جست‌وجوی استاد و پژوهشگر

• Underwater Acoustics
• Fluid-Structure Interaction
• Numerical Modeling
• +۳ مورد دیگر

Dr. Daniel Wilkes is an Adjunct Research Fellow at Curtin University's School of Earth and Planetary Sciences within the Faculty of Science and Engineering. His research focuses on computational acoustics, fluid-structure interaction, and numerical modeling of underwater and environmental noise, particularly in marine engineering contexts. He collaborates with the External Collaborative Research Centre for Marine Science and Technology, contributing to advancements in boundary element method (BEM) techniques and their applications in acoustic simulations. Wilkes' work spans experimental and numerical investigations of sound radiation from marine structures, such as impact-driven piles and offshore wind turbine foundations, with a focus on mitigating environmental noise impacts. He has developed parallel FMBEM (Fast Multipole Boundary Element Method) models for large-scale acoustic problems and contributed to benchmarking studies like COMPILE for marine pile-driving noise prediction. His publications emphasize coupled fluid-structure interaction (FSI) analysis, low-frequency elastodynamic modeling, and the optimization of numerical methods for complex acoustic scenarios. While no formal awards are listed, his extensive publication record reflects significant contributions to acoustic engineering and environmental noise management.

• Astrophysics
• Data Systems
• Galaxy Evolution
• +۴ مورد دیگر

Willem-Jan Vriend is a Researcher at the University of Groningen , affiliated with the Faculty of Science and Engineering and the Astronomy Department . He specializes in astrophysics , data systems , and space mission infrastructure , with a focus on the Euclid mission and Astro-WISE technologies . His research interests include galaxy evolution , dark matter , near-infrared astronomy , and distributed computing for large-scale data processing. He contributes to the development of data transmission systems and infrastructure for space missions like Euclid, collaborating with institutions such as the European Space Agency and International Astronomical Union . His work emphasizes high-resolution imaging , data federation , and software engineering for astronomical data analysis. Publications highlight his role in the Euclid Wide Survey and the design of data distribution systems for space telescopes.

• Evolutionary Algorithms
• Swarm Robotics
• Optimization
• +۳ مورد دیگر

Prof. Ben Paechter is a leading academic at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, specializing in evolutionary algorithms and swarm robotics. As a Professor , he has shaped research directions in computational optimization and adaptive systems for over two decades. Research Themes : Evolutionary Swarm Robotics, Multi-Objective Optimization, Hyper-heuristics, Neural Architecture Search Key Projects : FOCAS (European Commission), KTP: Intelligent Agents (Innovate UK), PerAda (Self-Aware Systems) Collaborations : CAVES Research Group, Centre for Algorithms, Visualisation and Evolving Systems His article trends demonstrate expertise in cross-domain hyper-heuristics, swarm behavior tree evolution, and neural network architecture optimization. Recent works (2023-2024) focus on GPU-accelerated evolutionary algorithms and hierarchical swarm control systems. Scientific Awards : Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) GECCO Best Paper Nomination (2018) Advising & Grants : Supervised 5 PhD students including Paul Lapok (planar mechanisms) and Andreas Steyven (swarm diversity). Secured £2.2M+ in funding across 13 projects, including EU FOCAS (£639,999) and Innovate UK grants.

Yasutaka Furukawaمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۵ مورد دیگر

Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.

• Cardiorespiratory Medicine
• Biomedical Engineering
• Applied Mathematics
• +۵ مورد دیگر

Dr. Chris Cantwell is a Professor in Computational Engineering at the Department of Aeronautics, Imperial College London . He specializes in developing high-performance numerical methods and software tools for fluid dynamics and biomedical applications. His work focuses on spectral/hp element methods, machine learning integration, and open-source frameworks like Nektar++. Education : MMath in Mathematics (2005), University of Warwick MSc in Scientific Computing (2006), University of Warwick PhD in Scientific Computing (2009), University of Warwick Research Interests : High-fidelity simulation of complex flows (aerodynamics, turbomachinery, nuclear fusion) Cardiac electrophysiology modeling and biomedical engineering Machine learning for fluid dynamics and system dynamics Open-source software development (Nektar++ framework) Affiliations : Imperial College Centre for Cardiac Engineering ElectroCardioMaths Programme Quantum Engineering, Science and Technology Nektar++ Project Leadership Labs & Teams : Leading research in fluid dynamics and cardiac engineering at Imperial College Cross-disciplinary collaborations in biomedical and computational sciences

