جست‌وجوی استاد و پژوهشگر

Ibrar Yaqoobمشاهده پروفایل

پژوهشگر ارشد

• Blockchain and Applications
• NFTs and Metaverse Applications
• Mobile Edge-Cloud Computing
• +۴ مورد دیگر

Dr. Ibrar Yaqoob serves as a Senior Research Fellow and Program Lead for the Smart and Resilient Supply Chain strand at the Artificial Intelligence and Cyber Futures Institute (AICF) at Charles Sturt University. He leads a research team comprising PhD students and postdoctoral fellows, and previously held research positions at Khalifa University in the UAE and Kyung Hee University in South Korea. Dr. Yaqoob holds a PhD in Computer Science from Universiti Malaya (2017) and has been a Visiting Academic at the University of Cambridge. His research focuses on blockchain, NFTs, and metaverse applications for healthcare, supply chain and logistics, wireless networks, IoT, and smart cities, with additional expertise in mobile edge-cloud computing and big data. Dr. Yaqoob has published over 100 research articles with more than 24,000 citations and an h-index of 58, with 98.9% of his publications appearing in Q1 journals. His recent publications demonstrate a strong trend toward practical blockchain implementations across diverse sectors including healthcare data management, sustainable supply chains, and smart city infrastructure. His work frequently combines blockchain with emerging technologies like NFTs, metaverse applications, and large language models to solve complex real-world problems in supply chain transparency, medical data security, and sustainable development. Ranked in the world's top 0.1% of researchers for three consecutive years (2023-2021) as a Highly Cited Researcher Listed among the World's top 2% scientists for five consecutive years (2020-2024) 2024 IEEE Consumer Electronics Magazine Best Paper Award (1st Place) 2024 Vice Chancellor's Researcher of the Year Commendation 2024 AICF Research Excellence Award 13 highly cited papers (top 1%) and 21 most downloaded articles Dr. Yaqoob serves as Chief Investigator on multiple significant research projects including the AgriTwins project (AUD $2.38 million), Trustworthy Operations and Supply Chain Management project (AUD $10,000), and Decentralized AI Juries project (AUD $13,875). He actively mentors PhD students and postdoctoral researchers while serving on editorial boards for prestigious journals including IEEE Network Magazine, Future Generation Computer Systems, and IEEE Access. His research team at the AICF Institute focuses on bridging emerging technologies with practical applications to address real-world challenges in supply chain resilience and sustainability.

• Combinatorial Optimization
• Algorithm Design and Analysis
• Game Theory and Social Choice
• +۴ مورد دیگر

Jannik Matuschke serves as Associate Professor in the Department of Decision Sciences and Information Management at KU Leuven's Faculty of Economics and Business (FEB). He is an active member of both the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM), holding office at Hogenheuvelcollege (HOGC) 04.123 in Leuven, Belgium. His academic career spans multiple prestigious institutions across Europe, reflecting his international recognition in operations research and theoretical computer science. Dr. Matuschke completed his PhD in Mathematics in 2013 at Technische Universität Berlin under Martin Skutella and Britta Peis. His academic journey included a postdoctoral position at Universidad de Chile (2014), a DAAD P.R.I.M.E. fellowship at the University of Rome 'Tor Vergata' (2015), and a junior group leader/lecturer position at Technische Universität München (2016-2018) before joining KU Leuven in 2019. His research focuses on the intersection of discrete mathematics, theoretical computer science, and operations research, with specific expertise in combinatorial optimization, algorithm design and analysis, game theory and social choice, robustness and uncertainty, and logistics applications. Dr. Matuschke has made significant contributions to network flow theory, security games, and robust optimization frameworks, developing both theoretical foundations and practical applications. Analysis of his recent publications reveals a strong emphasis on robustness under uncertainty, with increasing focus on security applications and theoretical aspects of scheduling problems. His work demonstrates consistent innovation in decomposing complex optimization problems and developing approximation algorithms for challenging combinatorial settings. 2024 Meritorious Service Reward of the journal Operations Research Dr. Matuschke actively mentors several PhD students and postdocs, including Yanfei Chen, Léonie Gallois, Phablo Moura, Felix Rauh, and Fei Wu. He leads significant research projects such as 'Rigidity and Flexibility: Structures and Algorithms' (starting 2025), 'Designing resilient and recoverable infrastructures via multi-level and multi-stage optimization' (2022), and 'Optimization and analytics for stochastic and robust project scheduling' (2020), with funding from diverse sources including KU Leuven start-up grants, FWO research projects, and DAAD fellowships. As an integral member of the academic community, Dr. Matuschke serves as Associate Editor for Omega (since 2023), Operations Research Letters (since 2024), and OR Spectrum (since 2019). He co-chairs the program committee for WAOA 2025 and has participated in numerous prestigious conference committees. At KU Leuven, he coordinates the 'Master's Thesis in Production and Logistics' program and serves on the Teaching Portfolio Peer Review Committee.

