جست‌وجوی استاد و پژوهشگر

Tyler Sorensenمشاهده پروفایل

استادیار

• Concurrency
• Heterogeneous Systems
• Compilers
• +۴ مورد دیگر

Tyler Sorensen is an Assistant Professor at the University of California, Santa Cruz in the Department of Computer Science and Engineering. He is currently on leave working with the RiSE group at Microsoft Research . His research focuses on concurrency programming, heterogeneous systems (GPUs, accelerators), compilers , and memory consistency models . PhD in Computer Science, Imperial College London (2018) MS in Computer Science, University of Utah (2014) BSc in Computer Science, University of Utah (2012) His work explores programming models for correctness and efficiency on emerging architectures, particularly GPGPU programming. He contributes to standards evolution with the Khronos Group and has developed testing frameworks for GPU memory consistency. His research has significant implications for parallel programming and GPU architecture design . His recent publications analyze GPU memory behavior, concurrency models, and performance portability across different architectures. Key trends include formal verification of memory models, empirical testing frameworks, and performance optimization for heterogeneous systems. Scientific Awards & Recognitions: ISSTA'23 Distinguished Artifact Award ASPLOS'23 Distinguished Paper & Artifact Awards IISWC'19 Best Paper Award PLDI'18 Distinguished Paper Award FSE'17 Distinguished Paper Award ISPASS'20 Best Paper Nomination Tyler advises a diverse group of students working on GPU programming, memory models, and heterogeneous systems. He has served on numerous program committees including ASPLOS 2024, PLDI 2024, and IWOCL 2019, and has been program co-chair for PLDI 2021 and 2022 Student Research Competition.

• Information visualization
• Taxonomic visualization
• Genomic data visualization
• +۵ مورد دیگر

Martin Graham is a Researcher at Edinburgh Napier University, affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems. With a PhD completed between 1998-2002 under Prof Jessie Kennedy, his career spans over two decades of research in information visualization. His research interests focus on information visualization with specialization in taxonomic visualization, genomic data representation, cybersecurity visualization, and techniques for visualizing multiple overlapping classification hierarchies. Graham has consistently applied visualization techniques to help domain experts explore complex data across biology, genomics, and security domains. His work bridges theoretical visualization concepts with practical applications through numerous Knowledge Transfer Partnership projects. Graham's publication record shows an evolution from foundational work on taxonomic visualization to diverse applications in genomics (VIPER, Helium), cybersecurity (Pianola, Security Visualisation), and biodiversity (Vesper). His recent work continues to advance visualization techniques for complex data analysis problems, with his latest publication in 2021 focusing on insider threat detection systems. As a supervisor, he has mentored PhD students including Alan Melville for the project 'An investigation into visual graph comparison' (2007-2014), serving as Second Supervisor. His research has been supported by significant funding including Innovate UK (£184,993 for Security Visualisation 2018-2022), Biotechnology and Biological Sciences Research Council (£104,072 for VESpeR 2012-2014), and Scottish Funding Council projects totaling over £20,000. Graham has collaborated extensively with Prof Jessie Kennedy throughout his career, co-authoring numerous publications and projects. His work demonstrates a consistent thread of developing practical visualization tools that address real-world data exploration challenges across multiple scientific domains.

• Information Visualization
• Data Visualization
• Graph Visualization
• +۵ مورد دیگر

Professor Jessie Kennedy is an Enhanced Associate at Edinburgh Napier University's School of Computing Engineering and the Built Environment, specializing in information visualization and human-computer interaction. She is a prominent member of the Centre for Algorithms, Visualisation and Evolving Systems, with extensive contributions to visualization theory and practice across multiple domains. Her research interests focus on Information Visualization , Network Visualization , Biological Visualization , and Argument Visualization . Professor Kennedy has developed innovative visualization techniques for complex data structures including multiple overlapping classification hierarchies, dynamic networks, and Bayesian networks. Her work bridges theoretical foundations with practical applications in cybersecurity, biology, and education. Professor Kennedy's publications reveal consistent contributions to visualization theory and applications over two decades. Her recent work includes visualization for insider threat detection, argument exploration techniques, and embodied dance learning systems through the CAROUSEL+ project. Her research demonstrates an evolution from foundational visualization techniques to complex applications in diverse domains including biological data, cybersecurity, and virtual reality environments. Professor Kennedy has been instrumental in securing significant research funding from organizations including the European Commission and Innovate UK, with projects totaling over £1.3 million. Her Scottish Woodlands KTP project focused on unified access to forest management data, while the CAROUSEL+ project (funded by Horizon 2020) explores how AR and AI technologies can enhance social interaction in virtual and hybrid events. She leads the Centre for Algorithms, Visualisation and Evolving Systems research group which has produced over 78 outputs. Her laboratory work focuses on developing practical visualization tools that address real-world challenges in data exploration and understanding, with particular emphasis on biological data visualization, network analysis, and educational applications.