Miguel Molina Solanaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Energy Management
• +۷ مورد دیگر

Dr. Miguel Molina-Solana is an Associate Professor (Prof. Titular) at the Department of Computer Science and AI, Universidad de Granada (Spain) and Honorary Research Fellow at Imperial College London's Data Science Institute. He holds a PhD in Computer Science and AI from Universidad de Granada (2008-2012) and an MSc in Computer Science from the same institution (2002-2007). His academic journey includes roles as Marie Curie Research Fellow (2017-2019 at Imperial College), Research Associate at the Data Science Institute (2015-2017), and postdoctoral researcher at UGR (2012-2015). Research focuses on Artificial Intelligence , Data Science , Energy Management , and Disinformation Analysis . Notable projects include SINERGY (deep learning for energy simulation), IBERIFIER (EU-funded disinformation observatory), and IA4TES (AI applications for sustainable energy transition). He co-leads the SAIL Lab at UGR and has led research groups in visualization and energy systems. Publications span Angewandte Chemie , Energy and Buildings , and Applied Soft Computing , covering topics like physics-informed neural networks, HVAC control optimization, and Telegram-based disinformation analysis. His work bridges machine learning with domain-specific challenges in energy, music informatics, and smart built environments. Outreach activities include science communication projects (e.g., 'The Sound of Mars' art installation) and education initiatives through Native Scientist. Current roles involve advising PhD students on energy and AI projects, with active grants from Spanish and EU programs. Labs/Teams: SAIL Lab (Universidad de Granada), Data Science Institute (Imperial College London collaborations), and interdisciplinary teams in energy and disinformation research.

John Ousterhoutمشاهده پروفایل

استاد

• Distributed Systems
• Operating Systems
• Storage Systems
• +۵ مورد دیگر

John Ousterhout is the Leonard Bosack and Sandy K. Lerner Professor of Engineering and Professor of Computer Science at Stanford University. He also holds a courtesy position in Electrical Engineering. His research focuses on infrastructure for software systems, including distributed systems, operating systems, storage systems, development frameworks, and programming languages. He is renowned for creating the Tcl scripting language and pioneering work in distributed operating systems and file systems. Education: Ousterhout earned a BS in Physics from Yale University (1975) and a PhD in Computer Science from Carnegie Mellon University (1980). He spent 14 years at UC Berkeley before moving to industry, founding Scriptics and Electric Cloud. He returned to academia at Stanford in 2010. Research Interests: Current projects emphasize granular computing—optimizing datacenter task execution at microsecond scales. This includes innovations in thread management, network communication, and logging systems. His past work includes RAMCloud (a low-latency storage system) and foundational contributions to distributed systems and file systems like Sawmill and Zebra. Scientific Awards: He is a member of the National Academy of Engineering and has received prestigious honors such as the ACM Software System Award, ACM Grace Murray Hopper Award, NSF Presidential Young Investigator Award, and U.C. Berkeley’s Distinguished Teaching Award. Industry Impact: As a serial entrepreneur, he co-founded Scriptics (Tcl/Tk) and Electric Cloud. His work on Tcl revolutionized scripting in software development and web frameworks. He has also contributed to AJAX-driven web components and program build architectures.

