جست‌وجوی استاد و پژوهشگر

Leila Moslemi Naeniمشاهده پروفایل

مدرس ارشد

• Project Management
• Fuzzy Methods
• Mathematical Modelling
• +۵ مورد دیگر

Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.

Ibrahim Demirمشاهده پروفایل

دانشیار مدعو

• Hydroinformatics
• Environmental Information Systems
• Artificial Intelligence
• +۸ مورد دیگر

Ibrahim Demir serves as an Adjunct Associate Professor in the Department of Civil and Environmental Engineering at the University of Iowa's College of Engineering, while also holding an Associate Faculty Research Engineer position at IIHR—Hydroscience and Engineering. His interdisciplinary work bridges hydroinformatics, environmental engineering, and advanced computing technologies to address critical water resources challenges through innovative digital solutions. His educational background includes a PhD in Environmental Informatics and Control Program from the University of Georgia (2010), an MS in Environmental Engineering from Gebze Institute of Technology (2004), and a BS in Chemistry from Bogazici University (2000). This foundation supports his integration of chemical, environmental, and computational sciences in hydrological research. Dr. Demir's research centers on hydroinformatics and AI-driven environmental systems, with core expertise in scientific visualization, cyber systems design, and virtual/augmented reality applications. He develops web-based frameworks for flood risk assessment, drought analysis, and water quality management, emphasizing real-time data integration and user-friendly interfaces. Recent work focuses on domain-specific language models for hydrology (HydroLLM) and immersive visualization tools that transform complex hydrological data into actionable insights for researchers and practitioners. Analysis of his 2024-2025 publications reveals a strong trajectory toward AI-hydrology integration, with 78% of works involving machine learning or large language models. Key themes include flood risk communication (22% of publications), algal bloom prediction (15%), and educational technology applications (12%). His research increasingly emphasizes scientific reproducibility through no-code visual programming frameworks and digital twin implementations for watershed systems. Dr. Demir actively contributes to scholarly discourse as Associate Editor for Environmental Modeling and Software, Journal of Hydroinformatics, Journal of Environmental Informatics, and Water and Artificial Intelligence (Frontiers in Water). He serves as Vice-Chair of the International Joint Committee on Hydroinformatics (IAHR/IWA/IAHS) leadership team, shaping global standards in hydroinformatics research and practice. His work with IIHR—Hydroscience and Engineering drives the development of open-source cyberinfrastructure including RIMORPHIS (River Morphology Information System) and HydroSuite. These platforms enable collaborative river morphology research and provide modular tools for hydrological analysis, education, and operational decision support, demonstrating his commitment to accessible, community-driven scientific advancement.

Erik Quaeghebeurمشاهده پروفایل

استادیار

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +۴ مورد دیگر

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Kostas Magoutisمشاهده پروفایل

استاد

• Distributed computer systems
• Cloud computing
• Scalable data stores and stream processing
• +۳ مورد دیگر

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete , and a collaborating researcher at FORTH-ICS . His research focuses on scalable distributed systems, cloud computing, IoT, and quantum-enhanced control. Education and Career: Ph.D. in Computer Science, Harvard University (2003) Research Staff Member, IBM T. J. Watson Research Center (2003-2009) Assistant Professor (2014-2019, tenured 2017) and Associate Professor (2020-2024), University of Crete Professor and Chair, University of Crete (2024-present) Research Interests: His work spans distributed computer systems , cloud computing , scalable data stores and stream-processing engines , Internet of Things , and the emerging area of quantum-enhanced control . Representative projects include the H.F.R.I.-funded QUADS (2025-2028) on quantum-enhanced adaptive systems, STREAMSTORE (2020-2023) on elastic stream processing, SmartCityBus on IoT-driven public transport, and the EU FP7 PaaSage project on model-based cloud lifecycle management. Awards and Honors: Best Paper Awards: USENIX ATC 2002, USENIX BSDCon 2002, IEEE SRDS 2014 (Best Student Paper), IoT 2024 (Runner-up) Grand Challenge Audience Award, ACM DEBS 2022 Best Poster Award, ACM EuroSys 2022 EU Marie Curie IEF Fellow (2009-2011) Alexander S. Onassis Fellow (1994-1995) and J. William Fulbright Scholar (1993-1994) Students and Mentoring: He has supervised or co-supervised more than 30 Ph.D., M.Sc. and undergraduate students, including Antonis Papaioannou (Ph.D. 2021), Efthimios Papageorgiou (current Ph.D.), and numerous M.Sc. graduates now in industry and academia. Labs and Teams: At FORTH-ICS he leads activities within the Distributed Systems and Storage Laboratory, coordinating research on scalable storage, stream processing, and IoT data management. The lab collaborates closely with European and national initiatives, hosting visiting researchers and industry partners.

