جستجوی استاد راهنمای دانشگاهی

Florent PLEDمشاهده پروفایل

دانشیار

• Computational Mechanics
• Uncertainty Quantification
• Stochastic Multiscale Modeling
• +۵ مورد دیگر

Florent PLED is an Associate Professor at Gustave Eiffel University, working within the Multiscale Modeling and Simulation Laboratory (MSME) UMR 8208 CNRS. His research focuses on advanced computational mechanics, particularly in the areas of uncertainty quantification, stochastic multiscale modeling, and statistical inverse problems applied to heterogeneous materials. Dr. PLED's research interests span several interconnected fields in computational mechanics and materials science. His work primarily investigates uncertainty quantification in computational mechanics, stochastic multiscale modeling of random heterogeneous materials, and statistical inverse identification of probabilistic models. He has made significant contributions to computational stochastic homogenization, machine learning applications for solving inverse problems in mechanics, and multiscale computational methods including domain decomposition approaches. His research also encompasses model verification techniques, global and goal-oriented error estimation methods, and model order reduction using Proper Generalized Decomposition. Analysis of Dr. PLED's recent publications reveals a strong focus on phase-field modeling of brittle fracture, particularly in wood and other heterogeneous materials. His work increasingly integrates machine learning techniques, especially artificial neural networks, to solve complex statistical inverse problems in computational mechanics and biomechanics. A notable trend in his research is the application of data-driven approaches to characterize material properties and predict failure mechanisms across multiple scales. Dr. PLED is actively involved in the Multiscale Modeling and Simulation Laboratory (MSME) at Gustave Eiffel University, where he collaborates with researchers on projects related to computational mechanics, uncertainty quantification, and multiscale modeling. His work has applications in diverse fields including wood mechanics, biomechanics, and general materials science.

• Artificial Intelligence
• Computer Vision
• Data Science
• +۴ مورد دیگر

Dr. Hongda Tian is a Senior Lecturer at the University of Technology Sydney's Data Science Institute within the Faculty of Engineering and Information Technology. With a strong background in AI and data science, he focuses on translating research into practical solutions for real-world problems across multiple sectors including water, transport, energy, and retail. BSc and MEn from Beijing University of Posts and Telecommunications (2006, 2009) PhD from University of Wollongong, Australia (2015) Postdoctoral Fellow with DATA61 | CSIRO Computer Vision Scientist with Kandao Australia Pty Ltd Associate Research Fellow with University of Wollongong Dr. Tian's research spans artificial intelligence, computer vision, data science, and machine learning with a focus on practical applications. His work combines theoretical advancements with industry implementation, particularly in environmental sustainability, water management, and infrastructure monitoring. He excels at transforming real-world issues into data science problems and developing appropriate solutions that demonstrate industrial applicability to stakeholders. His publication record shows a clear trend toward applying AI to critical infrastructure and environmental challenges, with recent work focusing on water quality prediction, electricity price forecasting, carbon intensity modeling, and bushfire smoke detection. These publications appear in top journals including International Journal of Computer Vision, IEEE Transactions on Image Processing, and IEEE Transactions on Multimedia. 2025 The Australian Financial Review AI Awards (Sustainability category) 2024 NSW iAwards Winner for Sustainability & Environmental Solution 2024 NSW Merit iAwards for Government & Public Sector Solution 2024 Distilling Research Impact 2023 NSW Merit iAwards for Sustainability & Environmental Solution Chinese Government Award for Outstanding Self-financed Students Abroad Dr. Tian has secured approximately $1.07 million in external research funding as Chief Investigator since 2020 and has led or delivered over 15 research innovation projects with government and industry partners. His projects span multiple sectors including water (Dynamic Prediction of Raw Water Quality, Water Quality Prediction for Drinking Water Delivery Systems), transport (Structural Health Monitoring for Sydney Harbor Bridge, Computer Vision-Based Track Defect Detection), energy (Electrical Network-Related Incidents Prediction), and retail (Woolworths Endcap Compliance). He serves on thesis examination committees and as an editorial board member and reviewer for over 20 peer-reviewed journals and conferences.

