جست‌وجوی استاد و پژوهشگر

Dar-Jen Changمشاهده پروفایل

دانشیار

• Computer Science
• Parallel Computing
• GPU Programming
• +۷ مورد دیگر

Dar-Jen Chang is an Associate Professor in the Department of Computer Science and Engineering at the University of Louisville. He holds a B.S. in Mathematics from National Tsing Hua University (1970), an M.S. in Computer Information/Control Engineering from the University of Michigan (1982), and a Ph.D. in Mathematics from the same institution (1982). His research focuses on parallel computing, GPU programming, bioinformatics, 3D modeling, and algorithm optimization. His work spans disciplines including computer science, data mining, and biomedical applications. Chang’s research interests emphasize leveraging GPU acceleration for computational tasks such as RNA folding algorithms, hierarchical clustering, and Euclidean distance calculations. He has explored applications in 3D anatomical modeling, robotics simulation using Unity, and gaming frameworks with CUDA integration. His contributions include developing frameworks for algorithm visualization and database systems for automated process planning. His publications highlight trends in GPU-optimized algorithms, graph database analysis (e.g., Yelp and IMDb datasets), and interdisciplinary applications like medical imaging and genetic sequence prediction. He has also contributed to neural networks for fuzzy logic systems and biological sequence mining.

Longfei Shangguanمشاهده پروفایل

استادیار

• Mobile and wireless systems
• IoT systems
• Edge computing
• +۵ مورد دیگر

Longfei Shangguan is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He leads the Pittsburgh Experimental Research Group (PERG), focusing on hardware/software systems to advance computing, sensing, and communication in sensor/wireless technologies. Previously, he was a postdoc at Princeton and a senior researcher at Microsoft. He holds a PhD from HKUST (2015) under Professors Yunhao Liu and Ke Yi. Research Interests: Mobile/wireless systems, IoT, edge computing, and hardware/software co-design for sensor networks. Key areas include novel IoT applications, low-power hardware, and wireless protocol optimization. Key Projects Sensor-in-the-loop AI agents leveraging multimodal sensors Human-centric sensing using acoustic/vision modalities Wireless Digital Twin via neural ray tracing Professional Contributions Editor: IEEE Pervasive Computing, ACM GetMobile PC Member for top conferences including MobiCom, NSDI, SenSys Organized Dagstuhl Seminar on IoT Wireless Scenarios Awards NSF CAREER Award (2024) Google Research Scholar Award (2023) Best Paper Awards at MobiCom 2024 and MobiCom 2021 Advising & Grants Supervises 5 PhD students, 3 MS students, and undergraduate researchers Recipient of MassAITC Pilot Award (2025), NSF CAREER (2024) Labs/Teams: PERG lab focuses on experimental systems research with hands-on prototyping.

D. Brian Larkinsمشاهده پروفایل

دانشیار

• Parallel Computing
• High-Performance Computing (HPC)
• Distributed Systems
• +۵ مورد دیگر

Dr. D. Brian Larkins is an Associate Professor and Chair of the Computer Science department at Rhodes College in Memphis, Tennessee. He specializes in parallel computing, high-performance computing (HPC), and distributed systems. His research focuses on optimizing runtime systems, work stealing algorithms, and leveraging network offload hardware to enhance parallel application performance. He has been recognized with the Clarence Day Award for Outstanding Teaching in 2021, reflecting his dedication to pedagogical innovation in computer science education. Before joining Rhodes College in 2015, Larkins worked at Bell Laboratories and several startups, focusing on network security, machine learning, and video streaming technologies. He holds a B.S., M.S., and Ph.D. in Computer Science and Engineering from The Ohio State University, with a focus on Parallel and Distributed Computing. As the NSF-funded XSEDE Campus Champion at Rhodes, Larkins supports faculty and students in accessing large-scale HPC resources nationwide. He also leads the installation of a 2,112-node HPC cluster in Briggs Hall for institutional research use. His teaching philosophy emphasizes blending theory with practical problem-solving to cultivate analytical thinkers. Key research contributions include frameworks for distributed data structures (e.g., Global Trees), scalable work stealing mechanisms, and GPU acceleration of scientific systems like the Advanced Regional Prediction System (ARPS). His work has been published at top venues such as the International Conference on Parallel Processing (ICPP) and the ACM/IEEE Supercomputing Conference (SC). Larkins has pioneered FPGA integration into computer science curricula and explored cognitive apprenticeship frameworks in robotics education. His current projects aim to repurpose network hardware for efficient distributed computing and dynamic load balancing.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +۲ مورد دیگر

