جست‌وجوی استاد و پژوهشگر

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +۶ مورد دیگر

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .

Alessandro Fogliمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Data Analytics
• Cluster Resource Management
• +۴ مورد دیگر

Alessandro Fogli is a PhD Student at Imperial College London in the Department of Computing, affiliated with the Large-Scale Data & Systems (LSDS) Group . His research focuses on systems support for data analytics in cloud environments, including distributed systems, resource management, and query processing. Education PhD in Computer Science, 2019–Present, Imperial College London MSc in Computer Science, 2015–2017, Roma Tre University BSc in Computer Science, 2012–2015, Roma Tre University His research spans Distributed Systems , Databases , Data Analytics , and Modern Hardware . Recent work examines chiplet-based processor architectures and runtime mapping systems, with applications in performance optimization and hardware-aware query execution. Scientific Contributions Co-developed CHARM (2025), a runtime mapping system for chiplet heterogeneity Published in VLDB (2024) on OLAP processing for chiplet-based CPUs Contributed to HeatWave at Oracle Labs, improving query offloading to in-memory accelerators

Julien FAVIERمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Lattice Boltzmann Method
• +۳ مورد دیگر

Julien FAVIER is a Professor at Aix-Marseille Université, where he directs the M2P2 laboratory and coordinates the H2020 FALCON project on fluid-structure interaction in aeronautics. He also serves as an associate editor for Computers and Fluids . Research Focus: Fluid-structure interaction (FSI), Lattice Boltzmann Method (LBM), Immersed Boundary Method (IBM), turbulent and compressible flows Applications: Biomedical (aortic valves, mucus transport), aerospace (hypersonic flows), and mechanical systems (rupture/fragmentation) Scientific Contributions include: Developing stable explicit FSI solvers for LBM-IBM coupling Modeling metachronal wave dynamics in cilia arrays Advancing compressible LBM with rotating overset grids Studying drag reduction via flexible filament coatings Pioneering non-Newtonian fluid transport simulations Technical Expertise spans: Multi-grid and dual-time stepping techniques GPU acceleration for heterogeneous architectures Viscoelastic and Herschel-Bulkley flow modeling Validation of immersed boundary methods for turbulent flows

• Scientific Software Development
• High Performance Computing
• Molecular Dynamics
• +۳ مورد دیگر

Dr. Torsten Stuehn serves as IT Group Leader at the Max Planck Institute for Polymer Research (MPI-P) in Mainz, Germany, leading scientific software development and HPC infrastructure since joining in 2003. He oversees the ESPResSo++ simulation package and collaborates with the University of Mainz and Max Planck Compute and Data Facility (MPCDF). Education: Diploma in Physics, University of Mainz, 1999 Doctorate in Physics, University of Mainz, 2005 His research focuses on scientific software engineering for exascale computing, developing neural network-based force fields, adaptive resolution methods, and load balancing algorithms to advance molecular simulation capabilities. This work addresses critical challenges in maintaining computational leadership for soft matter physics. Recent publications reveal a clear evolution in ESPResSo++ toward exascale readiness, integrating machine learning with multiscale modeling and parallel computing innovations. The software's progression reflects broader trends in computational physics where AI-driven methods and heterogeneous architecture optimization are becoming indispensable. Stuehn directs MPI-P's computational infrastructure team and contributes to major initiatives including Transregio SFB 146 and the European E-CAM project, driving open-source scientific software development for the global research community.

Reka Howardمشاهده پروفایل

دانشیار

• Genomic Prediction
• Plant Breeding
• Statistical Methods Development
• +۳ مورد دیگر

Reka Howard is an Associate Professor in the Department of Statistics at the University of Nebraska-Lincoln's Institute of Agriculture and Natural Resources (IANR), where she bridges advanced statistical methodology with agricultural innovation through genomic prediction research and graduate education. Education: PhD in Statistics and Plant Breeding, Iowa State University (2016) Research Focus: Dr. Howard pioneers statistical methods for genomic prediction in plant breeding, with emphasis on optimizing prediction accuracy and modeling genotype by environment interactions. Her work integrates environmental data with genomic information to develop robust models for crop improvement across soybean, wheat, and sorghum systems, directly addressing challenges in climate-resilient agriculture through computational innovation and field application. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Methodological advances in genomic prediction including lambda optimization for ridge regression and sparse testing protocols; (2) Integration of environmental features with genomic data for transferable prediction models; and (3) Physiological investigations into nitrogen dynamics and canopy architecture in soybean production systems. Her work increasingly employs artificial intelligence techniques while maintaining rigorous statistical foundations. Teaching & Service: Dr. Howard instructs graduate statistical methods courses for agronomy, animal science, and engineering students, developing curriculum that translates complex methodologies into practical research tools for the next generation of agricultural scientists.