• Neuroimaging
• Reproducible Research
• Computational Neuroscience
• +۳ مورد دیگر

Dorota Jarecka is a Research Scientist at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology (MIT). Her work focuses on developing open-source software tools and frameworks to enhance reproducibility and scalability in neuroimaging research. She is a core contributor to initiatives like BIDS Apps, NiMARE, and Pydra, which aim to standardize and streamline neuroimaging data analysis workflows. Her research interests span neuroinformatics, reproducible research practices, computational neuroscience, and the development of scalable data management solutions. She actively contributes to consortia such as the NMIND consortium and the BRAIN Initiative Cell Census Network, promoting collaborative approaches to neuroimaging challenges. Jarecka’s publications highlight her expertise in large-scale neuroimaging analysis, meta-analysis techniques, and the integration of open science tools like DataLad and Datalad. She emphasizes the importance of human-in-the-loop systems and agentic frameworks (e.g., STRUCTSENSE) to improve structured information extraction in scientific workflows. Her work has advanced reproducibility through ontologies and provenance tracking (e.g., NIDM Experiment) and has addressed technical challenges such as software variability across operating systems. She is also involved in educational efforts, including Software Carpentry workshops on version control with Git. Jarecka’s contributions bridge computational methods with neuroscience, fostering collaboration between researchers, developers, and institutions to tackle complex neuroimaging and atmospheric science problems.

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +۶ مورد دیگر

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

Jan S. Hesthavenمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۶ مورد دیگر

Jan S. Hesthaven is a Professor and Provost at EPFL, leading academic affairs. He holds a Master's from the Technical University of Denmark (DTU) and a PhD in Numerical Analysis, followed by an honorary dr.techn degree from DTU. His research focuses on high-order computational methods for wave problems, reduced order models, and machine learning integration. He has co-authored over 175 papers and 4 monographs. Previously, he served as Dean of the School of Basic Sciences at EPFL and held roles at Brown University, including Director of the Center for Computation and Visualization. Awards include the Alfred P. Sloan Fellowship and the Philip J. Bray Award. Education: Master of Science in Computational Physics, DTU (1991) PhD in Numerical Analysis, DTU (1995) dr.techn in Computational Mathematics, DTU (2009) Research Interests: Development of high-order numerical methods, computational wave propagation, geophysical flows, and machine learning applications in scientific computing. His work bridges traditional methods with AI-driven approaches for real-time modeling and structural health monitoring. Recent Work: His 2023–2025 publications emphasize machine learning-enhanced models, reduced order methods, and seismic data analysis for environmental applications. Key techniques include physics-informed neural networks and graph-based operator learning. Awards: Alfred P. Sloan Fellowship (2000) NSF Career Award (2002) Philip J. Bray Award (2004) Dr.techn from DTU (2009) Grants & Leadership: Led the Center for Computation and Visualization (CCV) at Brown (2006–2013) and co-directed the NSF Institute ICERM (2010–2013). Current roles include Provost at EPFL and leadership in MATHICSE. Collaborates with industry and applied scientists on computational challenges. Labs & Teams: Active in the MATHICSE lab, focusing on numerical methods and high-performance computing. Involved in interdisciplinary projects combining AI with traditional computational science.

• Approximation Algorithms
• Network Design Problems
• Operations Research
• +۳ مورد دیگر

Barbara M. Anthony is the Lord Chair and Professor of Computer Science at Southwestern University. She holds a PhD from Carnegie Mellon University (2008) and a BA from Rice University (2001). Her expertise spans Approximation Algorithms, Network Design, Operations Research, Graph Theory, and Algorithmic Game Theory. She teaches courses ranging from introductory programming to advanced topics like Theory of Computation and supervises academic internships. Dr. Anthony is actively involved in student research mentorship, leading projects such as algorithm development for course scheduling, transportation optimization, and educational tools like the Surad.io streaming platform. She has secured grants including an NSF S-STEM Award ($614k) and a Google CS Engagement Award ($5k). Notable awards include recognition as an ACM Senior Member and contributions to diversity initiatives like NCWIT Extension Services. She advises UPE (Computer Science Honor Society) and collaborates with nonprofits like Faith in Action Georgetown. Research highlights include work on Doodle Poll voting equilibria (AAMAS 2018), Dial-a-Ride optimization (ATMOS 2019), and bottleneck matching algorithms (COCOA 2022). Over 30 students have conducted research under her guidance, many presenting at conferences like Texas Academy of Sciences and SIGCSE. Her pedagogical innovations include integrating real-world datasets (e.g., UCSC Genome Browser) into coursework and promoting parallel computing concepts in first-year seminars. Affiliations: Department of Computer Science, Southwestern University Grants: NSF S-STEM, Google CS Engagement, NCWIT Labs/Teams: Collaborations with local nonprofits (Faith in Action), tech startups (Surad.io), and interdisciplinary student projects Future Work: Expanding AI ethics education, optimizing university timetabling systems, and fostering diversity in computing

