جست‌وجوی استاد و پژوهشگر

Xiongcai Caiمشاهده پروفایل

دانشیار مدعو

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

• Computer Engineering
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Ning Li is an Associate Professor in Electrical Engineering , with a focus on in-memory computing , phase-change memory , and deep learning acceleration . Their research spans hardware-aware training, analog memory device reliability, and energy-efficient AI systems. Key Research Areas : Phase-change memory, In-memory computing, Neural networks, Analog computing, Memory cell design, Resistance drift mitigation. Recent Publications highlight innovations in: Optimizing phase-change memory devices for low-resistance drift Hardware-aware training to maintain accuracy in analog compute-in-memory systems Energy efficiency improvements via drift-aware mapping of neural network weights Collaborations include institutions in the United States and Singapore, contributing to UN Sustainable Development Goals related to industry innovation and affordable clean energy .

• Machine Learning
• Statistics
• Model-Based Clustering
• +۵ مورد دیگر

Brendan Murphy is a Full Professor in the School of Mathematical Sciences at University College Dublin since 2015, previously holding a Professor role at the same institution (2007-2015) and a Lecturer position at Trinity College Dublin (1999-2007). He is a Principal Investigator at the Insight Centre for Data Analytics and actively works in Machine Learning & Statistics. Research Focus: His work centers on Model-based clustering Mixture models Applications in sports analytics, food science, microbiome studies, and public health Bayesian statistical methods High-dimensional data analysis Article Trends: Murphy's recent publications (2024-2025) emphasize soft clustering techniques, Bayesian mixture models, and their applications across diverse domains including metabolomics, political science, and oceanography. His work addresses challenges in variable selection, robust classification, and multi-omics integration.

Aravind N.R.مشاهده پروفایل

دانشیار

• Algorithms
• Graph Theory
• Combinatorics
• +۵ مورد دیگر

Dr. N.R. Aravind is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. Holding a Ph.D. in Theoretical Computer Science from The Institute of Mathematical Sciences, Chennai, he is affiliated with the College of Engineering. His research spans algorithms, graph theory, combinatorics, and determinantal complexity, with a focus on parameterized algorithms and approximation techniques for NP-hard problems. Education: Ph.D., The Institute of Mathematical Sciences, Chennai (Advisor: Prof. C.R. Subramanian) Postdoctoral research under Prof. Sundar Vishwanathan at IIT Bombay Aravind's research explores structural questions in graph theory, social network modeling, and complexity bounds. His work on parameterized algorithms addresses tractable instances via input parameters, while his studies in graph coloring and forbidden subgraphs contribute to theoretical understanding. Recent publications focus on matching cut problems, happy coloring, and determinantal complexity. His 15 most recent publications (2010-2024) span computational complexity, graph algorithms, coloring problems, and determinantal complexity. These works emphasize parameterized approaches, structural graph theory, and algorithmic bounds for intractable problems. Scientific Awards: Aravind has mentored numerous PhD and MTech students, including Roopam Saxena, Anjeneya Swami Kare, and R.B. Sandeep, now holding academic positions at prestigious institutions. He has taught courses like Algorithms, Probability in Computing, and Cryptology, often collaborating with co-instructors such as Dr. Rakesh Venkat. His office is located in Room CS-408, Indian Institute of Technology Hyderabad.

Gergely Zábrádiمشاهده پروفایل

دانشیار

• Algebraic Number Theory
• Iwasawa Theory
• Elliptic Curves
• +۵ مورد دیگر

Gergely Zábrádi is an Associate Professor and Head of the Department of Algebra and Number Theory at the Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest. He earned his PhD from Trinity College, University of Cambridge, and conducted postdoctoral research at Westfälische Wilhelmsuniversität Münster. His academic focus lies at the intersection of algebraic number theory, Iwasawa theory, and the p-adic Langlands programme. Education: PhD in Mathematics, Trinity College, University of Cambridge (2008) Habilitation, Eötvös Loránd University (2016) Research Interests: Zábrádi's research spans algebraic number theory , particularly noncommutative Iwasawa theory for elliptic curves, and the p-adic Langlands programme . He explores deep connections between Galois representations , automorphic forms , and L-functions , utilizing tools like (φ,Γ)-modules and perfectoid spaces . Publications Overview: His recent work includes studies on generalized Montréal functors , matrix Kloosterman sums , and sup-norm problems for automorphic forms. These publications reflect a consistent engagement with foundational questions in arithmetic geometry and representation theory. Advising and Grants: Zábrádi has supervised numerous PhD, Master's, and Bachelor's theses, guiding students through topics ranging from Iwasawa theory to p-adic Galois representations . His mentorship has produced award-winning research, including first-prize recognition at the Hungarian Student Research Competition (OTDK). Research Groups and Labs: He leads a vibrant research group at ELTE, fostering collaboration with international scholars and participating in networks like the CENTRAL programme. His team focuses on advancing the p-adic Langlands correspondence and related arithmetic questions.

