Henry Jäger is a Professor in Food Technology at the University of Natural Resources and Life Sciences, Vienna (BOKU) since 2014. Previously, he worked as a Project Manager at Nestlé (2012-2014) and Lecturer at TU Berlin (2012-2017), following his PhD/Postdoc at TU Berlin (2006-2012) and Diploma in Food Technology (2000-2006). His research focuses on electrotechnologies in food processing , particularly pulsed electric fields (PEF) and ohmic heating , for microbial inactivation, food preservation, and quality optimization. He explores applications in plant material processing , gluten-free baking , and novel food preservation methods . His work also addresses edible insect processing for allergenicity reduction and protein recovery. Recent publications (2025-2024) analyze synergies between PEF and ohmic heating, biofilm imitation systems for hygiene validation, and computational models for sterilization processes. These studies span food safety , sustainable processing , and functional food design . Henry Jäger actively contributes to scientific communities, serving on the EFFoST managing board , as scientific advisor for food conferences, and as reviewer for journals like Food Chemistry and Trends in Food Science & Technology . He has organized workshops on PEF applications and contributed to EU food technology initiatives.
Yves-Alexandre de Montjoye is an Associate Professor of Applied Mathematics and Computer Science at Imperial College London, where he leads the Computational Privacy Group. He holds a joint affiliation between the Department of Computing and the Data Science Institute. His roles include serving as a Special Adviser on AI and Data Protection to the EC Justice Commissioner Didier Reynders, a Parliament-appointed Commissioner for the Belgian Data Protection Agency, and a Special Adviser to EC Competition Commissioner Margrethe Vestager, co-authoring the 'Competition Policy for the Digital Era' report. He earned his PhD from MIT in 2015 under Alex 'Sandy' Pentland. His master's degrees include an M.Sc. in Applied Mathematics from UCLouvain, an M.Sc. (Centralien) from École Centrale Paris, and an M.Sc. in Mathematical Engineering from KU Leuven. He also holds a B.Sc. in Engineering from UCLouvain. His research interests focus on computational privacy, anonymization techniques, AI safety, and machine learning attacks. He develops methods to 'red team' AI systems and create privacy-preserving mechanisms. His work addresses vulnerabilities such as membership inference, attribute inference, and re-identification risks in datasets, with applications to location tracking, synthetic data, and LLMs. His articles analyze adversarial attacks against privacy systems, emphasizing robustness and practical guarantees. He advocates for privacy-by-design approaches in big data analytics and has explored ethical AI, competition policy in digital markets, and humanitarian uses of mobile data. While no scientific awards are explicitly listed, his contributions have been widely covered in media. He is currently recruiting motivated PhD students for his group at Imperial College. His advising and grants narrative includes work on privacy-preserving technologies and policy implications of AI, with collaborations across academia and public institutions. He is affiliated with the Computational Privacy Group and contributes to platforms like OPAL for privacy analytics. His office is in the ACE Extension building (ACEX 259), accessible via Exhibition Road.
Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.
Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.
Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.
Benjamin Alarie is a Professor and the Osler Chair in Business Law at the University of Toronto Faculty of Law. He is a leading scholar at the intersection of artificial intelligence, law, and taxation, and the co-founder and CEO of Blue J, an AI-powered tax research platform used by over 3,000 organizations across North America and the UK. Education: LL.M. – Yale Law School (2003) J.D. – University of Toronto (2002), with honours M.A. – University of Toronto (2002) B.A. – Wilfrid Laurier University (1999), with high distinction Research Interests: Alarie’s research spans tax law , legal theory , judicial behaviour , and the applications of machine learning in law . He introduced the concept of the “legal singularity”—the idea that legal systems may evolve toward greater clarity and predictability through AI and computational methods. His work explores how AI can enhance legal reasoning, regulatory design, and tax compliance. He is particularly focused on the ethical and practical implications of generative AI in legal and tax practice, including liability, transparency, and the future role of human practitioners in AI-augmented systems. Scientific Awards: PROSE Award (2024) for The Legal Singularity Donner Prize shortlist (2024) for The Legal Singularity vLex Fastcase 50 Honoree (2023) Top 50 Changemaker, The Globe and Mail (2022) Alan Mewett QC Prize for Excellence (2009) Advising & Grants: Alarie is a frequent advisor to courts, government agencies, and global professional networks. His work at Blue J involves strategic partnerships with CPA.com, CPA Canada, the National Association of Tax Professionals, Big 4 accounting firms, and Tax Notes. He also serves as a faculty affiliate at the Vector Institute for AI and the Schwartz Reisman Institute for Technology and Society. Labs & Teams: As CEO of Blue J, Alarie leads a multidisciplinary team of legal scholars, engineers, and data scientists. The platform is used by tax authorities and accounting firms to deliver precise, defensible, and efficient tax research and decision-making tools.
Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.
Dr Brandon M Grainger is an Eaton Faculty Fellow and Associate Professor of Electrical and Computer Engineering at the University of Pittsburgh’s Swanson School of Engineering, where he also directs the Electric Power Technologies Laboratory, serves as Associate Director of the Energy GRID Institute, and co-directs Pitt AMPED. A key architect of Pitt’s electric power program since 2008, he focuses on advanced power conversion, high-voltage electronics, wide-band-gap semiconductors, and aerospace power systems. Education PhD, Electrical Engineering (Power Conversion), University of Pittsburgh, 2014 MS, Electrical Engineering, University of Pittsburgh, 2011 BS, Mechanical Engineering & Minor in Electrical Engineering, University of Pittsburgh, 2007 Executive Education Certificate, Tepper School of Business, Carnegie Mellon University, 2019 Research Focus Dr Grainger’s work lies at the intersection of power electronics, high-voltage engineering, and sustainable energy systems. He specializes in medium- and high-voltage power electronics (HVDC, STATCOM), resonant converters, and ultra-high-power-density designs leveraging SiC and GaN semiconductors. His investigations extend to electric-vehicle traction drives, solid-state transformers, optimized magnetics for aerospace applications, and resilient microgrids. He and his students routinely collaborate with NASA JPL, Johns Hopkins APL, Honeywell Aerospace, and the Naval Research Laboratory, leveraging Pitt’s NSF SHREC center to push the boundaries of power conversion in space and defense systems. Selected Research Themes High-frequency, high-density DC/DC converters for satellite power systems Radiation-tolerant GaN converters and point-of-load power stages Medium-voltage testbed development (13.8 kV, 5 MVA) Rare-earth-free permanent-magnet machine topologies Model-predictive control of multi-phase drives and microgrids Honors & Awards 2024 IEEE Region 2 Outstanding Educator Award 2024 Pitt STRIVE Outstanding DEI Service Award 2019 ESWP Engineer of the Year 2019 ASEE 2nd Place Best Paper Award 2019 SRI Undergraduate Best Mentor Award Richard K. Mellon Endowed Graduate Fellowship National Academies of Science & Engineering Ambassador Senior Member, IEEE Grants & Industry Partnerships Dr Grainger’s research has been continuously funded by federal agencies and industry partners including NASA JPL, Johns Hopkins APL, Honeywell Aerospace, the Naval Research Laboratory, Eaton, and the National Science Foundation through the SHREC Center. These awards support graduate students and post-docs working on next-generation power systems for aerospace, naval, and terrestrial applications. Laboratories & Teams Director, Electric Power Technologies Laboratory (EPTL) Associate Director, Energy GRID Institute Co-Director, Pitt AMPED (Advanced Multimodal Power and Energy Development) Faculty Affiliate, NSF SHREC Center
Handan Kulan serves as Assistant Professor at Yeditepe University's Faculty of Computer and Information Sciences, Department of Information Systems and Technologies since 2024. Previously, she held faculty positions at Istinye University (2023), Uskudar University (2022), and Beykoz University (2020) across computer engineering and software engineering departments. Education: Ph.D. in Computer Engineering, Kadir Has University (2016-2020): Thesis on critical proteins in Down syndrome learning processes M.S. in Computer Science and Engineering, Sabanci University (2013-2014): Thesis analyzing protein residue networks B.S. in Genetics and Bioengineering, Yeditepe University (2007-2013) Second Major in Computer Engineering, Yeditepe University (2009-2013) Her research integrates machine learning with biomedical challenges, specializing in Down syndrome proteomics, neural network analysis of brain aging, and immune system disorders. She develops computational models for protein identification and applies gradient boosting algorithms to biological datasets, bridging AI with healthcare decision systems as demonstrated in her 2023 Springer book. Publications reveal consistent focus on computational approaches to Down syndrome, with recent conference presentations expanding into statistical clustering of biological data and gene ontology analysis for drug discovery. Her work demonstrates methodological evolution from protein network analysis to advanced predictive analytics. Awards: No specific scientific awards documented in source materials. Dr. Kulan actively supervises graduate theses while teaching core computer science courses including Deep Learning, Artificial Intelligence, and Data Structures at both undergraduate and graduate levels across multiple institutions. Her teaching portfolio reflects direct alignment with her research in AI-driven biomedical analysis.