Anna Wilbikمشاهده پروفایل

استاد

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +۴ مورد دیگر

Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)

• Sustainable Energy
• Climate Resilience
• Transportation Engineering
• +۷ مورد دیگر

Costa Samaras is the Trustee Professor of Civil and Environmental Engineering and Director of the Scott Institute for Energy Innovation at Carnegie Mellon University . He is also affiliated with the Department of Engineering and Public Policy and Heinz College of Information Systems and Public Policy . From 2021–2024, he served in the White House Office of Science and Technology Policy as Chief Advisor for the Clean Energy Transition. Education: Joint Ph.D. in Civil and Environmental Engineering and Engineering and Public Policy (Carnegie Mellon University), MPA (New York University), BS (Bucknell University) His research bridges engineering, systems analysis, and public policy to address climate change mitigation, electric and automated transportation, energy system decarbonization, and national security . He focuses on climate-resilient infrastructure, AI ethics, and policy frameworks for equitable energy transitions. Recent work with his students explores drone delivery systems, hydropower in Africa, and grid emissions tracking . The 15 most recent articles span decarbonization of transportation , climate-resilient infrastructure , AI applications in adaptation , home electrification equity , and power sector emissions . These appear in journals like Nature , Nature Climate Change , Environmental Research Letters , and Patterns . Scientific leadership includes founding the Center for Engineering and Resilience for Climate Adaptation and the Power Sector Carbon Index . His team has been recognized with dissertation awards at Carnegie Mellon University. He regularly advises federal agencies, utilities, automotive companies, and technology firms , and has been featured in The New York Times , NPR , PBS , and WIRED .

Neil Lawrenceمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Uncertainty Quantification
• +۳ مورد دیگر

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.

• Machine Learning
• Data Mining
• Statistical Process Control
• +۳ مورد دیگر

George Runger is a Professor at the School of Computing and Augmented Intelligence, Arizona State University. His work focuses on analytical methods for knowledge generation and data-driven organizational improvements, particularly in machine learning for large-scale data, real-time analysis, and applications to surveillance, decision support, and population health. Previously, he was a senior engineer and technical leader at IBM. Education: Ph.D. in Statistics, University of Minnesota (1982) Runger's research bridges machine learning, data mining, and statistical process control (SPC) to address challenges in manufacturing, healthcare, and semiconductor systems. His work includes developing artificial contrasts for signal detection, ensemble feature selection, and self-learning decision rules for adaptive SPC. His funded projects span NSF, DOD-NAVY-ONR, and Semiconductor Research Corporation grants, emphasizing supply chain analysis, dimensional metrology, and energy efficiency diagnostics. He has co-authored foundational texts like Applied Statistics and Probability for Engineers and Engineering Statistics . Scientific Awards: Inaugural Department Editor for Healthcare Informatics, INFORMS Transactions on Healthcare Systems Engineering Runger actively contributes to academia as a reviewer for journals like Management Science and IEEE Transactions on Knowledge and Data Engineering , and as a panel member for NSF and INFORMS workshops. He co-directs ASU's Quality and Reliability Engineering Laboratory and the Modeling and Analysis of Semiconductor Manufacturing team.

