Prof. Dr. Armando Walter Colombo is a Professor at the Department of Technology, Electrical Engineering and Informatics at the University of Applied Sciences Emden/Leer. He leads the Institute I2AR as Scientific Director and coordinates the DAAD/DAHZ Binational Master in Industrial Informatics with the Universidad Tecnológica Nacional-FRRe in Argentina. His research focuses on Cyber-Physical Systems (CPS), Industrial Digitalization, Industry 4.0, and Smart Manufacturing. Key areas include asset administration shells, IoT integration, and sustainable industrial automation. He holds IEEE Fellow status and is a Distinguished Lecturer for the IEEE Systems Council. He has pioneered educational frameworks like T-CHAT and contributed to standards alignment (RAMI4.0, IEEE Industrial Agents). Recent publications emphasize Industry 4.0 compliance, digital twins, and AI in logistics. Responsibilities: DAAD Master Coordinator, Institute I2AR Director, and International Relations Officer. Awards: IEEE Fellow, Distinguished Lecturer (IEEE Systems Council). Grants & Partnerships: DAAD-funded binational programs, EU-funded PERFoRM projects. His work bridges academia and industry through platforms like the ICPS-based Digital Factory Lab, addressing SME digitalization and sustainable automation.
Weihang Wang is a Volunteer Assistant Professor in the Department of Computer Science and Engineering at the University of Southern California (USC), part of the School of Engineering and Applied Sciences. His research focuses on WebAssembly security, program analysis, and static/dynamic analysis frameworks. He holds a PhD and has published extensively on topics like WebAssembly obfuscation, decompilation techniques, and flaky test mitigation. Research interests include cybersecurity, software engineering, and the application of AI in program analysis. His work addresses challenges in cross-compilation for WebAssembly, decompilation accuracy, and automated detection of security vulnerabilities in web applications. Notable projects include WaSCR (side channel repairer), WBSan (bug detection), and Wefix (flaky test automation). He has received NSF travel grants for IEEE Security Development (SecDev) conferences in 2022 and 2023. His articles span 2010–2025, showing sustained contributions to web security, compiler optimization, and program transformation. His work intersects with practical applications like ad-blocking systems (Adhere) and bird flu outbreak prediction using migration data (2010–2013). Grants and awards include NSF funding for travel and research, reflecting his active role in academic conferences. He is affiliated with USC's computer science department and maintains an active Google Scholar profile with over 30 publications listed.
Ashirbani Saha is an Assistant Professor in the Department of Oncology at McMaster University, holding the inaugural BRIGHT Run Breast Cancer Learning Health System Chair. She is affiliated with the Michael G. DeGroote School of Medicine and the Escarpment Cancer Research Institute. Her research focuses on applying artificial intelligence (AI) and advanced data analytics to improve breast cancer diagnostics and patient care. She also holds associate memberships in the School of Biomedical Engineering and the Department of Computing and Software at McMaster University. Dr. Saha’s education includes a B.E. in Electronics from IIEST Shibpur (India), an M.A.Sc. and Ph.D. in Electrical and Computer Engineering from the University of Windsor, followed by postdoctoral training at Duke University and St. Michael’s Hospital. Her work spans medical imaging analysis, radiogenomics, and healthcare data science, with an emphasis on breast cancer and neurosurgery applications. Her research trends highlight AI-driven solutions for medical imaging challenges, such as tumor segmentation, radiomics analysis, and cross-dataset generalization. She has also explored work-life balance issues in healthcare professionals and ethical considerations in AI-driven healthcare research. The BRIGHT Run Chair supports her interdisciplinary initiatives to integrate AI and clinical data into a learning health system for breast cancer. Awards: BRIGHT Run Breast Cancer Learning Health System Chair (2021) Grants: $3.2M New Frontiers in Research Fund (2021) and community-endowed BRIGHT Run funding. Labs/Teams: Escarpment Cancer Research Institute, CentRE for dAta science and digiTal hEalth (CREATE).
Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University, serving as the founding director of the MS in Health Informatics Program and director of the Computational Science & Engineering PhD Program. He leads the Biomedical Data Science (BDS) Lab, focusing on healthcare innovation through AI, cybersecurity, and data science. His expertise spans machine learning, medical image analysis, and healthcare interoperability. Education: PhD in Computational Science & Engineering (Michigan Tech), MPA in Public Administration (Northern Michigan University), BS in Finance and Economics (Michigan Tech). Research interests include human health-focused AI/ML models, cybersecurity in healthcare, medical image segmentation, and public health surveillance. His work emphasizes collaboration with medical institutions like Henry Ford Hospital to develop clinical decision support systems and improve disease surveillance. Recent projects include AI-driven fracture risk prediction from knee radiographs and enhancing mental health intervention efficacy through multi-source data integration. Advising includes four doctoral students in areas like medical image analysis, blockchain for patient data security, and cost-effective mental health modeling. His software projects include FHIR-enabled health information exchange systems and Tick-Talk, a crowdsourced tick disease monitoring platform. Labs/Teams: The BDS Lab integrates expertise in medicine, AI, and cybersecurity to tackle healthcare challenges, emphasizing real-world impact through industry and academic partnerships.