Dr. Philipp Porada is a Junior Professor of Ecological Modeling at the University of Hamburg, affiliated with the Department of Biology within the Faculty of Mathematics, Computer Science and Natural Sciences. He works at the Institute of Plant Sciences and Microbiology, specifically in the Applied Plant Ecology group, based at the Otto Warburg House. His research integrates process-based modeling with ecological field studies to investigate non-vascular vegetation, biogeochemical cycles, and climate-vegetation interactions across multiple temporal and spatial scales. Porada's research focuses primarily on non-vascular vegetation (bryophytes, lichens, and biocrusts), examining their role in global biogeochemical cycles, biodiversity-ecosystem functioning relationships, and paleoclimate dynamics. His work spans from contemporary ecosystem processes to geological time scales, with particular emphasis on the impacts of climate change on non-vascular communities. He has developed several process-based models including LiBry for lichen and bryophyte communities, LiDELS for soil-vegetation interactions, and LYCOm for early vascular plants. His research demonstrates how non-vascular vegetation influences carbon sequestration, water cycling, and soil processes across diverse ecosystems from urban forests to polar regions. Analysis of Porada's publication record reveals a strong interdisciplinary approach combining ecological theory, biogeochemistry, and computational modeling. His work spans multiple ecosystems including peatlands, drylands, urban forests, and coastal blue carbon systems. A consistent theme across his research is understanding how non-vascular vegetation mediates the relationship between environmental conditions and ecosystem functions. His most recent work increasingly focuses on climate change impacts and potential mitigation strategies through vegetation management. Porada leads two major research projects funded by the German Research Foundation (DFG): 'Effects of nutrient limitation on non-vascular vegetation under climate change' and 'The role of early plants for palaeoclimate dynamics'. These projects reflect his dual interest in contemporary environmental challenges and deep-time ecological processes. His collaborative work, evident in his extensive publication record with international researchers, demonstrates strong interdisciplinary connections across ecology, biogeochemistry, and climate science. Dr. Porada maintains an active research laboratory focused on ecological modeling, with particular expertise in non-vascular vegetation dynamics. His team develops and applies process-based models to address questions ranging from micro-scale lichen water relations to global biogeochemical cycles. The research group collaborates extensively with field ecologists, climate scientists, and biogeochemists to ground-truth model predictions and explore new ecological phenomena.
Marios Polycarpou is a Professor of Electrical and Computer Engineering and Director of the KIOS Research and Innovation Center of Excellence at the University of Cyprus. He holds honorary positions at Imperial College London and is a member of Academia Europaea. His expertise spans intelligent systems, adaptive control, machine learning, and critical infrastructure. Education: B.A. Computer Science (Rice University, 1987) B.Sc. Electrical Engineering (Rice University, 1987) M.S. Electrical Engineering (University of Southern California, 1989) Ph.D. Electrical Engineering (University of Southern California, 1992) Research Focus: Polycarpou’s work emphasizes fault diagnosis in cyber-physical systems, water distribution networks, and adaptive control. He pioneers digital twin technologies for infrastructure resilience and develops algorithms for real-time anomaly detection and system optimization. Article Trends: His recent publications address adaptive control strategies, cybersecurity in networked systems, and AI-driven solutions for water management. Key themes include distributed control, event-triggered mechanisms, and transformer-based anomaly localization. Awards: 2023 IEEE Frank Rosenblatt Technical Field Award 2016 IEEE Neural Networks Pioneer Award Fellow of IEEE and IFAC Grants & Leadership: He secured prestigious grants including ERC Advanced and Synergy Grants. He led KIOS CoE’s Horizon 2020 projects and served as IEEE Computational Intelligence Society President (2012–2013). Labs & Teams: Directs the KIOS CoE, a hub for AI in critical infrastructure. Collaborates on projects like ERC Water-Futures, focusing on long-term water system transitions and contamination mitigation.
Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.
Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.
Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).
Dr. Azadeh Ghari-Neiat is a Senior Lecturer in Software Engineering at the University of Queensland's School of Electrical Engineering and Computer Science. She completed her PhD in Computer Science from RMIT University in 2018. Prior to joining UQ, she held academic positions at Deakin University as a Senior Lecturer and at the University of Sydney as a postdoctoral research fellow. Her research focuses on the intersection of Internet of Things (IoT), Mobile Computing, Crowdsourcing, and Cybersecurity. She develops innovative solutions for enhancing connectivity and security in modern computing environments through crowdsourced approaches. Key areas include service composition in sensor clouds, trust management frameworks, and optimization of drone-as-a-service systems. Her publications demonstrate consistent focus on IoT service ecosystems, with recent work exploring blockchain applications and machine learning techniques for dynamic systems. The research trends show evolution from fundamental service composition to AI-driven optimization in distributed environments. Dr. Ghari-Neiat leads projects involving energy service crowdsourcing and secure architectures for cyber-physical systems. Her work maintains strong emphasis on practical applications in delivery systems, UAV networks, and IoT marketplaces.