Bastien Berretمشاهده پروفایل

استاد

• Neuroscience
• Biomechanics
• Robotics
• +۵ مورد دیگر

Bastien Berret is a Full Professor at the University of Paris-Saclay, where he leads the MHAPS research team within the Faculty of Sports Sciences. His academic career has progressed from Assistant Professor at Univ. Paris-Sud (2012-present) to Associate Professor with Habilitation à Diriger des Recherches (HDR) in 2017, and finally to Full Professor in September 2020. His research focuses on understanding how the central nervous system controls voluntary movements through a multidisciplinary approach combining neuroscience, biomechanics, and robotics. From a fundamental perspective, he identifies computational principles to explain human behavior in various motor tasks, with practical applications in human-exoskeleton interactions. His work employs computational tools including stochastic optimal control, numerical optimization, and machine learning methods, alongside experimental tools such as motion capture devices, force platforms, and electromyography systems. Berret's publication record shows consistent output since 2008, with particular productivity in recent years (2020-2023). His research trends indicate a strong focus on motor control theory, gravity effects on movement, human-exoskeleton interaction, and computational modeling of neural processes. Recent publications demonstrate increasing interdisciplinary collaboration across neuroscience, robotics, and biomechanics. Notable scientific achievements: Junior member of the prestigious Institut Universitaire de France (IUF) from 2017-2022 Coordinator of the ANR project "EXOMAN" (2020-2024) Extensive publication record in high-impact journals including Science Advances, PLoS Computational Biology, and Journal of Neurophysiology Berret maintains active research collaborations with institutions including INSERM, Istituto Italiano di Tecnologia, and various neuroscience laboratories. His work on stochastic optimal control and muscle activity decomposition has contributed significantly to computational neuroscience methodology. As team leader of MHAPS, he directs research on motor control principles and their applications in rehabilitation and robotics.

• Plasma Technology
• Photovoltaic Systems
• Renewable Energy Storage
• +۴ مورد دیگر

Prof. Christoph Ellert is a Professor in Physics and holds the academic rank of Professeur HES Ordinaire at HES-SO Valais-Wallis. His primary affiliation is with the School of Engineering and IT (Technique et IT), Department of Energy and Environmental Technologies (Energie et techniques environnementales). Education & Early Career: PhD in Physics (1995, University of Freiburg, Germany) focusing on optical spectroscopy of metal clusters. Postdoctoral research at the National Research Council (Ottawa, Canada) and CEA Grenoble/Paris (France), specializing in laser-matter interactions and plasma physics. Research Interests: Plasma technology for energy and environmental applications (e.g., waste conversion, surface treatment) Renewable energy systems: Photovoltaic integration, DC microgrids, hydrogen storage Material science: Plasma deposition of thin films (e.g., hematite, functional coatings) Energy management: Grid stability, peak shaving, and electrolysis for green hydrogen Key Projects: Plastic-to-hydrocarbon conversion via hydrogen plasma (2022–2024) DC-microgrid development for industrial energy autonomy (2017–2019) Cold atmospheric plasma for food decontamination (2014–2015) Solar energy potential assessment in urban environments (Geneva solar cadaster) Grants & Funding: Over CHF 2.5M in grants from Innosuisse, HES-SO Rectorate, and industry partners. Projects emphasize applied R&D in energy systems, plasma engineering, and sustainable technologies. Labs & Teams: Leads the Energy Systems Group at HES-SO Valais-Wallis, collaborating with interdisciplinary teams across engineering, materials science, and environmental technology. Key partners include EPFL, Oerlikon Solar, and local industry.

