Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.
Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.
Manolis Koubarakis is a Professor and Director of Graduate Studies at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is affiliated with the Archimedes Unit at Athena Research Center and is a member of ELLIS. His research focuses on Artificial Intelligence and Data Science, particularly in Linked Geospatial Data, Earth Observation, and Knowledge Graphs. Education: PhD in Computer Science (National Technical University of Athens), M.Sc. in Computer Science (University of Toronto), and B.Sc. in Mathematics (University of Crete). Research Interests: His work spans AI applications in geospatial data, entity resolution, ontology-based data access (OBDA), and semantic web technologies. He leads projects like ExtremeEarth (AI for Copernicus data) and LEO (linked Earth Observation data). Awards: 2015: Fellow of the European Association of Artificial Intelligence (EurAI) 2022: Best Demo Award at CIKM for Copernicus App Lab Advising & Grants: Supervises students in AI and data science. Collaborates on EU-funded projects like BigDataEurope and participates in initiatives like the Standing Scientific Committee for AI in Greece's justice system. Labs & Teams: Heads the AI Lab (ai.di.uoa.gr) and contributes to the MaDgIK group, focusing on data and knowledge management systems.
Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.
Yann-Gaël Guéhéneuc is a Professor at Concordia University's Department of Computer Science and Software Engineering. He leads the Ptidej Team, focusing on software engineering methodologies, IoT systems, and game engine architecture analysis. His research emphasizes static/dynamic analyses, service-oriented architectures, and machine learning design patterns. Current Affiliations: Concordia University (Full-time Professor) Ptidiej Team Lead Research Interests: Specializes in IoT system testing, microservices architecture, game engine design patterns, and anti-pattern detection in multi-language systems. His work bridges theoretical software engineering principles with practical industrial applications, particularly in legacy system modernization and machine learning system design. Recent Trends in Publications: Focuses on IoT testing methodologies, machine learning architecture patterns, and service-oriented system transformations. His 2025 works advance IoT system taxonomy and game engine analysis techniques. Advising: Supervises MASc and PhD programs in Software Engineering and Computer Science Labs/Teams: Ptidej Team develops software tools for system analysis (e.g., Magnet, SyDRA)
Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.
Syed Ahmad Chan Bukhari is an Associate Professor in the Department of Computer Science, Mathematics, and Science at The Lesley H. and William L. Collins College of Professional Studies, St. John's University, New York. He also serves as the Director of Research and Director of Healthcare Informatics at CCPS. Bukhari holds a Ph.D. in Computer Science from the University of New Brunswick and a postdoctoral fellowship from Yale University, with additional work at Stanford University's CEDAR Metadata Center. His research focuses on biomedical informatics, trustworthy AI, machine learning, and healthcare data standards. He teaches graduate and undergraduate courses in healthcare informatics and computer science. Bukhari leads an NSF-funded lab (bukharilab.org) and has developed protocols for biomedical data standardization with NCBI, including MiAIRR and CEDAR-to-NCBI pipelines. Notable awards include ACM Distinguished Speaker, Senior Member IEEE, and an NIH-NCBI Fellowship (2016). His work emphasizes reproducibility in biomedical data and FAIR principles. Recent projects include federated knowledge graphs, AI-driven tutoring systems, and sustainable AI in finance.
