جست‌وجوی استاد و پژوهشگر

Thomas Marrinanمشاهده پروفایل

استادیار

• Computer Graphics
• Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Thomas Marrinan is an Assistant Professor of Computer & Information Science at the University of St. Thomas, College of Arts and Sciences. His expertise spans computer graphics, visualization, human-computer interaction, and high-performance computing. He specializes in developing interactive tools for large-scale data analysis, including multi-platform visualization applications like VisAnywhere and immersive virtual reality experiences. His research focuses on bridging gaps between simulation analysis and user interaction through novel interfaces and algorithms. He has pioneered work in 3D Gaussian splatting, real-time image compression, and collaborative environments using 360 panoramas. Marrinan teaches courses such as Intro to Programming & Problem Solving and Web Development, emphasizing practical application of computational concepts. In 2024, his team won the IEEE Scientific Visualization Contest for VisAnywhere, a tool enabling cross-platform scientific visualization. His work spans over two decades, with contributions to scalable resolution displays (SAGE2), GPU-based rendering optimizations, and AI-driven audio generation for video games. He holds a strong commitment to data-intensive collaboration and immersive technologies. Recent projects include developing omnidirectional stereo imaging techniques for VR exploration of large datasets and leveraging AI to enhance user-generated content in interactive media. His research addresses challenges in distributed rendering, real-time data transmission, and improving accessibility to complex scientific datasets.

• High-Performance Computing
• Parallel Computing
• Linear Algebra
• +۳ مورد دیگر

Jack Dongarra is a Professor and Turing Fellow in Pure Mathematics. He is affiliated with research groups in the Data Science Institute, Software Systems, Numerical Analysis and Scientific Computing, and Applied Mathematics. His work contributes to UN Sustainable Development Goals through research in Digital Futures. Research Interests: High-Performance Computing, Parallel Architectures, Linear Algebra, GPU Computing, and Scientific Software Development. His fingerprint includes topics like Graphics Processing Units, Multicore Systems, and Performance Analysis. Publications (429 total) focus on algorithm design, high-performance computing frameworks, and exascale technologies. Notable contributions include the MAGMA project for GPU/multicore architectures and the development of the heFFTe library for exascale FFT. Awards: Turing Fellow Supervised 1 student. Active in datasets like 'Accelerating scientific computations with mixed precision algorithms' (2009) and impacts such as MAGMA's development.

Eun Jung Kimمشاهده پروفایل

استاد

• Computer Architecture
• Power Efficient Systems
• Parallel/Distributed Systems
• +۳ مورد دیگر

Eun Jung Kim is a Professor in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. Her research focuses on computer architecture, power-efficient systems, parallel/distributed systems, cluster computing, performance evaluation, and fault-tolerant computing. Ph.D.: Computer Science and Engineering, Pennsylvania State University (2003) M.S.: Computer Science and Engineering, Pohang University of Science and Technology (1994) B.S.: Computer Science, Korea Advanced Institute of Science and Technology (1992) Her work explores innovative solutions for optimizing hardware and software systems, including energy-efficient interconnects, security enhancements, and adaptive network-on-chip designs. Notable achievements include the NSF Early CAREER Award (2009) and contributions to publications like IEEE Transactions on Parallel and Distributed Systems . Recent research trends include advancements in near-data processing, secure computing abstractions (e.g., WHISTLE), and mitigating hardware vulnerabilities (e.g., cache timing attacks). She also integrates IoT and low-cost cluster systems for environmental research applications.

Mohamed Zahranمشاهده پروفایل

استاد بالینی

• Parallel computing
• Heterogeneous architectures
• Hardware/Software interaction
• +۷ مورد دیگر

Mohamed Zahran is a Clinical Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2003) and has extensive experience in academia and industry, including roles at CUNY and The George Washington University. His research focuses on computer architecture, heterogeneous systems, parallel computing, and AI-driven hardware optimization. Education: Ph.D., Electrical and Computer Engineering, University of Maryland (2003); M.Sc., Cairo University (1999); B.Sc., Cairo University (1997). Research interests include exascale computing, GPU architectures, cache hierarchy design, and AI support for architecture. He has authored over 40 refereed papers and a book on heterogeneous computing. Awards include ACM Distinguished Speaker (2019–2025) and IEEE Distinguished Contributor (2021). Teaching: Courses on multicore processors, GPUs, parallel computing, and compilers. Advised numerous graduate and undergraduate students, including notable achievements in student research competitions.