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

Ander Biguriمشاهده پروفایل

پژوهشگر

• Tomographic Reconstruction
• Medical Imaging
• Machine Learning
• +۴ مورد دیگر

Dr. Ander Biguri is a Researcher at the Cambridge Image Analysis (CIA) group within the Faculty of Mathematics at the University of Cambridge. His primary research focuses on tomographic reconstruction techniques, particularly in cone-beam computed tomography (CBCT), and the integration of machine learning into medical imaging workflows. He is the main developer and maintainer of the TIGRE toolbox, an open-source software for iterative CT reconstruction. Key research areas include source-detector trajectory optimization for improved imaging quality, low-dose CT reconstruction, metal artifact reduction, and dynamic PET imaging. His work emphasizes practical applications in clinical and industrial tomography, with contributions to both algorithm development and hardware optimization. Biguri collaborates on projects such as AdvanCT (EURAMET) and has published extensively on topics like motion correction in PET/CT, GPU-based iterative reconstruction, and the design of imaging systems for radiation therapy.

Sabrina M. Neumanمشاهده پروفایل

استادیار

• Computer Architecture
• Robotics
• Domain-Specific Accelerators
• +۳ مورد دیگر

Sabrina M. Neuman is an Assistant Professor of Computer Science at Boston University, specializing in computer architecture design informed by domain-specific insights, particularly for robotics applications. She holds a PhD in Electrical Engineering and Computer Science from MIT and was a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research focuses on closing computational gaps in robotics through domain-specific accelerators, leveraging robot morphology for hardware design. She has received honors including the 2021 EECS Rising Star and the Boston University Innovation Career Development Professorship (2023-2026). Education: PhD in Electrical Engineering and Computer Science, MIT M.Eng. in Electrical Engineering and Computer Science, MIT S.B. in Electrical Engineering and Computer Science, MIT Research Interests: Her work emphasizes domain-specific computing for robotics, including automated hardware design flows, FPGA/ASIC acceleration for motion planning, and scalable accelerator deployment across robot platforms. She develops methodologies like Robomorphic Computing, which translates robot morphology into customized hardware accelerators, achieving significant speedups in critical robotics kernels. Awards & Recognition: 2021 EECS Rising Star IEEE Micro Top Picks Honorable Mentions (2022, 2023) 2023-2026 Boston University Innovation Career Development Professorship Grants & Contributions: Developed RobotPerf, an open-source benchmarking suite for robotics computing systems Worked on Grid (GPU-accelerated rigid body dynamics) and RobotCore (ROS 2 hardware acceleration) Labs & Teams: While no specific lab name is mentioned, her work integrates closely with Boston University's robotics and computer architecture groups, emphasizing open collaboration with industry and academia.

Daniel Lohmannمشاهده پروفایل

استاد

• Operating Systems
• Embedded Systems
• Real-Time Systems
• +۴ مورد دیگر

Professor Daniel Lohmann leads the Systems Research and Architecture (SRA) group at Leibniz Universität Hannover since 2017, following his role as Associate Professor (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009–2016). His work focuses on configurable system software, emphasizing embedded systems, real-time systems, and dependable architectures. Key projects include danceOS (dependability in embedded OS), Sloth (minimal-effort kernels), and AspectC++ (aspect-oriented C++ extension). His research is funded by DFG and addresses challenges in variability management, fault tolerance, and real-time scheduling. Research interests span operating systems design, software product lines, generative programming, and aspect-oriented development. Notable contributions include the CiAO OS family and the Sloth RTOS, leveraging aspect-oriented techniques for flexibility and efficiency. His work on configuration analysis (e.g., static variability in Linux) and fault mitigation (e.g., AN-Codes for soft errors) highlights his expertise in system reliability and optimization. Publications emphasize real-time systems, embedded software, and software engineering, with awards including the Best Paper Award at SPLC 2010 and an Outstanding Paper at RTAS 2017. He supervises theses on system software tailoring, fault tolerance, and industrial software ecosystems. His groups collaborate on invasive computing (SFB/TRR 89) and hardware-aware OS design.

