Dr. Song Shu is an Assistant Professor in the Department of Geography and Planning at Appalachian State University, specializing in remote sensing applications for cryospheric and hydrospheric studies. She holds a Ph.D. from the University of Cincinnati (2013–2019), an M.S. and B.S. from East China Normal University (2010–2013 and 2006–2010). Her research focuses on satellite altimetry, lake hydrology, and climate change impacts using advanced remote sensing techniques. She teaches courses such as GHY 3812: Geographic Information Systems, GHY 3310: Environmental Remote Sensing, and GHY 4810/5810: Digital Image Processing. Her work emphasizes improving satellite altimetry for Earth surface dynamics, retrieving hydrosphere/cryosphere parameters (e.g., snow depth, lake levels), and analyzing climate change effects on lakes. Key contributions include studies on Arctic snow accumulation, Tibetan Plateau vegetation dynamics, and urban heat island mitigation. Dr. Shu collaborates on interdisciplinary projects, including ICESat-2 ice shelf analysis and water quality modeling in inland lakes. Publications span high-impact journals like Remote Sensing of Environment (IF 9.09), IEEE Transactions on Geoscience and Remote Sensing (IF 5.86), and Nature Communications Earth & Environment. Her research integrates multi-source remote sensing data and geospatial methods to address global environmental challenges.
Oliver Eberle is a Researcher at the Machine Learning Group within the Technical University of Berlin (TU Berlin), affiliated with BIFOLD – Berlin Institute for the Foundations of Learning and Data. He holds a Joint M.Sc. in Computational Neuroscience (2017) and a Ph.D. in Machine Learning (2022) from TU Berlin and Humboldt-Universität zu Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing (NLP), and their applications in Digital Humanities and Cognitive Science. Notable projects include XAI methods for transformer architectures, historical corpus analysis, and collaborations with the Max Planck Institute for the History of Science on the Sphaera Corpus. Key contributions include developing frameworks like BiLRP for similarity models, MambaLRP for sequence models, and xMIL for medical imaging. His work bridges technical advancements in AI with interdisciplinary applications in humanities and science.
Dr. Holger Eichelberger is part of the Academic Staff in the Software Systems Engineering (SSE) department at the University of Hildesheim's Institute of Computer Science. He is affiliated with Faculty 4: Mathematics, Natural Sciences, Economics and Computer Science. His roles include membership in the Managing Committee of the Institute of Computer Science and the Committee for Student Scholarships. He has extensive experience in model-based software development, Industry 4.0 platforms, and performance engineering. Research Interests: Software Engineering for adaptive systems, Asset Administration Shells (AAS), IIoT platforms, MLOps, container orchestration, and open-source tools like EASy-Producer and SPASS-meter. His work focuses on bridging research and industrial needs, particularly in smart manufacturing and edge computing. Publications highlight contributions to IIoT platform analysis, AI integration in Industry 4.0, and performance benchmarking of communication protocols. He has organized conferences like ICPE and SSP and reviewed for top journals such as IEEE Transactions on Software Engineering. Key projects include the IIP-Ecosphere platform and contributions to standards like AAS. Collaborations involve institutions like the University of the West Indies and industry partners through funded projects like BMBF AI-Lab HAISEM. His research emphasizes reproducibility, interoperability, and scalable solutions for industrial challenges.
Kevin Iga is a Professor of Mathematics at Pepperdine University's Seaver College, part of the Natural Science Division. He has been teaching since 1998 and holds a PhD from Stanford University (1998) and dual BS degrees in Mathematics and Physics from MIT (1992). His research focuses on supersymmetry, particularly using Adinkra diagrams, differential topology, general relativity, and cryptography. He has contributed to geometric interpretations of supersymmetry algebras and combinatorial analysis of Adinkras. Iga serves as Public Information Officer for the MAA's Philosophy of Mathematics SIG and is active in professional organizations like the AMS, ASA, and MAA. His work spans over 25 years, with significant contributions to mathematical physics, including structural theory of Adinkras, cohomological applications, and cryptographic hardness problems. He teaches courses such as Algebraic Structures, Automata Theory, and Real Analysis. Iga is also involved in community activities, singing in the Pepperdine Concert Choir and participating in Malibu Presbyterian Church.
David Lopez Vilariño is a **Professor** at the **University of Santiago de Compostela**, affiliated with the **Department of Electronics and Computing** within the **Faculty of Physics**. He earned his PhD in 2001 with a thesis titled *"Active contours at the pixel level: design and implementation on cellular network architectures,"* advised by Dr. Diego Cabello Ferrer. His research focuses on **Computer Architecture**, **FPGA Acceleration**, **LiDAR Data Analysis**, and **Embedded Systems**, with notable contributions to LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He is part of the **ARQCOMP (Computer Architecture)** and **Artificial Vision** research groups. His work spans topics such as high-performance computing, parallel processing, and hardware optimization for vision-capable systems. Key projects include developing FPGA-based solutions for real-time video surveillance, retinal vessel analysis, and autonomous navigation systems. Publications emphasize **LiDAR data processing**, including algorithms for road detection, power line characterization, and 3D point cloud analysis. He also pioneered tools like the *Open Lidar Visualizer and Analyser* for 3D stereoscopic visualization. His expertise bridges hardware design and software development, particularly in leveraging FPGAs for embedded vision systems. No scientific awards or grants are explicitly listed, but his prolific publication record highlights sustained innovation in computer vision and geospatial technologies. His research team collaborates on projects involving manycore systems, GPU acceleration, and reconfigurable computing architectures.
Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.
Andrei-Constantin Braitor is a researcher specializing in control engineering and power systems, with a focus on DC microgrids, stability analysis, and power electronics. His work addresses challenges in voltage stability, overvoltage/overcurrent protection, and control design for DC microgrids in hybrid electric aircraft and meshed networks. He has collaborated with notable researchers such as Houria Siguerdidjane and Alessio Iovine on projects involving droop control, admittance matrix computation, and consensus-based control algorithms. Research Interests: DC Microgrid Stability and Control Distributed Control Systems Power Electronics Integration Aerospace Power Systems Nonlinear Dynamics in Power Networks His recent publications (2020-2025) explore advanced hierarchical control frameworks, fault-tolerant designs, and educational applications of control engineering through animated cartoons. He has contributed to both theoretical stability analysis and practical control implementations in meshed and parallel-operated converter systems. No scientific awards, grants, or lab affiliations were explicitly mentioned in the provided texts. His student advising record is currently empty.
Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.
Brian K. Smith is a Professor at the Lynch School of Education and Human Development at Boston College , holding the Honorable David S. Nelson Chair and serving as Associate Dean for Research . His career spans roles at Drexel University, MIT, the National Science Foundation, and Rhode Island School of Design. Education: Ph.D., Learning Sciences, Northwestern University B.A., Computer Science and Engineering, University of California at Los Angeles (1991) Research Interests focus on the design of computer-based learning environments, human-computer interaction, and computational thinking. He leads the Lynch School’s new M.A. program in Learning Engineering , blending learning science with practical design for curricula, museum exhibits, and corporate training. Recent publications highlight trends in AI integration for education , game-based learning platforms , and sociomateriality theory for learning sciences. His work emphasizes equity, particularly for underrepresented groups in STEM. Scientific Awards include the NSF CAREER Award , Apple Distinguished Educator , and TRW Chairman's Award for Innovation . Grants & Collaborations: Technical advisor to the Center for Inclusive Computing at Northeastern University Co-investigator in RISD’s “STEM to STEAM” initiative Labs & Teams: Co-director of Boston College’s M.A. in Learning Engineering program Vice chair of the World Usability Day Design Challenge
Dr. Kimberly Engber serves as Dean of the Dorothy and Bill Cohen Honors College and Associate Professor in the Department of English at Wichita State University. She joined the English faculty in 2007, leading the Emory Lindquist Honors Program as Director from 2012 before becoming the founding Dean of the Honors College in 2014. Her academic leadership focuses on strategic vision, faculty development, curriculum innovation, and fostering interdisciplinary research opportunities. Her research expertise spans 19th- and 20th-century American literature, women's literature, and literary anthropology. Notable works include studies of Margaret Mead and Virginia Woolf, transnational literature, and ethnographic traditions in women’s writing. She also explores interdisciplinary topics such as perception studies, cultural theory, and data visualization. In her administrative role, Dr. Engber oversees strategic planning, budget management, alumni relations, and student governance. She champions collaborative initiatives bridging academic programs and community engagement.
Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.
Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .
Stefan Lengauer is a Senior Researcher at the Institute of Visual Computing (IVC), Graz University of Technology. His work bridges cultural heritage analysis and health informatics through advanced visualization techniques. PhD in Computer Science (2022), Graz University of Technology MSc in Space Sciences (2018), TU Graz BSc in Computer Science (2014-2022) and Aviation (2015), FH JOANNEUM Research focuses on visual analytics , 3D object retrieval , and cross-modal search , with applications in: Medical domains (diabetes care, health information systems) Cultural heritage (pottery analysis, fragment matching, digital restoration) Pattern recognition (geometric motifs, surface textures) Recent publications highlight trends in adaptive visualization (2024-2025) and 3D cultural heritage analysis (2021-2023). Key projects include: HEREDITARY (2024-present): HORIZON Europe project on gut-brain interaction A+CHIS (2020-present): FWF research group on adaptive health information systems CrossSAVE-CH (2019-2022): Cross-modal search in cultural heritage Scientific recognition includes: Best Challenge Entry (2024) Honorable Mention (2020) PhD distinction (2022) Mentored 12+ students in topics ranging from medical chatbots to 3D pottery analysis . Reviewing activities span journals like Springer Nature and conferences including WSCG.
Elena Simperl is a Professor at King's College London, UK, with former affiliations at the University of Southampton and Karlsruhe Institute of Technology. Her work focuses on knowledge graphs, semantic web technologies, and AI-driven data management. She leads research in collaborative knowledge engineering, dataset search, and AI ethics, contributing to projects like the TheyBuyForYou platform for public procurement transparency. Her research interests span knowledge representation, crowdsourcing, and human-AI collaboration. Notable contributions include advancing methods for knowledge graph construction, improving data quality via crowdsourced and automated approaches, and exploring the societal impact of AI systems. She has co-edited major conferences such as ISWC and ESWC, and her work bridges technical innovation with practical applications in public policy and information systems. Key projects include developing frameworks for dataset usability, AI-ready data infrastructure, and systems for fact-checking visual content. Her collaborations span academia and industry, addressing challenges in data governance, misinformation detection, and ethical AI deployment.
Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.