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

Hongbo Liu is a researcher at Indiana University - Purdue University Indianapolis , Department of Computer Information and Graphics. With a focus on Artificial Intelligence, Machine Learning, and Network Analysis , Liu has contributed extensively to computational intelligence through 118+ publications since 2004. Multi-disciplinary research spanning Graph Theory, Swarm Intelligence, and Deep Learning Recent work includes Robust Gated Models for Temporal Networks and Self-Adaptive Neuroevolution Systems (2024-2025) Key research themes include: Dynamic network analysis and link prediction Crowd behavior modeling and trajectory forecasting Swarm-based optimization for complex systems Fuzzy logic and granular computing applications Neural network architectures for image and text processing Liu's publications demonstrate strong collaborations with researchers like Ajith Abraham, Yu Yang, and Bo Zhang across 15+ academic journals and conferences . The work spans from theoretical graph algorithms (2015-2017) to applied systems in autonomous robotics and blockchain (2024).

Günter Lastمشاهده پروفایل

استاد

• Stochastic Processes
• Stochastic Geometry
• Poisson Processes
• +۶ مورد دیگر

Prof. Dr. Günter Last is a Professor at the Institute of Stochastics within the Faculty of Mathematics at the Karlsruhe Institute of Technology (KIT) . He has held this position since 2000 (C4/W3 Professorship). Last's research focuses on stochastic processes , stochastic geometry , and their applications in mathematics, physics, and finance. Education: Diploma in Mathematics, Humboldt University Berlin (1984) PhD in Mathematics, Humboldt University Berlin (1987) Dr. Sc. in Mathematics, Technical University Braunschweig (1995) Research Interests: Last leads research in Poisson processes, Boolean models, Gibbs processes, and spatial random systems. His work explores Palm calculus, hyperuniformity, normal approximation, and percolation phenomena. He co-authored the textbook Lectures on the Poisson Process with Mathew Penrose. Publications & Trends: His recent work includes Normal approximation of Kabanov-Skorohod integrals , Hyperuniform stable matchings , and Poisson hulls , reflecting a focus on stochastic analysis and geometric probability. His research bridges theoretical insights with applications in digital microstructures and wireless network modeling. Scientific Leadership: Co-Editor for Electronic Journal of Probability (2018-2023) Speaker of DFG Research Unit Geometry and Physics of Spatial Random Systems (2011-2018) Associate Editor for Applied Probability Journals (2005-2019) Advising: Last has supervised 15 doctoral theses and numerous diploma/master's theses, including topics on neural networks, random tessellations, and stochastic financial models.

Daniel Büscherمشاهده پروفایل

پژوهشگر

• Autonomous Robot Navigation
• Deep Learning
• Computer Vision
• +۱ مورد دیگر

Daniel Büscher is a post-doctoral researcher at the Robot Learning Lab of the University of Freiburg. His work focuses on autonomous robot navigation , deep learning , and probabilistic state estimation . He has contributed to robotics research through projects involving mobile manipulation and audio-visual navigation . Research Interests : Autonomous navigation, deep learning, computer vision, probabilistic estimation Teaching : Lecturer and tutor for Introduction to Mobile Robotics and Robot Mapping (2017–2023) Publication Trends : Recent work emphasizes robot design optimization , language-grounded scene graphs , and uncertainty-aware perception . His research connects reinforcement learning with mobile manipulation and cross-domain navigation challenges.

Guillaume Ducoffeمشاهده پروفایل

دانشیار

• Algorithmic Graph Theory
• Computation in Large Graphs
• Parameterized Algorithms
• +۱۲ مورد دیگر