Gabriele Mencagliمشاهده پروفایل

دانشیار

• Parallel Programming
• Parallel Architectures
• Self-* Systems
• +۳ مورد دیگر

Dr. Gabriele Mencagli is an Associate Professor in the Department of Computer Science at the University of Pisa, Italy. He holds a Ph.D. in Computer Science (2012) and has served as an Assistant Professor (2014–2018) and Tenure-Track Professor (2018–2021) before his current position. His research focuses on parallel systems, including architectures, programming models, and runtime systems for data stream processing. He leads work on the WindFlow stream processing library and has contributed to projects like TEXTAROSSA, ADMIRE, and EUPEX. He co-organized major conferences like HPDC 2024 and DEBS 2025 and serves on editorial boards for journals like Future Generation Computer Systems and Cluster Computing. Education: B.Sc. (2006, summa cum laude), M.Sc. (2008, summa cum laude), Ph.D. (2012) in Computer Science, all from the University of Pisa. Research Interests: Parallel programming, self-adaptive systems, data stream processing, GPU/FPGA acceleration, and high-performance computing. Over 80 publications in top journals and conferences, including IEEE TPDS, JPDC, and Euro-Par. Awards include Italian Habilitation as Full Professor (2025). Teaching: Courses on High-Performance Computing and Computer Architecture, including CUDA programming and parallel design patterns. Active in curriculum development for both bachelor’s and master’s programs. Grants & Projects: Principal investigator in EU-funded projects (e.g., TEXTAROSSA, NOUS) and collaborations with industry (e.g., List-group S.p.A., Autodesk). Focus on exascale computing, digital twins, and edge computing.

• Mechanical Engineering
• Railway Systems
• Multibody Dynamics
• +۳ مورد دیگر

Eduardo Corral Abad is an Associate Professor in the Department of Mechanical Engineering at Carlos III University of Madrid, specializing in multibody dynamics, railway systems, and contact mechanics. His research spans industrial heritage, vibration analysis, and educational technology. Railway axle monitoring and crack detection using advanced signal processing Dynamic modeling of mechanical systems with nonlinear contact and friction forces Development of interactive teaching applications for engineering education Use of parallel computing and GPU acceleration in mechanical simulations His contact email is eduardo.corral@uc3m.es . Recent work includes publications on railway diagnostics, helical gear transmissions, and Android-based educational tools.

Sajad Homayounمشاهده پروفایل

استادیار

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

Sajad Homayoun is a Tenure Track Assistant Professor at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His research focuses on integrating Artificial Intelligence with cybersecurity to develop advanced solutions for threat detection and mitigation. PhD in Computer Science Specialization in Cybersecurity & Machine Learning His technical expertise spans: AI-driven intrusion detection systems Adversarial machine learning (AML) Explainable AI (XAI) for security transparency Time series and graph analysis MLOps implementation with MLflow Python development using pandas, scikit-learn, and TensorFlow Research trends from his publications show strong emphasis on: Combating phishing attacks through ML Applying deep learning to crowdsensing trustworthiness Failure prediction in industrial systems Extracting behavioral patterns from ransomware Blockchain-based security applications He teaches Network Security courses during fall semesters and maintains active collaborations with researchers across Denmark and international institutions.