Sara Sollaمشاهده پروفایل

استاد

• Statistical Mechanics
• Neural Networks
• Complex Systems
• +۳ مورد دیگر

Sara Solla is a Professor in the Department of Physics and Astronomy at Northwestern University, with a joint appointment in Physiology. She holds a PhD from the University of Washington (1982). Her research focuses on applying statistical mechanics to complex systems, particularly neural networks, exploring topics like associative memory, supervised learning, and neural dynamics. Solla's work bridges theoretical physics and neuroscience, with contributions to computational models of neural computation and brain-machine interfaces. Research Interests : Solla investigates neural networks through the lens of statistical mechanics, examining how systems like spin-glass models can describe associative memory and generalization in adaptive systems. She has contributed to understanding neural network dynamics, spiking neuron behavior, and the role of heterogeneity in network computations. Her recent work explores low-dimensional neural manifolds for motor control, brain-computer interface development, and neuro-inspired artificial intelligence. Key Contributions : Her research spans theoretical frameworks for neural learning algorithms, applications in motor control decoding, and interdisciplinary approaches combining neuroscience with robotics. She has advanced methods for analyzing multi-electrode neural recordings and contributed to understanding tactile perception and somatosensory processing. Labs & Affiliations : Solla is affiliated with Northwestern's interdisciplinary centers, including the Center for Interdisciplinary Research and Exploration in Astrophysics (CIERA), the Center for Network Dynamics (CND), and the Center for Applied Physics and Superconducting Technologies (CAPST). These collaborations highlight her role in fostering cross-disciplinary research in physics, neuroscience, and engineering.

John Morrisonمشاهده پروفایل

استاد

• Parallel Distributed Computing
• Grid Computing
• Cloud Computing
• +۲ مورد دیگر

Professor John Morrison is the founder and director of the Centre for Unified Computing and co-founder of the Boole Centre for Research in Informatics at University College Cork. With qualifications including BSc, MSc, PhD, and DipTLHE, his research focuses on parallel distributed computing, grid technologies, and cloud architectures. His primary research explores: Self-organizing cloud management systems Heterogeneous computing environments Energy-efficient cloud infrastructure Grid computing middleware Professor Morrison's publications demonstrate strong focus on cloud computing optimization, distributed systems, and virtual reality applications in healthcare and education. Recent work emphasizes scalable resource management and trust systems in cloud environments. Honors include: Senior Member of ACM Senior Member of IEEE He has secured significant research funding including: €883,226 from Horizon 2020 for CloudLightning Project €379,111 from Enterprise Ireland for Cloud Computing Centre €646,604 from Higher Education Authority for Biophotonics Platform He leads the MAVRIC Research Lab focusing on immersive computing technologies and serves on editorial boards for multiple journals including Journal of SuperComputing.

Fatemeh Vafaeeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Systems Biology
• +۵ مورد دیگر