Dr. Amirreza Khodadadian is a Lecturer in Mathematics at the School of Computer Science and Mathematics, Keele University, since August 2023. He holds a Ph.D. from the University of Vienna (2017), followed by postdoctoral positions at the Technical University of Vienna and Leibniz University Hannover. His research focuses on uncertainty quantification, numerical methods for stochastic PDEs, finite element methods, computational mechanics, and machine learning applications in nanoelectronics and biological systems. Key research interests include Bayesian inversion, multiscale modeling, reduced-order methods, and the design of nanoscale sensors. He has collaborated with institutions like the University of Oxford and secured an Austrian Science Fund (FWF) grant (476k€) for nanozyme sensor research. Dr. Khodadadian mentors postdoctoral researchers, including Dr. Samaneh Mirsian, and actively publishes in top-tier journals such as Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . His work bridges applied mathematics with engineering challenges, emphasizing efficient numerical algorithms for real-world problems like battery degradation, groundwater contamination, and biomedical sensor optimization. Recent projects involve machine learning integration for enhanced predictive modeling. Education: Ph.D. in Mathematics, University of Vienna, Austria (2017) Postdoctoral Fellowships: TU Vienna (2018), Leibniz University Hannover (2018–2022) Grants/Awards: Austrian Science Fund (FWF) Grant: Single Atom Catalysts as Nanozymes in FET Sensors (2023) Advising: Postdoctoral Mentor: Dr. Samaneh Mirsian (Keele University) Dr. Khodadadian’s publications span computational mechanics, stochastic modeling, and interdisciplinary applications, reflecting his expertise in translating mathematical theory into practical engineering solutions.
Prof. Grzegorz J. Nalepa holds the position of Full Professor at the Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Poland. He leads the Jagiellonian Human-Centered Artificial Intelligence Lab and is involved in international projects such as the CHISTERA XPM initiative on explainable AI in predictive maintenance. His academic journey includes a PhD (2004), habilitation (2012), and a philosophy MA (2012). Research focuses on AI, affective computing, context-aware systems, and explainable AI (XAI). He has authored over 200 papers, two monographs, and edited volumes. Key projects include the BIRAFFE2 dataset for emotion-based personalization and the GEIST team's semantic wiki tools. Holds leadership roles in conferences (e.g., ECAI 2023) and serves on editorial boards of journals like Sensors and Intelligent Sensors Section . Awards include the 2018 Outstanding Monograph Prize and multiple AGH-UST Rector's Prizes for scientific achievements. Advises PhD and master students, supervising 36 MSc theses. Active in professional organizations like the Polish Alliance for AI and the IEEE Computational Collective Intelligence TC. His contributions span industrial collaborations, software tools (e.g., InXAI, LUX, KnAC), and international research networks.