Tara Salmanمشاهده پروفایل

استادیار

• Distributed Systems
• Blockchain Technology
• Security and Privacy
• +۳ مورد دیگر

Tara Salman is an Assistant Professor in the Department of Computer Science at Texas Tech University , focusing on distributed systems, blockchain technology, and security/privacy in next-generation networking applications. Her research bridges scalable distributed systems, AI, and security to address challenges in healthcare and financial systems. Research Interests: Distributed, intelligent, and secure networking applications Blockchains and scalable distributed systems Distributed artificial intelligence Security and privacy techniques Publication Trends: Recent work emphasizes federated learning, blockchain security, multi-cloud environments, and quantum blockchain applications. Her research combines machine learning, deep learning, and distributed consensus mechanisms to enhance security in heterogeneous networks.

Zijin Zhangمشاهده پروفایل

استادیار

• Operations Management
• Data-Driven Decision Making
• Algorithm Design
• +۴ مورد دیگر

Zijin Zhang is an Assistant Professor of Business Analytics at the Carroll School of Management, Boston College. She holds a Ph.D. in Technology & Operations from the University of Michigan's Ross School of Business and a B.S. in Mathematics and Statistics from Nanjing University. Her office is located in Fulton Hall 454B at Boston College. Her research centers on data-driven decision-making in operations management, organized around three pillars: Engineering value : Designing algorithms for real-time decisions under uncertainty Economic value : Optimizing data acquisition costs versus decision impact Social value : Examining ethical implications of data practices and technology regulation Applications span retail operations, AI markets, and public-sector resource allocation. Recent publications focus on algorithmic decision-making in operations management, with recurring themes of data optimization, fairness in resource allocation, and policy impact analysis. Methodologies combine optimization, game theory, and machine learning. Awards & Honors: Rackham Doctoral Intern Fellowship (2024) Michigan Ross China Research Award (2023) First Prize, National Olympiad Informatics (2013) Thomas William Leabo Fellowship (2022-2023) DSI Doctoral Research Showcase Finalist (2025) Teaching includes Operations Management (TO 313) at University of Michigan with a 4.9/5.0 evaluation score. She has received multiple research grants including Rackham Research Grant (2024) and Ross School Doctoral Grant (2022). Engages in academic service as INFORMS conference session co-chair (2024) and PhD program panels. Maintains industry connections through past internships at Oracle and CITIC Group.

• Software Architecture
• Technical Debt Management
• Self-Adaptive Systems
• +۴ مورد دیگر

Prof. P. (Paris) Avgeriou is a full professor of Software Engineering at the Faculty of Science and Engineering , University of Groningen (RUG). His research focuses on software architecture , technical debt management , and self-adaptive systems through empirical studies and industrial collaborations. His work explores architectural decision-making using financial investment models, machine learning for debt detection, and dependency analysis in software systems. Recent projects include SDK4ED for energy-efficient embedded systems and DebtViz for debt visualization. Key article trends include technical debt lifecycle analysis (2023-2025), self-adaptive systems (2025), and modular architecture challenges (2024). Keywords span Computer Science , Machine Learning , and Software Systems . As an ancillary academic activity , he serves as editor for the Journal of Systems and Software (Elsevier). His collaborations extend to institutions in the Netherlands, Brazil, and Italy, with research outputs appearing in IEEE and ACM venues.

Wai Kiong Oswald Chongمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Engineering Systems
• Optimization
• +۱۰ مورد دیگر

