Justin London (Ph.D.)

James H. Cross IIView profile

Professor

• Software Engineering
• Computer Science
• Program Visualization
• +3 more

James H. Cross II is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering, with prior affiliations at the University of Nevada Las Vegas' Business School and Drexel University's College of Medicine. His research focuses on software engineering education, program visualization, reverse engineering, and educational technology. Key Roles : Software Engineering Educator, Java Pedagogy Innovator, Program Visualization Researcher His work emphasizes enhancing code comprehension through dynamic data structure visualizations (e.g., jGRASP IDE), reverse engineering methodologies, and interdisciplinary applications in software maintenance and curriculum development. Recent trends in his publications (2019–2024) include FAIR data principles, ontological frameworks, and token-based incentives for scholarly contributions. Notable collaborations include T. Dean Hendrix, Larry A. Barowski, and David A. Umphress. Affiliated with institutions like Auburn University, University of Nevada Las Vegas, and Drexel University's College of Medicine, his career spans software engineering research, educational tool development, and academic leadership.

Li HanView profile

Professor

• Data Science
• Computational Biology
• Protein Dynamics
• +2 more

Li Han is Professor of Computer Science at Clark University's Becker School of Design and Technology, where she also directs the Data Science program. She holds a Ph.D. from Texas A&M University and M.S./B.S. degrees from Xi'an Jiaotong University. Her research spans computational protein studies, data science, and robotics, with current focus on protein dynamics and allosteric mechanisms. Her publications demonstrate consistent focus on computational approaches to biological systems, particularly protein dynamics, folding mechanisms, and conformation analysis. Recent work (2022) examines allosteric pathways in ubiquitin ligases using advanced simulation techniques. Methodological contributions include dimensionality reduction for protein conformation spaces and novel algorithms for molecular simulation. She teaches diverse courses including Introduction to Data Science, Algorithms, and Robotics, and advises student computing organizations. Her research has received funding from NSF and NIH.

Peter DoddsView profile

Professor

• Complex systems
• Networks
• Contagion
• +7 more

Peter Dodds is a Professor in the Department of Mathematics and Statistics at the University of Vermont, part of the College of Engineering and Mathematical Sciences. His research focuses on complex systems, networks, and sociotechnical phenomena, with a particular emphasis on analyzing large-scale data from social media, language, and human behavior. Education: Ph.D. in Mathematics from the Massachusetts Institute of Technology (MIT). Research Interests: Complex systems, contagion dynamics, storytelling, happiness studies, and applications of computational methods to social and biological systems. Key Projects: Co-director of the Computational Story Lab, analyzing narratives and collective behavior via Twitter and other platforms. His work bridges disciplines, combining mathematical modeling, data science, and social science to understand phenomena such as viral spread, cultural trends, and public health. Notable contributions include studying emotional arcs in stories, the universality of positivity in language, and the dynamics of social media discourse around issues like climate change and political movements. Recent studies include analyzing sleep patterns in college students using wearable devices and examining the impact of social media on collective attention during crises like the pandemic. His methodologies, such as allotaxonometry and word shift graphs, provide novel tools for comparing complex datasets across domains. Dr. Dodds collaborates extensively with colleagues in computer science, sociology, and environmental science, fostering interdisciplinary approaches to understanding human systems. His lab’s work is often applied to real-world challenges, from improving well-being through wearable tech interventions to mitigating misinformation in online spaces.

Patrick J.F. GroenenView profile

Professor

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +5 more

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

Erik MannensView profile

Professor

• Semantic Intelligence
• Big Data Analytics
• Semantic Web
• +4 more

Erik Mannens is a Professor at Ghent University and Research Valorisation Director at imec, specializing in Semantic Technologies and Artificial Intelligence since 2005. He leads the Data Science team at IDLab, managing over 50 researchers focused on advancing semantic data science and its applications. Education: PhD in Computer Science Engineering (2011), Master’s in Computer Science (1995), and Master’s in Electro-Mechanical Engineering (1992) The team explores the fusion of semantics and AI, aiming to: (1) analyze the Web’s societal impact, (2) study Web-enabled lifestyle changes, and (3) develop future Web innovations. Their work emphasizes Open Source , Open Access , and Open Knowledge principles. His research spans Semantic Intelligence , Big Data Analytics , and Linked Data applications, with notable publications on RDF mappings, IoT platforms, and distributed data querying. Key projects include MapVOWL, RMLEditor, and the MASSIF platform. Current affiliations include Ghent University’s IDLab , where he contributes to cutting-edge research in data science and semantic technologies.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +6 more

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

• Data Visualization
• Machine Learning
• Neural Networks
• +5 more

Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.