Sophia Shaoمشاهده پروفایل

دانشیار

• Computer Architecture
• Integrated Circuits
• Domain-Specific Accelerators
• +۵ مورد دیگر

Sophia Shao is an Associate Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She was promoted to Associate Professor with tenure in July 2024 after serving as an Assistant Professor from August 2019 to June 2024. Previously, she worked as a Senior Research Scientist at NVIDIA from October 2018 to July 2019. Her academic journey began with a Bachelor of Electrical Engineering from Zhejiang University, China (2005-2009), followed by a Master of Science (2009-2014) and Ph.D. (2009-2016) in Computer Science from Harvard University. Shao's research focuses on computer architecture, with special emphasis on domain-specific accelerators, heterogeneous architecture, and agile VLSI design methodology. Her work bridges the gap between hardware and software through systematic approaches to accelerator design and evaluation. She leads research efforts in the Agile Design of Efficient Processing Technologies (ADEPT), Berkeley Emerging Technologies Research (BETR), Berkeley Wireless Research Center (BWRC), and SpeciaLIzed Computing Ecosystems (SLICE) centers. Her recent publications reveal a strong trend toward holistic hardware-software co-design for machine learning acceleration, with particular focus on efficient neural network accelerators, multi-tenant execution environments, and automated design methodologies. Her research spans from low-level circuit design to system-level architecture, demonstrating expertise across the entire computing stack. Notably, her work on Gemmini, Stellar, and Virgo represents significant contributions to the field of domain-specific accelerator design and integration. Sloan Research Fellowship (2024) Anita Borg Early Career Award (2024) NSF CAREER Award (2023) Google Research Scholar Award (2023) IEEE TCCA Young Computer Architect Award (2022) Intel Rising Star Faculty Award (2022) ISCA Distinguished Artifact Award (2023, 2024) Best Paper Award at DAC 2021 Professor Shao actively mentors a large group of graduate and undergraduate students, with several former students now at leading technology companies including NVIDIA, Microsoft, and Apple. Her research has been supported by multiple grants from NSF, Google, Intel, and other organizations. She leads several open-source research projects including Gemmini, Stellar, Chipyard, and RoSÉ, which have gained significant traction in both academic and industrial research communities. Her lab, part of the SLICE ecosystem, focuses on creating next-generation computing platforms through vertically integrated hardware-software approaches.

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +۶ مورد دیگر

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +۸ مورد دیگر

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

Yuwen Guمشاهده پروفایل

استادیار

• High-dimensional statistics
• Variable selection
• Model combination
• +۳ مورد دیگر

Yuwen Gu is an Assistant Professor in the Department of Statistics at the University of Connecticut. Their research focuses on high-dimensional statistics, variable selection, model combination, nonparametric methods, causal inference, and optimization techniques. Their recent publications highlight advancements in quantile regression, sparse modeling, and high-dimensional data analysis. Notable trends include applications of kernel methods, distributed computing for large-scale statistics, and regularization techniques for insurance and multi-source data. Yuwen Gu's work also explores interdisciplinary domains, such as bioacoustic signal processing in ecological studies, though their primary contributions remain in statistical methodology and computational efficiency.

Jeffrey D. Blanchardمشاهده پروفایل

استاد

• Applied Harmonic Analysis
• Compressed Sensing
• Matrix Completion
• +۷ مورد دیگر

Jeffrey D. Blanchard is a Professor in the Department of Mathematics and Statistics at Grinnell College. He also holds the endowed Donald L. Wilson Professor of Enterprise and Leadership title and directs the Donald and Winifred Wilson Center for Innovation and Leadership. His academic journey includes a Ph.D. from Washington University in St. Louis (2007), followed by a VIGRE Postdoctoral Fellowship at the University of Utah (2007–2009) and an NSF International Research Fellowship at the University of Edinburgh (2010). His research focuses on applied and computational harmonic analysis, particularly compressed sensing, matrix completion, composite dilation wavelets, and GPU-accelerated scientific computing. These areas intersect with digital signal processing, high-dimensional geometry, and random matrix theory. He has developed efficient algorithms for sparse recovery and large-scale data processing, often leveraging graphical processing units for performance gains. The recent publications reflect a consistent trend in algorithmic development for compressed sensing and sparse approximation, with increasing emphasis on GPU implementations and structured sparsity. His work bridges theoretical analysis with practical computational solutions, often resulting in open-access software tools like GAGA, GGKS, and GGMS. NSF International Research Fellowship (2010) NSF Grants: DMS 1112612, DMS 1620390, OISE 0854991 Harris Faculty Fellowship (2013–2014) MAA Project NExT Fellow (2008–2009) Department of Homeland Security Fellowship (2003–2006) Blanchard has advised numerous undergraduate research projects in areas such as GPU-based k-selection, joint sparsity, and composite dilation wavelets. His research has been supported by multiple National Science Foundation grants focused on algorithm analysis and high-performance computing. He has taught a wide range of courses including Calculus, Numerical Analysis, and Wavelet Applications. He leads a research group that develops GPU-accelerated algorithms for compressed sensing and sparse recovery. His software packages—GAGA, GGKS, and GGMS—are publicly available and used for high-performance computing tasks such as greedy pursuit and order statistics selection on GPUs.