Guillaume Ducoffe is an Associate Professor at the Faculty of Mathematics and Informatics, University of Bucharest, Romania, and a Senior Research Scientist at the National Institute of Research and Development in Informatics (I.C.I.), Romania. He is also affiliated with a joint research team between ICI and the Research Institute of the University of Bucharest (I.C.U.B.). Previously, he was a PhD student at Université Côte d'Azur, France, under the supervision of David Coudert, within the COATI project-team at Inria Sophia Antipolis. PhD, Université Côte d'Azur, France (2016) Master's Thesis, MPRI-ENS Cachan (2013) His research centers on algorithmic graph theory , with emphasis on computation in large graphs , including parameterized algorithms for problems like Diameter and Maximum Matching. He investigates metric tree-likeness in real-life networks through Gromov hyperbolicity, which has implications for routing efficiency and congestion. His work extends to information propagation using game-theoretic models such as coloring and hedonic games, and to online targeting detection , where he develops theoretically sound algorithms to uncover sensitive attribute targeting on the web via reductions to PAC learning of k-juntas. He also explores combinatorial topics like proper connectivity and Randic indices with applications in cryptography and chemistry. The analysis of his recent publications reveals a strong trend in theoretical computer science , particularly in the design and complexity of graph algorithms, structural graph properties, and their applications in network science and privacy. His work bridges pure graph theory with practical concerns in data centers, web transparency, and social networks. Guillaume Ducoffe has made significant contributions to both journal and conference literature, publishing in venues such as Discrete Applied Mathematics , SIAM Journal on Discrete Mathematics , ACM SIGMETRICS , and USENIX Security . His research is highly interdisciplinary, combining insights from algorithms, game theory, and network analysis. He actively supervises and mentors students, though specific names are not listed in the provided materials. He has been involved in research grants, including a postdoc grant from I.C.U.B., and has collaborated with prominent researchers such as David Coudert, Nicolas Nisse, Augustin Chaintreau, and Roxana Geambasu. His teaching includes core courses such as Data Structures and Algorithms , Advanced Graph Algorithms , and Advanced Programming Techniques at the University of Bucharest. Guillaume Ducoffe is a key member of collaborative research initiatives, including the joint ICI-I.C.U.B. team and the former COATI project at Inria. His work continues to advance the theoretical foundations of graph algorithms while addressing pressing issues in web transparency and network design.

Ilias Tachmazidisمشاهده پروفایل

مدرس ارشد

• Knowledge Representation and Reasoning
• Semantic Web
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Ilias Tachmazidis is a Senior Lecturer in the Department of Computer Science at the University of Huddersfield's School of Computing and Engineering. He actively contributes to research within the Centre for Autonomous and Intelligent Systems and the Centre of Artificial Intelligence for Mental Health. His academic journey includes a PhD from the University of Huddersfield (2015) and previous research roles in the PARK group. Education: University of Crete (B.Sc. 2010, M.Sc. 2012) Current academic rank: Senior Lecturer Active research centers: Centre for Autonomous and Intelligent Systems, Centre of Artificial Intelligence for Mental Health His research spans foundational AI concepts and applied implementations, with specific emphasis on: Knowledge Representation and Reasoning Semantic Web Technologies High Performance and Distributed Computing Artificial Intelligence Applications in Medicine and Law Recent publications highlight his work at the intersection of large language models and formal verification, alongside practical implementations for ADHD diagnosis using AI. The research demonstrates consistent engagement with UN Sustainable Development Goals through technology solutions that balance scientific rigor with societal impact. While not explicitly listing scientific awards, his activities show substantial contributions through peer-review work at IEEE conferences (2016-2020) and collaborative projects like MIREL (MIning and REasoning with Legal texts) that produced notable outputs in regulatory compliance systems.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +۳ مورد دیگر

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

Frederick J. Vetterمشاهده پروفایل

دانشیار

• Cardiac Physiology
• Computational Modeling
• Biomedical Engineering
• +۴ مورد دیگر

Frederick J. Vetter is an Associate Professor at the University of Rhode Island in the Department of Electrical, Computer and Biomedical Engineering . His research focuses on Cardiac Physiology , Computational Modeling , and Biomedical Engineering . Education Ph.D. in Bioengineering, University of California, San Diego (1999) M.S. in Bioengineering, University of California, San Diego (1994) M.B.A., University of California, Davis (1989) M.S. in Electrical and Computer Engineering, University of California, Davis (1988) B.S. in Electrical and Computer Engineering, University of California, Davis (1984) Dr. Vetter's research spans computational cardiology, finite element modeling, and cardiac material parameter optimization using genetic algorithms . His work also extends to biomechanics and cardiac hypertrophy analysis. Key publications include studies on three-dimensional heart models and GPU performance optimization . Scientific Awards : 2009 NSF CAREER Award Dr. Vetter has advised graduate students such as Arun U. Nair and Michael Eugene Dunn, with contributions to patents related to integrated finite element circulatory models . His work bridges engineering and cardiac physiology .

Eran Treisterمشاهده پروفایل

استادیار

• Computational Science
• Scientific Computing
• Numerical Methods
• +۸ مورد دیگر

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)