Professor Bradley Evans is a distinguished Earth observation and remote sensing specialist at the University of New England, where he holds a position in the Faculty of Science, Agriculture, Business and Law within the School of Environmental and Rural Science. His expertise spans environmental science, biodiversity conservation, and the application of hyperspectral imaging spectroscopy to solve real-world environmental challenges. Previously, he has held significant positions including Director of Australia's Terrestrial Ecosystem Research Network and Director of Sydney Informatics Hub at The University of Sydney. PhD in Environmental Science, Murdoch University, Western Australia, 2013 Bachelor of Science with Honours in Environmental Science, Murdoch University, Western Australia, 2009 Bachelor of Science in Energy Studies, Murdoch University, Western Australia, 2009 Advanced Diploma in Marketing Management, TAFE NSW, Bradfield College, 1999 CASA RPAS sub 25kg (Multirotor Drone) certification Professor Evans's research focuses on applying advanced remote sensing techniques to environmental monitoring and conservation. His work integrates hyperspectral imaging with ecological modeling to address critical issues such as koala habitat mapping, forest health assessment, and water quality monitoring. He has pioneered approaches using plant fluorescence to model growth patterns and has contributed significantly to NASA's OCO2 mission. His recent work emphasizes the development of open-source tools for hyperspectral imaging, making advanced remote sensing more accessible to researchers worldwide. Analysis of Professor Evans's recent publications reveals a strong trend toward practical applications of hyperspectral imaging across diverse environmental contexts. His work spans from precision agriculture applications for cotton farming to koala habitat conservation, demonstrating the versatility of remote sensing technologies. The research shows increasing integration of machine learning techniques with hyperspectral data, enhancing the accuracy and efficiency of environmental monitoring systems. There's also a notable emphasis on open-source solutions, reflecting his commitment to democratizing access to advanced remote sensing technologies. 2016 – Terrestrial Ecosystem Research Network NSW – NSW Chief Scientist Award Multiple travel scholarships from NCCARF, EUFAR, and Australian Research Council 2010 Centre of Excellence for Climate Change PhD top-up Scholarship 2008 Master class Scholarship from Wentworth Group of Concerned Scientists Professor Evans has successfully supervised numerous PhD and Master's students across multiple institutions, demonstrating strong mentorship capabilities. His research is supported by substantial grants including the $198K NSW Department of Environment Koala's in the Landscape project (2023), the University of Sydney's Koala's in the Air project ($70K), and significant funding for the OpenHSI initiative. He has been a Chief Investigator for the Australian Research Council Training Centre on CubeSats, UAVs and Their Applications, securing funding for innovative remote sensing projects. His work with NASA JPL's Surface Biology and Geology Study and collaborations with international space agencies demonstrates the global impact of his research. At the University of New England since 2023, Professor Evans has established the Earth Observation Laboratory with a special focus on water and wildlife habitat (particularly koalas) and riverine water quality. He serves as Vice President of Earth Observation Australia and participates in the AquaWatch Steering Committee for the Commonwealth Department of Defence. His laboratory actively collaborates with industry partners like HyVista Corporation and academic institutions including The University of Sydney. The lab emphasizes open-source approaches to remote sensing technology, exemplified by the OpenHSI project, which has created accessible hyperspectral imaging solutions for researchers worldwide.
Maysson Ibrahim is a Senior Lecturer in Computing at the School of Computing, University of Buckingham, where she joined in 2020 as a part-time Lecturer in Computer Science while continuing her research role at the University of Oxford. She holds a PhD in Bioinformatics from the Buckingham Institute for Translational Medicine (2013) and a BEng in Software Engineering and Information Systems. PhD in Bioinformatics, University of Buckingham (2013) BEng in Software Engineering and Information Systems Her research focuses on bioinformatics, genetic data analysis, and machine learning applications in big data. She has developed computational tools for pathway enrichment analysis and biomarkers identification using gene expression data. At Oxford, she leads bioinformatics work on large-scale biobanks (e.g., UK Biobank) and clinical trials like REVEAL, SHARP, and THRIVE to decode genetic determinants of complex diseases, particularly cardiovascular conditions. Her recent publications emphasize pathway-based algorithms, disease classification, and the integration of gene network analysis with machine learning techniques. Despite no explicit scientific awards listed, her contributions span academic roles in teaching statistics and data science modules at Buckingham and leading analytical pipelines for major clinical studies. She teaches modules such as Data Exploration and Visualisation and Systems and Tools for Data Science in the MSc Applied Data Science programme.
Brendan Mumey is a Professor of Computer Science at Montana State University, affiliated with the Gianforte School of Computing under the College of Engineering. He holds a Ph.D. in Computer Science from the University of Washington (1997), an MS from the University of British Columbia, and a BS in Mathematics from the University of Alberta. His research focuses on applied algorithms, computational biology, and optimization, particularly in pangenomics, flow decomposition, and genomics. Key research areas include DNA/RNA sequence multiassembly, pangenomics, and algorithms for flow decomposition in networks. He is part of the Applied Algorithms Group and develops software through the MSU Algorithms Lab. Notable projects involve NSF-funded initiatives to scale flow decomposition and explore plant genetic diversity using pangenomic tools. Selected awards include multiple Excellence in Research Awards (2007, 2011, 2012, 2013) and an Excellence in Service Award (2011). His teaching spans algorithms, discrete structures, and computational biology at both undergraduate and graduate levels. Grants include NSF support for pangenomic tools, functional genomics, and interdisciplinary mentoring programs. Collaborative work emphasizes bridging theoretical algorithms with practical applications in biology and sustainability.