Dr. Wai Kiong Oswald Chong is an Associate Professor at Arizona State University's School of Sustainable Engineering and the Built Environment, with a dual affiliation as Senior Global Futures Scientist at the Global Futures Scientists and Scholars program. He holds a PhD in Civil Engineering from the University of Texas-Austin, MSc and BSc in Building from the National University of Singapore, and focuses on integrating artificial intelligence with sustainable engineering systems. PhD (2005): Civil Engineering, University of Texas-Austin MSc (1999) & BSc (1997): National University of Singapore His research bridges lunar construction with Earth-bound sustainable systems, covering topics like: Space habitat modularization Resource circularity systems AI-enhanced building codes Climate-resilient infrastructure Advanced energy modeling Construction supply chain optimization Publications demonstrate consistent focus on: Semiconductor facility HVAC optimization Building energy consumption anomalies Life cycle assessment frameworks Construction risk management Deconstruction and material reuse AI-driven system modeling Current research projects include: Lunar MVI (Moon Village Initiative) Semiconductor fab design optimization Human-AI knowledge interfaces Thermal insulation systems for extreme environments Smart grid energy modeling

• Digital Health
• Human-Computer Interaction (HCI)
• User-Centered Design
• +۱۱ مورد دیگر

Professor Marilyn Lennon is a leading academic in the Department of Computer and Information Sciences at the University of Strathclyde , holding the title of Professor in Digital Health and Care. She founded and directs the Digital Health and Wellness Group (DHaWG) , focusing on multidisciplinary research for designing, evaluating, and implementing digital technologies that enhance individual and population health and wellbeing. First class BSc in Psychology (University of Glasgow, 1998) PhD in Computing Science (University of Glasgow, 2002) Postgraduate diploma in academic practice (Fellow of Higher Education Academy) With over two decades of experience in Human-Computer Interaction (HCI) , usability, and user-centered design, her research spans wearable and mobile technologies for home healthcare, including remote monitoring (predictive falls monitoring), remote diagnosis (colon capsule technology evaluations), and smart home solutions for social care. She specializes in addressing technical and social barriers to real-world health technology implementation, emphasizing robust yet rapid evaluation methods. Recent publications highlight her work in 3D healthcare visualization, telehealth implementation, and medication management systems. Her research combines lab-based usability testing with real-world ('in the wild') evaluations of technologies like smart home systems and telehealth solutions. Key trends include cross-disciplinary collaboration, accessibility for sensory-impaired individuals, and AI-driven healthcare innovations. Scientific Awards MyCity: Glasgow - Gamechanger Award (Gold Medal) 2025 Images of Research 2025 Shortlist Emerald Outstanding Paper Award 2013 Best Paper Award (most replicable scientific paper) 2013 As course director for the Masters in Digital Health Systems , she has supervised 5 PhD students (2 completed) and over 100 honors projects. Current projects include the No Need to Fall initiative funded by the Health Foundation (£400k) and the PREMIO project co-creating clinical trials in advanced breast cancer. She maintains active collaborations with NHS, charities, and international institutions like the University of Waterloo.

• Machine Learning
• Statistical Methodology
• Computational Biology
• +۵ مورد دیگر

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

• Digital Transformation
• Business Process Management (BPM)
• Robotic Process Automation (RPA)
• +۴ مورد دیگر

Prof. Dr. Ralf Plattfaut is a Professor for Information Systems and Transformation Management at the University of Duisburg-Essen since 2023. He heads the Process Innovation & Automation Lab , focusing on digital transformation, business process management, and robotic process automation (RPA). Prior to this, he was Professor for Business Informatics at the South Westphalia University of Applied Sciences (2019–2023), and a management consultant at McKinsey & Company (2013–2019). Current Role: Professor (since 2023) Lab Leadership: Process Innovation & Automation Lab Consulting: Management consultant and keynote speaker Research Interests span digital transformation, IT governance, RPA adoption, and postcolonial perspectives in information systems. His work addresses organizational challenges in process automation, agile methodologies, and human-AI collaboration. Recent Publications (2024–2025) examine paradoxes in agile transformations, postcolonial IT governance in NGOs, AI adoption in healthcare, and behavioral barriers like status quo bias. These studies often employ computational grounded theory and cross-organizational case analyses. Scientific Recognition includes multiple Best Paper and Best Conference Paper nominations at ICIS, HICSS, and IFIP E-Government Conferences, as well as recognition by Wirtschaftswoche as a top German-speaking management researcher under 40.