Nat Tantivasadakarnمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Physics
• Quantum Computing
• Topological Order
• +۴ مورد دیگر

Nat Tantivasadakarn is a Sherman Fairchild Postdoctoral Scholar and Research Associate in Theoretical Physics at the California Institute of Technology (Caltech), affiliated with the Division of Physics, Mathematics and Astronomy. His research focuses on topological phases of matter, quantum computing architectures, and non-Abelian anyon systems. He explores theoretical frameworks to engineer topological order through gauging procedures, symmetry-enriched phases, and measurement-based protocols. Research areas include non-invertible symmetries, fracton models, and fault-tolerant quantum computation leveraging topological codes. He investigates protocols for creating anyons in trapped ion systems and developing scalable quantum error correction strategies. His work bridges abstract algebraic structures (e.g., cohomology invariants) with experimental realizations in quantum hardware. Key contributions involve constructing tensor networks for higher-dimensional topological phases and analyzing Nishimori transitions in quantum circuits. His research also addresses the interplay between measurement-driven dynamics and long-range entanglement, particularly in symmetry-protected topological systems. Tantivasadakarn collaborates with institutions like the Institute for Quantum Information and Matter (IQIM) at Caltech. No scientific awards are explicitly listed. His advising roles and grant details remain unspecified in available texts.

Haiping Luمشاهده پروفایل

استاد

• Machine Learning
• Multimodal AI
• Healthcare AI
• +۷ مورد دیگر

Professor Haiping Lu is the Professor of Machine Learning and Head of AI Research Engineering at the University of Sheffield's School of Computer Science. He leads the UK Open Multimodal AI Network (UKOMAIN), a national initiative funded by EPSRC. His research focuses on translational multimodal AI for healthcare and scientific discovery, including applications in medical imaging, protein engineering, and drug discovery. Lu holds a PhD from the University of Toronto and has received awards such as the Turing Network Development Award and Wellcome Trust Innovator Award. Education: BEng and MEng from Nanyang Technological University (2001-2004), PhD in Electrical and Computer Engineering from the University of Toronto (2008). Research Interests: Multimodal AI foundations, generative AI, domain adaptation, healthcare AI (brain/cardiac imaging, cancer diagnosis), and scientific discovery (protein engineering, drug/materials discovery). He developed the open-source PyKale library embedded in PyTorch. Awards: Listed above, including grant leadership for projects like the £446k Cancer Data Driven Detection (CRUK) and £2M UKOMAIN network. Grants include EPSRC-funded UKOMAIN, Wellcome Trust AI in Health, and Amazon Research Awards. His team collaborates with institutions globally, focusing on AI ethics and clinical translation. Labs/Teams: Leads the KALE Group (Knowledge-Aware Machine Learning) with 15+ current researchers. Collaborations span medicine, bioinformatics, and materials science.

• GPU Architecture
• Hardware Accelerators
• Machine Learning
• +۳ مورد دیگر

José L. Abellán is a Ramón y Cajal Fellow (Tenure-Track Associate Professor) and European R3 researcher at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech research group and holds a Ph.D. in Computer Science from the University of Murcia (2012). His career includes postdoctoral positions at Boston University and previous faculty roles at Universidad Católica de Murcia. Dr. Abellán's research focuses on architectural enhancements for GPU systems and customized accelerators targeting machine learning and fully homomorphic encryption applications. His work spans hardware/software co-design, microarchitectural extensions for privacy-preserving computation, and efficient parallel processing. His extensive publication record demonstrates consistent contributions to GPU architecture, processing-in-memory, hardware acceleration for cryptography, and graph neural networks. Recent work has appeared in top computer architecture venues including MICRO, ASPLOS, and HPCA. Awards and Honors HiPEAC Paper Awards (2019, 2020, 2023, 2024) Best Paper Award at IPDPS 2011 Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Dr. Abellán leads the EcoArTech research team and serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and Frontiers in Electronics. He is a Senior Member of IEEE and active in the HiPEAC European network.