Peter J.H.J. van der Waerdenمشاهده پروفایل

مدرس

• Transportation Planning
• Urban Mobility
• Transportation Economics
• +۳ مورد دیگر

Peter J.H.J. van der Waerden is a University Lecturer in the Department of Urban Planning and Transportation at Eindhoven University of Technology. His work focuses on sustainable urban mobility systems, transportation modeling, and public transport efficiency. He has been involved in projects such as the Urban Mobility Changemaker initiative (2022-2022) and OSCAR, exploring off-street parking solutions for carsharing. His research integrates choice experiments, spatial analysis, and environmental impact assessments to address urban challenges. Van der Waerden has contributed to over 191 research outputs, including articles on light-rail transit environmental effects, cycling infrastructure innovations, and climate change adaptation strategies. He teaches courses like Big Data for Urban Analysis and Geographic Modeling, emphasizing data-driven approaches to urban development. His datasets include drone-based spatial analyses for 3D urban modeling. Recent work explores participatory urban planning methods and citizen engagement in climate resilience initiatives. He actively participates in peer-review activities for journals like the Journal of Public Transportation and Journal of Cycling and Micromobility Research. Collaborations span institutions globally, addressing topics like parking system compliance, intersection design for cyclists, and multi-state supernetwork analysis for transport systems.

Federica Marone Welfordمشاهده پروفایل

پژوهشگر

• X-ray Tomography
• Seismology
• Tomographic Reconstruction
• +۷ مورد دیگر

Federica Marone Welford is a Beamline Scientist at the TOMCAT beamline of the Swiss Light Source (SLS) at the Paul Scherrer Institute (PSI) . She holds an Earth Sciences degree with a focus on seismology and a PhD in seismology from ETH Zurich , following a postdoctoral fellowship at the Berkeley Seismological Laboratory . Her work centers on advancing tomographic reconstruction algorithms, mitigating artifacts, and optimizing computational infrastructure for high-speed X-ray imaging at the TOMCAT beamline. Research Focus: X-ray tomography methodology, data compression, real-time reconstruction systems, and applications in paleontology, earth sciences, additive manufacturing, and energy research. Collaborations: Engages with global researchers and industry partners, particularly in battery/fuel cell analysis and laser powder bed fusion. Teaching: Lectures at ETH Zurich on X-ray imaging techniques. Publications highlight her contributions to X-ray scattering tensor tomography, dynamic process visualization, and computational advancements in imaging systems.

• Structural Health Monitoring
• Structural Control
• Stochastic Fatigue
• +۴ مورد دیگر

B. F. Spencer Jr. is the Nathan M. and Anne M. Newmark Endowed Chair in Civil Engineering at the University of Illinois at Urbana-Champaign, where he directs the Multi-Axial Full-Scale Sub-Structured Testing & Simulation Facility and the Smart Structures Technology Laboratory. He joined the university in 2002 after serving as Leo E. and Patti Ruth Linbeck Professor of Engineering at the University of Notre Dame (1985-2002). Education includes: Ph.D. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1985) M.S. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1983) B.S. in Mechanical Engineering, University of Missouri-Rolla (1981) His research focuses on pioneering innovations in structural health monitoring, stochastic mechanics, and smart sensor technologies. Key areas include development of wireless sensor networks for real-time infrastructure assessment, seismic hazard mitigation strategies, and AI-driven damage detection systems. His work bridges theoretical computational mechanics with practical civil engineering applications to enhance resilience against natural disasters. Recent publications emphasize digital twins, UAV-based structural inspection, machine learning for damage identification, and advanced sensor networks. Trends show strong integration of AI, 3D visualization, and edge computing for rapid post-disaster evaluation and predictive maintenance of critical infrastructure. Major scientific honors: ASCE Housner Medal (2015) J.M. Ko Medal (2014) Foreign Member of Polish Academy of Sciences (2005) Structural Health Monitoring Person of the Year (2011) JSPS Fellowships (1999, 2000) He leads significant infrastructure projects including NSF-funded facilities and industry collaborations. Laboratory initiatives involve full-scale testing of bridges, gates, and seismic mitigation systems. Educational outreach includes K-12 STEM programs like 'Shakes and Quakes' to inspire future engineers.