• Explainable AI
• Machine Learning
• Software Verification
• +۶ مورد دیگر

Alnis Murtovi is an Assistant Professor at TU Dortmund University's Faculty of Computer Science, Department of Software Engineering. They are a prominent researcher in the fields of explainable AI, machine learning, and software verification, collaborating extensively with Professor Bernhard Steffen and other colleagues at the university. Dr. Murtovi's research focuses on making machine learning models more interpretable and verifiable, particularly through work on decision trees, random forests, and neural networks. Their recent publications demonstrate a strong emphasis on developing practical tools and compilation-based approaches to explain complex models while maintaining performance. The research spans theoretical foundations in formal methods to practical implementations like the Forest GUMP tool for verification and explanation. The publication trend shows a consistent output with increasing focus on explainable AI in recent years, with significant contributions in 2023-2025. Their work bridges the gap between theoretical computer science and practical machine learning applications, particularly in verification techniques for AI models. The research has strong connections to software engineering principles and formal methods. Dr. Murtovi has made substantial contributions to the field through numerous publications in top venues including TACAS, STTT, and various conference proceedings. Their work on Forest GUMP represents a significant practical contribution to the community for model verification and explanation. As part of the research group led by Bernhard Steffen at TU Dortmund, Dr. Murtovi contributes to interdisciplinary projects that combine language-driven engineering approaches with practical software development challenges. Their research has implications for both academic and industrial applications where model interpretability and verification are critical requirements.

Rajat Rainaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Statistical Machine Learning
• +۷ مورد دیگر

Rajat Raina is a Researcher currently affiliated with Facebook, where he focuses on enhancing the relevance of online advertising through machine learning techniques. He holds a Ph.D. in Computer Science from Stanford University, where he conducted research under the guidance of Andrew Y. Ng at the Stanford AI Lab . His academic background includes undergraduate studies at the Indian Institute of Technology (IIT) Kanpur , India. Education: Ph.D. in Computer Science (Stanford University), B.Tech at IIT Kanpur Rajat's research interests center on statistical machine learning , with particular emphasis on unsupervised learning , semi-supervised learning , and transfer learning . He explores these areas in the context of large-scale problems and high-dimensional data, contributing to diverse applications such as computer vision, text processing, natural language processing, web search, speech/music processing, and online advertising. His publication record spans multiple disciplines within machine learning and AI. Notable trends include advancements in GPU-based deep learning , sparse coding algorithms , and self-taught learning . His work frequently bridges theoretical insights with practical implementations, particularly in optimizing computational efficiency for complex models. Rajat has previously collaborated with prestigious institutions and organizations, including Microsoft Research , Google , and the AI Lab at EPFL , during internship experiences. He has also contributed to organizational workflows at the NIPS 2007 Conference as Workflow Master.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.

• Computer Architecture
• Quantum Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Ehsan Atoofian is an Associate Professor in the Department of Electrical & Computer Engineering at Lakehead University, Thunder Bay, Canada. Academic Qualifications: BSc, University of Tehran, Iran MSc, University of Tehran, Iran PhD, University of Victoria Research Interests: Dr. Atoofian’s work spans computer architecture , quantum computing , and parallel programming models , with a focus on GPU-based high-performance computing and machine learning applications. Scientific Awards: Grants & Projects: He has received funding through the NSERC Alliance program, supporting projects in machine learning , forest management , and mining technologies .