Adam Andersonمشاهده پروفایل

استاد

• Biomedical Imaging
• Magnetic Resonance Imaging (MRI)
• Neuroimaging
• +۳ مورد دیگر

Adam Anderson is a Professor of Biomedical Engineering and Radiology and Radiological Sciences at Vanderbilt University's School of Engineering. His research focuses on developing and applying advanced MRI methods to study tissue structure and function, particularly white matter fiber analysis in the central nervous system. He holds a Ph.D. and M.S. in Physics from Yale University, an M.Phil. from Yale, and a B.A. in Physics & Philosophy from Williams College. His work integrates hardware design, data analysis, and clinical applications in systems neuroscience. Key research interests include diffusion MRI methodologies, high-resolution imaging at 7T, and validation techniques for fiber tractography. His group explores how white matter microstructure and macrostructure relate to neurological conditions like Alzheimer’s and mild traumatic brain injury. Recent efforts emphasize functional connectivity in white matter and BOLD signal modulation in clinical contexts. Publications span innovations in MRI hardware, diffusion-weighted imaging validation, and applications in neurology. Collaborations include the ISMRM Diffusion Study Group for preclinical imaging standards. Current projects address white matter’s role in brain networks and biomarker development for neurodegenerative diseases.

• Machine Vision
• Artificial Intelligence
• Medical Imaging
• +۵ مورد دیگر

Assoc. Prof. Andrew Busch is the Head of the School of Engineering and Built Environment at Griffith University since 2024. With over two decades of academic experience, he specializes in machine vision for agricultural applications , medical imaging , human movement analysis , and marine science engineering . His research has secured over $14M in funding (including $6M from ARC and $7M from industry partners) and spans collaborations across disciplines. Education: PhD, Queensland University of Technology (2004) BEng (Electrical and Electronic), Queensland University of Technology (1998) Bachelor of Information Technology, Queensland University of Technology (1998) Andrew's research bridges computer vision and artificial intelligence with applications in smart farming , medical device development , and marine ecosystem monitoring . Recent works focus on sugarcane quality automation, retinal vessel delineation, and AI-driven energy systems for buildings. His 15 most recent publications (2025-2002) highlight trends in: Agricultural engineering (sugarcane billet analysis, strawberry grading) Medical imaging (retinal vasculature, speech enhancement) Energy systems (smart buildings, digital twins) Education innovation (industry placements, curriculum design) Scientific Awards: 2021: Excellence in Teaching - Group Educational Leadership Award (Highly Commended) Andrew has supervised 13 doctoral students across projects in aquaculture automation, sugarcane AI systems, and medical diagnostics. He chairs conferences like AAEE 2023 and leads programs such as the Bachelor of Advanced Engineering (Honours) . Labs & Teams: He collaborates with the Institute for Integrated and Intelligent Systems (2018-2024) and directs projects funded by Sugar Research Australia , ARC , and DFAT .

• Information Retrieval
• Machine Learning
• Recommendation Systems
• +۴ مورد دیگر

Chenliang Li is a Professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He holds a PhD in Computer Science from the same institution (2013). His research focuses on information retrieval, machine learning, recommendation systems, natural language processing, and social media analysis. He has published extensively in top-tier venues such as SIGIR, CIKM, ACL, and IEEE TKDE. Key contributions include advancements in sequential recommendation systems using diffusion models and transformers, knowledge graph reasoning with GNNs, and applications of pretrained language models in NLP tasks. His work bridges theory and practice, addressing challenges in data-driven decision making and large-scale systems. Recent publications (2023-2025) explore topics like cross-city POI recommendation, bias-agnostic recommender systems, and multimodal vision-language models. He collaborates widely with industry and academia, contributing to open-source projects like ModelScope-Agent.