• Generative Programming
• Model-Based Development
• Software System Families
• +۷ مورد دیگر

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

Tommaso Calarcoمشاهده پروفایل

استاد

• Quantum Optimal Control
• Quantum Computing
• Quantum Technologies
• +۳ مورد دیگر

Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.

Lawrence Sassمشاهده پروفایل

استاد

• Digital Fabrication
• Low-Cost Housing
• Construction Automation
• +۳ مورد دیگر

Lawrence Sass is a Professor and Chair of the Computation Group in the Department of Architecture at MIT's School of Architecture + Planning. His research focuses on design innovation using digital fabrication, particularly in low-cost housing and construction automation. He pioneered methods to reduce home construction steps through computational tools, including the concept of Snap Assembly building systems showcased at MoMA in 2008. His work has inspired startups in digitally fabricated housing. B.Arch from Pratt Institute (1990) Master’s (1994) and PhD (2000) from MIT Research interests include sustainable construction techniques, 3D printing in architecture, and material optimization. He co-founded LuBan3D, a software system enabling large-scale 3D modeling with affordable tools. Current projects explore AI and robotic systems for home production. Awards include the prestigious MacVicar Faculty Fellow recognition. His teaching includes courses like Design Computation (4.500) and Tiny Fab (4.501), emphasizing computational methods in architectural design. Collaborations include work with Singapore-based researcher Prof. Lujie Chen on large-scale fabrication software. His lab focuses on integrating digital tools into physical production workflows, aiming to democratize construction through accessible fabrication technologies.

• Traditional Chinese Medicine (TCM)
• Data Mining
• Medical Informatics
• +۴ مورد دیگر

Associate Professor Josiah Poon is affiliated with the School of Computer Science at the University of Sydney. His research focuses on applying data mining and IT techniques to Traditional Chinese Medicine (TCM), particularly analyzing herbal combinations for effective treatments. He collaborates with institutions in China to improve TCM evidence and has contributed to clinical data analysis, medical informatics, and multimodal AI systems. Teaching includes courses such as INFO1003 (Foundations of IT) and INFO9003 (IT for Health Professionals). Current research students are Rina CABRAL (Multimodality Representation), Yan LI (Long Document Comprehension), and Xiaobin LU (Financial Decisions). Research highlights include developing algorithms to quantify TCM efficacy, analyzing complementarity in herbal combinations, and applying machine learning to healthcare data. Notable projects include a randomized controlled trial on pneumococcal vaccination (2021) and a Google-funded multimodal health detection system (2020). Key areas of expertise span TCM informatics, medical data analytics, and AI-driven healthcare solutions. His work bridges Eastern/Western medicine through computational methods, emphasizing evidence-based practices in TCM.

Alfred Heroمشاهده پروفایل

استاد

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +۱۰ مورد دیگر

Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS) with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is affiliated with multiple research centers including the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS). Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization using statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His research group has produced numerous PhD students who have gone on to prominent academic and industry positions. His recent publications show a strong focus on high-dimensional statistical methods, machine learning theory, network analysis, and applications in biomedical domains. The research trends indicate increasing emphasis on multimodal data fusion, robust learning algorithms, and applications to complex systems in biology and security domains. His work bridges theoretical foundations with practical implementations across diverse application areas. Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fourier Award in Signal Processing from the IEEE Hero has advised numerous PhD, MS, and undergraduate students who have gone on to successful careers in academia and industry. His research has been supported by various grants, though specific grant details are not provided in the source material. His lab collaborates extensively across disciplines with researchers in statistics, biomedical engineering, and computational medicine. The Hero Research Group maintains active collaborations with institutions worldwide and participates in major conferences in machine learning, signal processing, and data science.