Luca Vizioliمشاهده پروفایل

دانشیار

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

Luca Vizioli is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His work focuses on advancing neuroimaging techniques, particularly at ultrahigh magnetic fields (e.g., 10.5T), to enhance spatial and temporal resolution in functional MRI (fMRI). Research interests include RF coil design, noise reduction algorithms (e.g., NORDIC), and applications of deep learning to imaging challenges. Education details are not explicitly stated, though his PhD is noted. His research spans cutting-edge topics like laminar fMRI at UHF, denoising strategies for submillimeter resolution, and the impact of psychedelic substances on brain networks. He collaborates on tools like fMRIPrep Lifespan for developmental neuroimaging preprocessing. Key technical innovations include high-Quality 0.5mm isotropic fMRI leveraging random matrix theory and physics-driven AI, as well as multi-echo distortion correction frameworks. His work bridges engineering (coil design) and neuroscience (cortical processing mechanisms), with implications for understanding both normal brain function and neurological disorders.

Patrizia Saviمشاهده پروفایل

دانشیار

• GNSS Reflectometry
• Microwave Characterization of Materials
• Graphene Nanoplatelets
• +۳ مورد دیگر

Patrizia Savi is a Tenured Associate Professor at the Polytechnic University of Turin within the Department of Electronics and Telecommunications. She actively participates in the Power Electronics Innovation Center (PEIC) and serves as a Senior Member of IEEE and the International Union of Radio Science . Teaching: She has been the Titolare del corso for 'Campi Elettromagnetici' (Electromagnetic Fields) since 2019-2021 at the Polytechnic University of Turin. Key Collaborations: Works with researchers across Italy and international institutions on projects involving GNSS-R for environmental monitoring. Research Interests: Her work focuses on GNSS Reflectometry for soil moisture retrieval, carbon-based composites (graphene, biochar, nanotubes) for microwave applications, and graphene tunable devices including biosensors. She also explores electromagnetic shielding using sustainable materials. Recent Publication Trends: Recent work emphasizes machine learning integration with GNSS-R data, biochar composite shielding in construction materials, and graphene-based biosensors for glucose and HRP detection. Key applications span climate action , environmental monitoring , and medical diagnostics . Scientific Awards: IEEE Fellow (2016-) IEEE Senior Member (2016-) International Union of Radio Science Senior Member (2024-) Advising: Supervises PhD students Simone Gaetano Ballaera and Fabio Peinetti , focusing on graphene sensors and tunable devices.

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +۳ مورد دیگر

Ingemar Johansson Cox serves as a Professor within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His research bridges theoretical machine learning foundations with practical applications across medical data analysis, information retrieval, remote sensing, and sustainability initiatives. His research portfolio emphasizes machine learning applications in high-impact domains, particularly medical data analysis (e.g., early detection of gynecological malignancy using online search activity) and sustainability (e.g., reducing AI's carbon footprint). The Machine Learning section actively contributes to the university's SCIENCE AI Centre, focusing on both algorithmic innovation and real-world problem-solving in biological modeling and environmental monitoring. Recent publication trends reveal expanding work in quantum computing applications for biomolecular modeling, sustainable AI frameworks, and cross-cultural NLP systems. His 2024-2025 output demonstrates strong interdisciplinary collaboration, especially in medical informatics and climate-related AI research. Professor Cox operates within the Department of Computer Science's robust research ecosystem, which includes dedicated compute clusters and specialized initiatives like TreeSense for global tree resource monitoring through remote sensing and deep learning. The department's infrastructure supports large-scale machine learning projects requiring significant computational resources.