• 3D Object Retrieval
• Biometrics
• Shape Analysis
• +۵ مورد دیگر

Theoharis Theoharis is a Professor at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU Trondheim), where he leads research in shape analysis and visual computing. He is a core member of the Visual Computing Lab , focusing on 3D object retrieval, biometrics, and cultural heritage applications. Research Interests : 3D Object Retrieval and Registration Biometric Identification (facial data, somatotype analysis) Shape Segmentation and Cultural Heritage Preservation Medical Imaging and Robotics Notable Projects : PRESIOUS : EU-funded initiative for cultural heritage using visual computing D4FLY : On-the-move biometric verification CHANGE ITN : Monitoring cultural heritage change with 15 PhD theses Publications highlight trends in: 3D shape descriptors (QUICCI, radial intersection count) Deep learning for 3D reconstruction and registration Applications in cultural heritage, medical imaging, and geophysics Education : BSc in Computer Science MSc in Computation DPhil in Computer Graphics and Parallel Processing Advising includes doctoral students like Evdokia Saiti (2023) and Peder Bergebakken Sundt (2024), with contributions to SHREC'16 and 3DOR2022 symposiums. Labs & Collaborations : Visual Computing Lab (NTNU) NTNU's Computing unit European Union FP7-ICT (D3.1 Deliverable)

Maarit Korpi-Laggمشاهده پروفایل

دانشیار

• High-Performance Computing
• Numerical Modelling
• Astroinformatics
• +۳ مورد دیگر

Dr. Maarit Korpi-Lagg is an Associate Professor in the Department of Computer Science at Aalto University's School of Science. She specializes in High-Performance Computing (HPC), numerical modelling, and astroinformatics, with a focus on solar magnetic activity and dynamo theory. Her work bridges computational methods with astrophysical phenomena. External Positions: Corresponding Fellow, Nordic Institute for Theoretical Physics (2020–2025) Independent Max Planck Research Group Leader, Max Planck Institute for Solar System Research (2016–) Research interests include: High-Performance Computing for astrophysical simulations Solar magnetic field dynamics and sunspot analysis GPU-accelerated code development Stellar convection zones and atmospheric modeling Her recent publications highlight advancements in exascale astrophysics data analysis, solar dynamo simulations, and cross-disciplinary applications like epidemic modeling via computational methods. Key collaborations span Europe and global institutions. Scientific Awards: Grand Challenge Award (2018) for galactic dynamo research Nordita Corresponding Fellowship (2020) She leads multiple projects, including ERC UniSDyn and NEOSC, focusing on cosmic magnetism and GPU-based stencil computations. Her work contributes to UN Sustainable Development Goals, particularly quality education and planetary science.

Anastasia Angelopoulouمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +۵ مورد دیگر

Anastasia Angelopoulou is a Senior Lecturer (equivalent to Associate Professor) at the School of Computer Science and Engineering, University of Westminster , since 2005. She co-leads the Health and Social Care Modelling research group , focusing on AI systems for real-time healthcare and sustainable cities. A Fellow of the UK Higher Education Academy (FHEA) and certified STEM Ambassador, she bridges academia and industry with 5+ years of experience in image processing and software development across the UK and Greece. Research Interests: Computer Vision, Sign Language Recognition, Brain-Computer Interfaces, and Machine Learning for healthcare and sustainable systems. Grants: £65,173 from Dunhill Medical Trust for AI-based dementia diagnosis in deaf populations; NVIDIA GPU grant for depth map research; Heritage Lottery Fund grants for storytelling and health projects. Publications: 50+ works in Tier-1 journals (e.g., Neural Networks , Neurocomputing ) and top conferences (ICCV, ECCV, IJCNN). Awards: Elsevier Outstanding Review Status (2016); Westminster Teaching Excellence Team Award (2017). Her work emphasizes ethical AI, self-learning algorithms, and multimodal systems for diverse applications including clinical investigation and cultural heritage.