Björn Jensen is a Professor and Co-Head of the AI Robotics Research Lab at Lucerne University of Applied Sciences and Arts (HSLU), specifically within the Lucerne School of Computer Science and Information Technology. He also teaches medical robotics at the University of Bern's Biomedical Engineering Program. His professional background includes roles at the Autonomous Systems Lab at EPFL, Switzerland, and founding the startup Singleton 3D focusing on 3D laser measurement technology. Educational background: MSc in Electrical Engineering (Automation & Image Processing) from TU Darmstadt (1998), followed by a Master's in Industrial Management from the same institution. PhD in human-robot interaction from EPFL (2005), with research stints at Tokyo University (2005) and involvement in projects like Robox@Expo.02 and Smarter-Elrob. Research interests span robotics, human-robot interaction, autonomous systems, medical robotics, and sensor-based navigation. Notable projects include the 'Smart Ennoblement Factory', 'NaviMow' autonomous lawnmower, and 'Bagger Assistenzsysteme'. His work emphasizes real-world applications of robotics in dynamic environments and human-centric systems. Lab leadership includes co-directing the AI Robotics Research Lab, focusing on advancing robotics technologies for practical scenarios. No scientific awards explicitly listed, but contributions to industry-academia collaborations are highlighted through startup ventures and applied research projects.
Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.
Saim Ghafoor is a Lecturer in the Department of Computing at Atlantic Technological University, Ireland. He holds a PhD in Engineering and Technology from University College Cork (2018), an MEng from Hanyang University (2010), and a BEng in Computer Systems Engineering from Mehran University (2005). His research focuses on advanced wireless communications, including Terahertz networks, green machine learning, and emergency communication systems. He is actively involved in the review process of top-tier journals and conferences. Education: PhD, University College Cork, 2018 MEng, Hanyang University, 2010 BEng, Mehran University, 2005 His work spans Terahertz communications , intelligent radio systems , and 5G/6G network protocols , with contributions to cybersecurity and automation. Notable outputs include a 2023 book on Green Machine Learning Protocols and a 2020 IEEE Communications Surveys review on Terahertz MAC protocols. He leads the WiSAR research group and serves as Associate Editor for Elsevier's Computers & Electrical Engineering . Grants & Labs: Active in the WiSAR (Wireless Sensor Applied Research Gateway) unit at ATU. Collaborates on projects involving THz networks and disaster response systems.
Anargyros Tsadimas serves as a Teaching Associate and member of the Department of Informatics & Telematics at Harokopio University in Athens, Greece. He joined the university in 2004 as a research associate and currently holds a position in teaching laboratory staff. His academic background includes a BSc in Applied Informatics from the University of Macedonia (2002) and an MSc in Advanced Information Systems from the National and Kapodistrian University of Athens (2005). He earned his PhD in Information Systems from Harokopio University, focusing on model-based design of enterprise systems using SysML. His research interests span modeling and simulation of systems, distributed systems, enterprise information systems engineering, and cyber-physical systems. He has contributed over 25 publications in international conferences and journals, including work on SysML extensions for cost analysis, hybrid simulation platforms, and human-centric design of cyber-physical systems. Tsadimas has participated in EU and Greek government-funded R&D projects, with practical experience in startups as a Software Engineer, DevOps, and Technology Advisor. His recent research emphasizes integrating human factors into system design frameworks, exploring cloud pricing policies, and advancing simulation methodologies for complex systems. Notable contributions include developing declarative approaches for executable SysML models and applying systems engineering to transportation infrastructure challenges. No scientific awards are explicitly mentioned in the provided materials. His professional engagement includes advising on systems design projects and collaborating on interdisciplinary initiatives combining technical and human-centric perspectives.
Dr. Jesper Andersson is a Professor of Computer Science and Dean of the Faculty of Technology at Linnaeus University. He holds a PhD from Linköping University (2007) and has extensive leadership experience, serving as Department Chair from 2013–2020. His research focuses on self-adaptive software systems, software reuse, and cyber-physical systems. He has published widely in top venues like ACM Transactions on Autonomous and Adaptive Systems and Computing , and actively contributes to organizing international conferences. Education: Responsible for advanced courses in software design and development processes. Engaged with industry through technical advising for global companies. Completed major projects include developing a master’s program in computer science and the PROSSES project on self-protecting systems. Current research projects include Digital Twin of Organizations (DTO), DIACCESS for sustainable cities, and Aladino for adaptable architectures. His work emphasizes resilience frameworks, decentralized control, and industrial adaptation practices. Key collaborations include leading the AdaptWise research group and co-chairing SEAMS 2023. His articles span self-adaptive patterns, trust-aware systems, and IoT applications, reflecting a strong focus on both theoretical and applied software engineering challenges.