Liavas Athanasiosمشاهده پروفایل

استاد

• Signal Processing
• Communications
• Information Theory
• +۴ مورد دیگر

Athanasios Liavas is a Professor at the Technical University of Crete (TUC), School of Electrical and Computer Engineering (ECE), where he has served as Department Chair (2009-2011) and Vice Chair (2011-2013). He holds a Diploma (1989) and PhD (1993) in Computer Engineering and Informatics from the University of Patras. His career includes postdoctoral research at the Institut National des Télécommunications (1996-1998) as a Marie Curie Fellow, and academic roles at the University of Ioannina and the University of the Aegean before joining TUC in 2004 as Associate Professor. He has been a Professor since 2009. His research focuses on Signal Processing for Communications , Information Theory , and Telecommunications , with recent emphasis on tensor decomposition techniques for biomedical signal analysis and machine learning applications. He leads the Telecommunications Laboratory and teaches courses such as Digital Communication Systems II and Wireless Communication Systems. He served as an Associate Editor for the IEEE Transactions on Signal Processing (2005-2009) and was a member of the IEEE SP COM Technical Committee (2006-2011). His recent work includes advancements in nonnegative tensor completion, parallel algorithms for large-scale tensor factorization, and generalized canonical correlation analysis for multi-subject fMRI data. These contributions address challenges in high-dimensional data reconstruction and brain imaging signal processing, leveraging stochastic optimization and distributed computing frameworks. Liavas has authored over 80 peer-reviewed articles, with key contributions in IEEE journals and conferences. His research spans theoretical signal processing, algorithm design, and practical implementations for telecommunications and biomedical engineering.

• High-dimensional Probability
• Random Matrices
• Random Graphs
• +۹ مورد دیگر

Nicholas Cook is an Assistant Professor in the Department of Mathematics at Duke University. Currently on leave at the Berkeley SLMath Institute for the program on Probability and Statistics of Discrete Structures (Spring 2025). He will teach Math 690-40: Introduction to Random Matrix Theory in Fall 2025. His research focuses on high-dimensional probability, including random matrices, random graphs, large deviations, and universality phenomena, with connections to graph theory, additive combinatorics, free probability, and mathematical physics. Background: Cook earned his PhD in 2016 from UCLA under Terence Tao. He was a Stein Fellow at Stanford University's Statistics Department and held NSF postdoctoral positions at Stanford and UCLA. His academic journey includes affiliations with Amir Dembo and Jun Yin as postdoctoral sponsors. Research interests span broad areas such as concentration of measure, universality in random structures, and applications in numerical analysis and statistics. His recent work emphasizes large deviation principles for eigenvalues, universality in random matrix ensembles, and combinatorial problems in graph theory. Articles published since 2018 reflect a deep engagement with spectral properties of random structures and their probabilistic behaviors. Teaching and academic service: Currently on research leave, Cook has taught advanced courses in probability and random matrix theory. His academic contributions are supported by NSF grants, though specific grant details are not provided here.

Niklas Muellerمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Information
• Non-Equilibrium Phenomena
• +۵ مورد دیگر

Niklas Mueller is an Assistant Professor at the Center for Quantum Information and Control (CQuIC) at the University of New Mexico. Previously, he held a Research Assistant Professor position at the University of Washington's Department of Physics (2022-2025) within the College of Arts & Sciences. His research bridges nuclear physics and quantum information science, focusing on quantum simulation protocols, entanglement structure analysis, and thermalization dynamics in complex systems. Dr. Mueller earned his PhD from Ruprecht-Karls University Heidelberg (2017) and completed postdoctoral fellowships at the University of Maryland (2020-2022) and Brookhaven National Lab (2017-2020). His research interests include quantum algorithms for lattice gauge theories, topological phases in quantum many-body systems, and developing tomography methods using random measurements. He collaborates with national labs and industry to advance quantum hardware co-design. Key research themes involve non-equilibrium phenomena, quantum chaos, and leveraging entanglement to overcome computational bottlenecks in strongly correlated systems. His work intersects quantum error correction, AMO physics, and condensed matter systems.