Associate Professor Fatemeh Vafaee is a leading researcher at the University of New South Wales (UNSW) , holding appointments as Associate Professor in the School of Biotechnology and Biomolecular Sciences (BABS) and Deputy Director (Science) of the UNSW AI Institute . She previously served as Deputy Director of the UNSW Data Science Hub (uDASH) and has held academic positions at the University of Toronto and the University of Sydney. PhD in Artificial Intelligence from University of Illinois at Chicago Postdoctoral Fellowships at University of Toronto and University of Sydney Founded the AI-Enhanced Biomedicine Laboratory in 2017 Her research focuses on deploying advanced AI techniques to address biomedical challenges through: Biomarker Discovery for cancer and neurodegenerative diseases Single-Cell Multi-Omics data integration and analysis Computational Drug Repositioning and network pharmacology Multi-Omics Data Fusion and temporal network modeling Recent publications demonstrate expertise in liquid biopsy development , single-cell imaging , and AI-driven cancer diagnostics . Her methodological contributions include novel deep learning architectures for omics data analysis and graph neural networks for drug synergy prediction. Scientific accolades include: Winner, Women in AI Asia-Pacific Health Award (2023) Runner-Up, WAI-APAC Innovator of the Year (2023) Top 10 Women in AI in Asia-Pacific (2023) Australian Bioinformatics and Computational Biology Society Research Excellence Award (2023) She supervises PhD candidates across computational biomedicine and AI in healthcare , with significant grant achievements exceeding $17M in competitive funding, including schemes from ARC Discovery , NHMRC , and Medical Research Future Fund .

Graham Rileyمشاهده پروفایل

مدرس

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +۴ مورد دیگر

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

David H. Albonesiمشاهده پروفایل

استاد

• Computer Architecture
• Power-Efficient Systems
• Reconfigurable Architectures
• +۵ مورد دیگر

David H. Albonesi is a Professor in the Computer Systems Laboratory at Cornell University's School of Electrical and Computer Engineering. His research focuses on power-efficient computer architectures, including reconfigurable systems, accelerator design for deep learning, smart buildings, and silicon nanophotonics interconnects. He has held leadership roles in major conferences like ISCA and MICRO, and serves on editorial boards for IEEE Computer and IEEE Micro. Research interests span adaptive architectures for dynamic power management, sparse matrix/tensor accelerators, and energy-efficient smart building systems. His work bridges hardware-software co-design, emphasizing phase-aware resource allocation and energy minimization. Awards include the IEEE Fellow distinction, NSF CAREER Award, and multiple teaching accolades from Cornell. Over 30 years of industry-academic collaboration has led to innovations in GALS microarchitectures, clustered multi-threaded processors, and thermal-aware scheduling. Key contributions include the CuttleSys reconfigurable multicore framework, MatRaptor sparse matrix accelerator, and foundational work in silicon photonics for on-chip interconnects. Patents cover dynamic core power management and adaptive microprocessor designs.

Mohamad Mroueمشاهده پروفایل

پژوهشگر

• Embedded Systems
• FPGA
• Electronics
• +۵ مورد دیگر

Mohamad Mroue is affiliated with the Laboratory at the Electrical and Electronic Engineering of Paris, focusing on cutting-edge research in Embedded Systems, FPGA, Electronics, IoT, and Wireless Communications. His work emphasizes FPGA-based solutions for power optimization, reconfigurable wireless systems, and low-power wide-area networks (LPWANs). He has contributed to advancements in partial reconfiguration techniques, machine learning integration for hardware monitoring, and secure mobility management protocols for IoT devices. His research spans multiple domains including UWB transceiver design, PAPR reduction in DVB-T2 systems, and innovative sensor networks. Collaborations with colleagues like Jean-Christophe Prevotet and Fabienne Nouvel highlight his interdisciplinary approach to solving complex engineering challenges. Mroue’s publications reflect a strong emphasis on practical implementations and real-world applications of embedded and wireless technologies. Key technical contributions include an ARM-FPGA platform for adaptive wireless communication, automated data generation systems, and energy-efficient solutions for IoT mobility. His work bridges theoretical research with hands-on prototyping, often leveraging FPGA capabilities for high-performance and flexible systems.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

Herman Bruyninckxمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Bayesian Sensor Processing
• +۱۰ مورد دیگر

Herman Bruyninckx is a Professor at the Faculty of Engineering Sciences , KU Leuven , where he also serves as Vice-Chair of the Department of Mechanical Engineering and head of the Robotics, Automation and Mechatronics (RAM) subdivision. His research focuses on integrating formally represented domain knowledge into robotic systems for real-time, self-explanatory, and certifiable control. He advocates for open standards and software engineering practices in robotics, with a career-long emphasis on knowledge-driven robotic systems over data-driven approaches.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +۷ مورد دیگر

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.

Marcus Handteمشاهده پروفایل

پژوهشگر

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +۳ مورد دیگر

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.