George Mannمشاهده پروفایل

استاد

• Intelligent Control
• Robotics
• Machine Vision
• +۶ مورد دیگر

George Mann is a Professor at Memorial University of Newfoundland's Faculty of Engineering and Applied Science. He holds a B.Sc. in Mechanical Engineering from the University of Moratuwa (Sri Lanka), an M.Sc. in Computer Integrated Manufacturing from Loughborough University (UK), and a Ph.D. in Intelligent Control from Memorial University. His research focuses on intelligent control systems, robotics, and machine vision, with significant contributions to autonomous navigation, sensor fusion, and UAV technologies. He leads the Intelligent Systems Laboratory (ISLAB) and has pioneered work on LiDAR-assisted radar enhancement, multi-robot localization, and exoskeleton control systems. **Education**: B.Sc., Mechanical Engineering, University of Moratuwa (Sri Lanka) M.Sc., Computer Integrated Manufacturing, Loughborough University (UK) Ph.D., Intelligent Control, Memorial University (Canada) **Research Interests**: Dr. Mann’s work spans autonomous systems, UAV navigation, sensor fusion (e.g., LiDAR, radar, INS), and robotics applications in mining, healthcare, and environmental monitoring. His recent projects include developing computationally efficient NMPC algorithms for UAVs and creating robust localization systems for heterogeneous multi-robot networks. **Publications**: His articles emphasize advancements in navigation algorithms, disturbance estimation for multi-rotor stability, and AI-driven sensor integration. Key themes include improving system robustness under uncertainty and optimizing computational efficiency in real-time control. **Labs & Teams**: Director of ISLAB, collaborating on exoskeleton development (e.g., Anthro-X) and UAV plume tracking for environmental sensing. Active in industry partnerships like C-CORE for mining automation.

Anders Lindquistمشاهده پروفایل

استاد

• Mathematical systems theory
• Control theory
• Stochastic realization theory
• +۳ مورد دیگر

Anders Lindquist is Zhiyuan Chair Professor at Shanghai Jiao Tong University and Emeritus Professor at KTH Royal Institute of Technology. He earned his PhD from KTH in 1972 and began his career as a postdoctoral fellow at the University of Florida under R.E. Kalman. His academic journey includes positions as Assistant Professor (University of Florida), Associate Professor (University of Kentucky and Brown University), and Full Professor (University of Kentucky and KTH). At KTH, he served as Head of Mathematics Department (2000-2009) and Director of the Center for Industrial and Applied Mathematics (2006-2016). His research focuses on: Mathematical systems theory and control theory Stochastic realization and estimation Spectral estimation methods Moment problems with complexity constraints Applications of operator theory His publications demonstrate consistent focus on mathematical foundations of control systems, stochastic processes, and optimization techniques, with recent work expanding into multidimensional applications and image processing. Major scientific honors include: IEEE Control Systems Award (2020) Reid Prize in Mathematics (2009) Axelby Outstanding Paper Award (2003) Fellowships: IEEE, SIAM, IFAC Memberships: Royal Swedish Academy of Engineering Sciences, Chinese Academy of Sciences He holds four U.S. patents and has served on editorial boards of leading journals including Philosophical Transactions of the Royal Society and SIAM Review.

• Computer Architecture
• Parallel Processing
• FPGA Design
• +۳ مورد دیگر

Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.

• Edge Computing
• Cloud Computing
• Artificial Intelligence
• +۷ مورد دیگر

Hadi Tabatabaee is an Assistant Professor at the School of Computer Science, University College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). His research focuses on sustainable orchestration of services across edge-cloud environments, emphasizing energy efficiency, carbon-aware systems, and AI-driven applications like large language models (LLMs). Key roles include Associate Editor for IEEE Access and Management Committee member of COST Action CA22151 (CYPHER). He holds a PhD in Computer Engineering from the University of Isfahan and has held academic positions at Maynooth University, Shahid Beheshti University, and Trinity College Dublin's CONNECT research program. Education: PhD (Computer Engineering, University of Isfahan), MSc (Computer Engineering), with a research visit at TU Delft (2010-2011). Certifications include Epigeum's Research Leadership and Research Integrity courses. Languages: Persian (fluent), Azerbaijani (spoken). Research Interests: Edge-cloud continuum, dynamic service placement, distributed AI workloads, LLM optimization, and sustainable resource management. Recent work includes zero-trust vehicular networks, parallel algorithms for recommender systems, and geospatial event processing. Awards: None explicitly listed, though his contributions include over 20 journal articles in IEEE/Elsevier/Springer venues. Professional Activities: IEEE Senior Member, TPC member for IEEE conferences, and reviewer for multiple journals. Teaching: Coordinates/teaches Cloud Computing, Computer Networks, and Principles of Computer Organization at UCD.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +۳ مورد دیگر

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.