Jeffrey S. Rosenthalمشاهده پروفایل

استاد

• Probability theory
• Stochastic processes
• Markov chain Monte Carlo
• +۴ مورد دیگر

Jeffrey S. Rosenthal is a Professor in the Department of Statistical Sciences at the University of Toronto, Faculty of Arts and Science. He holds a PhD in Mathematics from Harvard University and a BSc from the University of Toronto. PhD, Mathematics, Harvard University BSc, University of Toronto His research centers on probability theory , stochastic processes , and statistical computation , with a particular focus on Markov chain Monte Carlo (MCMC) algorithms . His work spans theoretical foundations and practical applications, including random walks on groups and interdisciplinary modeling. He is also known for his public engagement in statistics through bestselling books and media appearances. The recent publications reflect a sustained focus on the theoretical underpinnings and convergence properties of MCMC methods, including adaptive and non-reversible algorithms. His work also extends into data science applications, such as analyzing streaks in online chess and the long-term impact of the COVID-19 pandemic on mortality. The keywords span probability, statistics, computational mathematics, and machine learning, indicating a blend of theoretical rigor and applied relevance. Scientific Awards and Honors: CRM-SSC Prize in Statistics COPSS Presidents' Award SSC Gold Medal Fellow of the Royal Society of Canada Fellow of the Institute of Mathematical Statistics Alumnus of Influence, University College Pierre Robillard Award SSC Student Research Presentation Award Savage Award Finalist Academic Supervision and Grants: Professor Rosenthal has supervised a large and diverse group of students, including numerous PhD candidates, MSc students, and post-doctoral fellows, many of whom have gone on to successful academic careers. His research is supported by ongoing publications and collaborations, indicating active grant funding and a vibrant research program. He maintains a well-documented research team and provides extensive resources for students and collaborators. Research Teams and Labs: He leads an active research group in probability and computational statistics, with a documented team of current and past post-doctoral fellows, PhD students, and research assistants. The group maintains a collaborative environment, as evidenced by joint publications and team photos, and focuses on advanced topics in MCMC theory and applications.

Maurizio Leottaمشاهده پروفایل

پژوهشگر

• Test Automation
• Empirical Software Engineering
• Requirements Engineering
• +۶ مورد دیگر

Maurizio Leotta is a faculty member at the Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genova, Italy. His research focuses on software testing automation, empirical software engineering, requirements engineering, business process modeling, and model-driven software development. He has contributed to advancing end-to-end web testing methodologies through tools like Assessor, STILE, and SleepReplacer-GPT, emphasizing robust locators, thread sleep replacement, and parallel test execution optimization. Primary Affiliation : DIBRIS, University of Genova Research Themes : Test Automation, Gamification in Software Testing, Web Accessibility, IoT Systems Testing In recent years, Leotta has explored the integration of large language models (LLMs) in automated testing, comparing browser automation tools (Selenium, Cypress, Puppeteer, Playwright), and analyzing job market trends post-pandemic. His work spans both practical tool development (e.g., TESTQUEST, Mutta) and empirical studies evaluating testing approaches. He actively participates in program committees for testing workshops and collaborates on gamification initiatives in software development education. The 15 most recent publications reflect his expertise in test automation frameworks, gamification strategies, and accessibility enhancements. Articles cover topics such as explicit wait management in web testing, LLM applications for image captioning, and comparative studies of automation tools. His research also extends to IoT system testing, runtime verification, and job market analysis using large-scale job advertisement data.