Dr. Xiao Li is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University, School of Engineering. His research focuses on blockchain technology applications in distributed systems (Edge Computing, IoT, Federated Learning), machine learning, and privacy-preserving frameworks. He has a Ph.D. in Computer Science from The University of Texas at Dallas (2024) and was awarded the Jan P. Van der Ziel Engineering Fellowship there. Education: Ph.D. in Computer Science, The University of Texas at Dallas, 2024 Research interests include: Blockchain architecture optimization in resource-constrained environments Machine learning model development for cryptocurrency analysis Cybersecurity disclosure sentiment analysis using unsupervised techniques Low-resource data challenges in psychiatric clinics Professional activities: Program Committee Member, IEEE International Workshop on Blockchain and Smart Contracts (IEEE BSC 2024) Recruitment of PhD/Master’s students specializing in blockchain, distributed machine learning, and edge computing Office Location: Bergin 206 Office Hours: Mon/Wed 4pm-5pm (Winter 2025)
Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)
Prof. Catherine De Wolf is an Assistant Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering and Deputy Head of the Institute of Construction and Infrastructure Management. She leads the Chair of Circular Engineering for Architecture (CEA), an interdisciplinary lab focused on automating building material reuse through digital innovation. Her work bridges academia, government, and industry, exemplified by partnerships with institutions like the Centre Pompidou and initiatives like Anku and the Digital Circular Economy (DiCE) Lab. She is also a faculty member at ETH Zurich's AI Center and collaborates with EMPA's Urban Energy Systems Lab. Her roles include Chair of the Design++ Advisory Board and PI in the National Centre of Competence in Research on Digital Fabrication (DFAB). Education: Catherine holds a PhD in Building Technology from MIT, with prior studies in Civil Engineering and Architecture at VUB and ULB. She has additional training in documentary filmmaking and postdoctoral research at the University of Cambridge and EPFL's Structural Xploration Lab, funded by Marie Sklodowska-Curie and Swiss Excellence scholarships. Research Interests: Her work centers on digital tools for circular construction, including blockchain-based material passports, AI-driven design, and automated deconstruction planning. Key areas include: Material reuse and lifecycle analysis Building Information Modeling (BIM) applications Decentralized data networks in construction Carbon reduction in structural systems Interdisciplinary collaboration across engineering, architecture, and computer science Awards: Marie Sklodowska-Curie Postdoctoral Fellowship (European Commission) Swiss Excellence Scholarship Advising & Grants: Catherine has overseen projects funded by EU initiatives and industry partnerships. Her research group actively collaborates with firms like Arup and Thornton Tomasetti through initiatives such as the Structural Engineers 2050 Commitment. She advises on policy frameworks like the EU's Level(s) sustainability standard. Labs & Teams: Her CEA lab operates with 20+ researchers and has pioneered tools like the '5D Digital Circular Workflow.' The lab's work is showcased in real-world projects such as the Centre Pompidou material reuse case study.
Professor Jonathan Corney holds the Chair of Digital Manufacture in the School of Engineering at the University of Edinburgh. His research focuses on advanced manufacturing technologies, including digital twin applications, smart factory optimization, sustainability engineering, and additive manufacturing. He leads projects addressing human factors in industrial environments, predictive analytics for production processes, and intellectual property challenges in modern manufacturing systems. His academic background encompasses mechanical engineering with specialization in CAD/CAM systems, hydroforming technology, and patent analysis for design innovation. Corney has pioneered methods like the Economic and Environmental Impact Assessment for Sustainability (EENIAS), and developed decision support frameworks for energy-efficient scheduling and garment reprocessing in circular economies. Key research themes include: Smart factory design through real-time worker movement analysis Cyber-physical systems for supply chain optimization Machine learning applications in manufacturing process control Human-centric automation and safety protocols His recent work emphasizes predictive modeling using spatio-temporal graph networks, digital twin integration in assembly processes, and sustainable manufacturing practices. Over 150 peer-reviewed articles demonstrate his contributions to near-net-shape manufacturing, intellectual property management, and crowdsourced design methodologies.
Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.
Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.
Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.
Dr. Vasiliki Kantere is a Professor at the School of Electrical Engineering and Computer Science within the Faculty of Engineering at the University of Ottawa . She has held previous academic positions, including: Senior Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens Maître d’Enseignement et de Recherche and Maître Assistante at the University of Geneva Junior Assistant Professor at the Department of Electrical Engineering and Information Technology, Cyprus University of Technology Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne Her research interests span a wide range of interdisciplinary domains, including: Big Data Management: data storage optimization, analytics workflows, and deployment on hybrid infrastructures Big Data Analytics: support for users of varying expertise and large data graphs Machine Learning: applications in resource management and query optimization Cloud Computing: cost-aware data management and service-level agreements P2P Systems: query processing and mobile peer environments Semantic Web: ontology mapping and semantic integration Distributed Systems: federated databases and grid computing Databases: query languages and semi-structured data management General: spatial data, privacy and security, and sensor networks No academic awards, funded grants, or advising roles are explicitly listed in the provided information. Her professional contributions are reflected through her extensive research collaborations and diverse academic experiences. The text does not mention any affiliated research labs or teams.