• Healthcare Informatics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Maqbool Hussain is a Senior Lecturer in Computer Science at the College of Science and Engineering. His research focuses on healthcare informatics, artificial intelligence (AI) applications in medical decision-making, and the development of knowledge-based systems for aerospace and healthcare sectors. He has contributed significantly to areas such as digital twin frameworks for critical care workflows, clinical decision support systems, and semantic interoperability in healthcare. Research Interests Hussain’s work spans multiple disciplines, including: Healthcare Informatics and AI-driven medical solutions Knowledge Graphs and Large Language Models (LLMs) in healthcare and aerospace Digital twins for optimizing critical care and aerospace maintenance Data-driven clinical decision support systems Medical data security and interoperability standards Machine learning for text annotation and classification Recent Research Trends Recent publications highlight his focus on integrating advanced technologies like LLMs and digital twins into healthcare and aerospace contexts. His work addresses challenges in critical care workflow optimization, kidney disease diagnosis, and aerospace product maintenance through innovative knowledge graph approaches. He also explores the application of transformer-based models for text analysis and the secure exchange of medical data using biometric methods. Labs and Teams Hussain is affiliated with the College of Science and Engineering, contributing to interdisciplinary research teams focused on healthcare technology, aerospace systems, and AI applications. His collaborations span academic and industry partners, emphasizing practical solutions for healthcare informatics and aerospace engineering challenges.

• Multi-modal Composition
• Feminist Theory
• Trauma and Psychoanalytic Theory
• +۳ مورد دیگر

Tara J. Roeder is an Associate Professor and Director of the First Year Writing Program at St. John's University's Department of Core Studies. Her work focuses on multi-modal composition, feminist theory, trauma studies, and critical animal theory. She holds a Ph.D. from CUNY Graduate Center (2014) and has taught since 2006, emphasizing interdisciplinary approaches to writing instruction. Her research explores intersections between anti-speciesism and social justice, with notable publications like *Critical Expressivism* (2015) and recent contributions to MLA's *Social Justice in Action* (2024). Education: Ph.D. English (CUNY GC), M.A. & B.A. English (St. John's University) Leadership: FYW Program Director, Committee Chair for Program Review Awards: 2020 Hammond House Prize (2nd place), Pushcart nominations Roeder advocates for innovative pedagogy through platforms like the 'Coming to Writing' conference and has presented widely on composition studies, trauma-based writing, and global learning. Her creative works include poetry chapbooks and fiction recognized in literary journals.

Antony Bhavnaمشاهده پروفایل

استاد

• Deep Learning
• Medical Image Analytics
• Ophthalmic Data Analytics
• +۳ مورد دیگر

Prof. Antony Bhavna is a Professor in the Information Technology discipline at Federation University's Institute of Innovation, Science and Sustainability (IISS). Her research focuses on AI-driven medical image analytics, particularly in ophthalmology, with expertise in deep learning for disease detection and personalized prognosis. She holds a PhD from the University of Iowa and is a Senior Member of IEEE and the Association for Research in Vision & Ophthalmology (ARVO). Her work spans glaucoma diagnosis improvement, OCT image processing, and AI tools for neovascular AMD prediction. Recent projects include AutoML-based glaucoma classification and multi-modal cohort selection systems. She teaches Machine Learning & AI, emphasizing research communication. No formal awards are listed, though her active grants and collaborations with global institutions like the Charchar Lab and the University of Iowa highlight her impactful research. She advises on AI ethics in healthcare and collaborates with clinical partners for translational studies.

Jakob PUCHINGERمشاهده پروفایل

استاد

• Logistics
• Urban Mobility
• Disruptive Technologies
• +۳ مورد دیگر

Jakob Puchinger is a Professor in Supply Chain Management and Logistics at EM Normandie since 2022. He also serves as an affiliate professor at the Laboratoire Génie Industriel at CentraleSupélec, Université Paris-Saclay, and co-director of the Future Cities Lab with Centrale Pékin. He holds a PhD in Computer Science from TU Wien (2006), focusing on metaheuristics and integer programming for cutting and packing problems. His research spans logistics, urban mobility, disruptive technologies, and transport system optimization. Professional roles include Head of the Operations Management team at CentraleSupélec (2019–present), holder of the Anthropolis Chair at IRT SystemX (2015–2022), and scientist at the Austrian Institute of Technology (2008–2014). He has authored over 80 publications and supervised six theses, with five ongoing. Research interests emphasize sustainable urban logistics, last-mile delivery optimization, and shared mobility systems. Key contributions include studies on electric vehicles, autonomous systems, and policy frameworks for decarbonization. His work integrates optimization algorithms, agent-based modeling, and real-world case studies to address complex mobility challenges. Recent publications focus on route optimization for cold chain logistics, decarbonization policies, and collaborative truck-robot delivery systems. He actively contributes to conferences like Transportation Research Board and ROADEF, and co-authored books on urban mobility futures and robomobility revolution.

José PICHERALمشاهده پروفایل

استاد

• Signal Processing
• Acoustic Imaging
• Array Processing
• +۵ مورد دیگر

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.