Petra Hatalovaمشاهده پروفایل

پژوهشگر ارشد

• Astronomy
• Astrophysics
• Planetary Science
• +۳ مورد دیگر

Petra Hatalova is a Research Fellow at the Centre for Planetary Habitability at the University of Oslo. Her research focuses on planetary science, computational astrophysics, and exoplanet research, with particular emphasis on rocky exoplanets around K-dwarf stars and their compositional diversity. Her recent work involves studying planetary formation mechanisms and improving computational tools like the GENGA N-body integrator for simulating stellar and planetary systems. Publications highlight her contributions to understanding how planets of similar size can exhibit vastly different interior structures and geochemical properties. She can be contacted via email at petra.hatalova@geo.uio.no . No awards or students are mentioned in the available records.

• Computer Science
• CUDA
• OpenCL
• +۴ مورد دیگر

Jiri Filipovic is a researcher affiliated with the Faculty of Computer Science, active in the Research Group Scientific Computing. His work focuses on optimizing computational performance through dynamic autotuning techniques in heterogeneous computing environments. Primary Fields: OpenCL, Autotuning, Dynamic Autotuning Secondary Areas: Graphics Processing Unit (GPU) Computing, Performance Portability, Energy Efficiency In 2020, Filipovic co-authored a significant article in Future Generation Computer Systems presenting a benchmark suite for CUDA/OpenCL kernels with dynamic autotuning capabilities. This work reflects his emphasis on maximizing computational efficiency across diverse hardware architectures. The PlumX Metrics indicate 25 citations, 16 Mendeley readers, and 47 social media shares, highlighting the visibility of his research in high-performance computing communities. No specific scientific awards or formal student advisement activities are documented in the available materials.

Ben Moewsمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Statistical Inference
• +۷ مورد دیگر

Ben Moews is a Lecturer in Predictive Analytics at the University of Edinburgh Business School. He serves as Director of the Domain-Driven Machine Learning Lab, PhD Programme in Financial Technology, and Chair of the Centre for Data, Culture and Society's Advisory Board. PhD in Astrophysics (University of Edinburgh) MSc in Artificial Intelligence (University of Edinburgh) Professional Statistician (American Statistical Association) His research bridges artificial intelligence with financial technology and astrophysics , focusing on domain-specific machine learning and privacy-preserving synthetic data . Recent projects include generative modeling for market microstructure , AI-driven cosmological simulations , and geospatial analytics for societal challenges . Research outputs span Physics-informed neural networks (2024), random number generator analysis in financial markets (2024), and synthetic data evaluation frameworks (2024). His work combines high-performance computing with real-world applications in central banking and public health . Fellowship of the Higher Education Academy (2025) McWilliams Fellowship (2021) Principal’s Career Development Scholarship (2017) Professional Statistician accreditation (2023) Winton Thesis Prize (2022) As Director of the Domain-Driven Machine Learning Lab and FinTech PhD Programme, he leads initiatives integrating data science with domain-specific challenges across business, finance, and public policy. He collaborates with the Scottish Centre for Crime and Justice Research and Edinburgh Centre for Financial Innovations.

Kirshanthan Sundararajahمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• High-Performance Computing
• +۳ مورد دیگر

Kirshanthan Sundararajah is an Assistant Professor in the Department of Computer Science at Virginia Tech's College of Engineering. His research focuses on compilers, programming languages, and high-performance computing, with an emphasis on optimizing algorithms for complex data structures like sparse tensors and tree traversals. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Purdue University (2022) and a B.S. in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka (2014). His work spans compiler optimization techniques, parallel computing strategies, and efficient execution models for heterogeneous architectures. Key contributions include frameworks like Orchard for heterogeneous parallelism, SparseAuto for sparse tensor computations, and SABLE for blocked evaluation of sparse matrices. He also explores dynamic symbolic execution and secure multi-party computation through metaprogramming (HACCLE). No scientific awards were explicitly mentioned in the provided text. His advising and grant activities are not detailed here, though his research likely involves collaborative projects in high-performance computing and compiler design. He is affiliated with the Department of Computer Science at Virginia Tech and contributes to academic initiatives in parallel algorithms and computational efficiency.