• String Theory
• Supersymmetric Field Theory
• Particle Physics
• +۵ مورد دیگر

Rob Leigh is a Professor of Physics at the University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering. His research focuses on theoretical high-energy physics, string theory, and holography. He holds a PhD from the University of Texas at Austin (1991) and has held postdoctoral positions at the University of California, Santa Cruz, and Rutgers University. Leigh's research interests include D-branes, superstring duality, gauge/gravity dualities, and applications of holography to condensed matter systems. His work has contributed to foundational aspects of string theory, including the discovery of D-branes and the Dirac-Born-Infeld action. He has also explored entanglement entropy in Chern-Simons theory and topological phases of matter. Leigh has been recognized with awards such as the APS Fellowship (2007), Arnold O. Beckman Award (2004), and DOE Outstanding Junior Investigator (1997–2000). He teaches advanced courses in quantum mechanics and field theory, consistently earning student excellence ratings.

Shenhao Wangمشاهده پروفایل

استادیار

• Urban Science
• Deep Learning
• Choice Modeling
• +۷ مورد دیگر

Shenhao Wang is an Assistant Professor in Artificial Intelligence at the University of Florida and the Director of the Urban Artificial Intelligence (AI) Lab. His work bridges urban science and computer science to address challenges in resilient urban systems, travel behavior modeling, and generative AI-driven urban design. Education Ph.D. in Computer and Urban Science, Massachusetts Institute of Technology (2020) M.S. in Transportation and City Planning, MIT (2017) B.A. in Economics, Peking University (2014) B.A. in Architecture and Law, Tsinghua University (2012) Research Interests Dr. Wang focuses on three core themes: Resilient and Equitable Urban Systems : Using network theory and deep learning to model city dynamics and design equitable infrastructure. Travel Behavior Analysis : Integrating choice models with deep learning to predict mobility patterns and urban activity decisions. Generative AI in Urban Design : Automating urban planning tasks (e.g., land use, mobility patterns) using diffusion models and multimodal AI. His research has been funded by the Department of Energy (DOE), Singapore-MIT Alliance for Research and Technology (SMART), and industry partners. Grants & Labs He leads the Urban AI Lab at the University of Florida, advancing interdisciplinary projects at the intersection of AI and urban systems. Current grants include DOE-funded studies on energy transition impacts and equitable urban prediction models.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

Gareth Barkerمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Neuroscience
• Psychiatry
• +۳ مورد دیگر

Professor Gareth Barker is a Professor of Magnetic Resonance Physics at King’s College London, affiliated with the Department of Neuroimaging within the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). He leads research in quantitative MRI techniques, including T1/T2 mapping, diffusion tensor imaging, and magnetisation transfer measurements, with applications to neurological and psychiatric disorders. His work focuses on developing MRI protocols for patient populations and advancing near-silent acquisition methods. He chairs doctoral student progress committees and contributes to research ethics governance. Education: PhD in Physics from the University of Nottingham (1986), BSc in Engineering (1982). Career milestones include roles at the University of Florida (1986-1988) and University College London (1988-2002) before joining King’s College London in 2002. He co-leads the Wellcome EPSRC Centre for Medical Engineering, focusing on medical imaging innovation. Research Interests: Neuroscience applications of MRI, neuroimaging technology development, psychiatric disorder diagnostics, and translational neuroimaging. Key projects include the IMAGEN study (adolescent brain development), c-VEDA cohort (child vulnerability), and STRATA schizophrenia research. Scientific Contributions: Over 600 peer-reviewed publications, including seminal work on MRI technique optimisation (e.g., 2006 Brain and Cognition paper on study design). Awards include Fellowship in the International Society for Magnetic Resonance in Medicine. Grants/Teams: Principal Investigator on grants for accelerated MRI in Alzheimer’s, silent MRI for MS, and deep learning in imaging. Collaborates with institutions globally on neuroimaging initiatives.