Debopam Bhattacherjeeمشاهده پروفایل

پژوهشگر

• AI Systems
• Networked Systems
• Low-Earth Orbit Satellite Networks
• +۱ مورد دیگر

Debopam Bhattacherjee is a Senior Researcher at Microsoft Research, Bangalore, focusing on AI Systems, Networked Systems, and Low-Earth Orbit (LEO) Satellite Networks. He holds a PhD in Computer Science from ETH Zürich (2021) and a dual Erasmus Mundus Master's in Security and Mobile Computing. His work has been recognized with the IRTF Applied Networking Research Prize (2020) and multiple Best Paper/Dataset Awards. Research interests include LEO satellite networks, sustainable AI systems, network topology design at hypersonic speeds (27,000 km/hour), and green computing via modular data centers. His publications span ACM SIGCOMM, USENIX ATC, IEEE COMSNETS, and arXiv, with recent patents on AI-driven LLM training and inference systems. Key awards: IRTF Applied Networking Research Prize (2020) Best Paper Award at ACM IMC 2020 Best Dataset Award at ACM IMC 2020 He has supervised numerous PhD/Master's students at ETH Zürich and Microsoft Research, including Tella Rajashekhar Reddy, Shubham Tiwari, and Saksham Bhushan. His educational background includes a B.E. in Computer Science & Engineering from Jadavpur University (2009) and a research fellowship at the Max Planck Institute (2019).

• GPU Programming
• Multi-Core Processors
• Statistical Mechanics
• +۵ مورد دیگر

Prof. Piotr Białas is a Professor of Physical Sciences and Director of the Institute of Applied Informatics at Jagiellonian University. He holds a habilitation in physics from the same institution and has served as department head and faculty member since 1993. His research focuses on GPU programming, multi-core processor optimization, and theoretical physics areas like simplicial gravity and quantum chromodynamics on networks. He has supervised over three doctoral and dozens of master's theses. Current projects include GPU acceleration for medical imaging (J-PET tomograph) and real-time FPGA-based data processing. Education: PhD in Physics (1993), MSc in Physics (1990) from Jagiellonian University. Academic roles include membership in the Academic Council of Technical Informatics and Complex Systems Commission at Polish Academy of Arts and Sciences. Invited professorships at universities in Amsterdam, Bielefeld, Barcelona, and Saclay. Specializes in CUDA optimization, parallel computing, and medical imaging algorithms.

Edmond Chowمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +۷ مورد دیگر

Edmond Chow is a Professor and Associate Chair in the School of Computational Science and Engineering at Georgia Institute of Technology. His research focuses on numerical methods, high-performance computing, and scientific computing applications in quantum chemistry, molecular dynamics, and machine learning. He holds awards including the 2009 ACM Gordon Bell Prize and 2002 PECASE. He leads the Intel Parallel Computing Center, advancing computational chemistry algorithms for Intel architectures. Education: Ph.D., Computer Science (minor in Aerospace Engineering), University of Minnesota, 1997 Honors B.A.Sc., Systems Design Engineering, University of Waterloo, 1993 Research Interests: Parallel algorithms for sparse linear systems and preconditioning High-performance quantum chemistry simulations Asynchronous iterative methods for extreme-scale computing Kernel-based methods and hierarchical matrices for Gaussian processes Applications in biological macromolecule dynamics and machine learning Awards & Recognition: ACM Gordon Bell Prize (2009) PECASE (2002) SIAM Fellow (2021) SC23 Test of Time Award Advising & Grants: Advised 11+ PhD/MSc students in numerical methods and HPC Funded by NSF, DOE, DARPA, and Intel Developed software tools like H2Pack (hierarchical matrices), GTFock (quantum chemistry), and Simint (electron integrals) Labs & Teams: Leads the Intel Parallel Computing Center and collaborates with institutions like LLNL, Sandia, and Temple University on asynchronous computing and quantum chemistry projects.

Stefan Zellmannمشاهده پروفایل

دانشیار

• Scientific Visualization
• Computer Graphics
• High-Performance Computing
• +۴ مورد دیگر

Stefan Zellmann is an Associate Professor and Principal Investigator of the DFG Project 'VTV-AMR' at the Institute of Computer Science , University of Cologne. His research focuses on the intersection of large-scale scientific visualization and high-performance computing, particularly in developing real-time rendering algorithms for adaptive mesh refinement (AMR) data. He leads projects such as ExaBrick (AMR rendering framework) and Visionaray (cross-platform ray tracing library). Education & Roles: Completed his PhD in 2014 on 'Interactive High-Performance Volume Rendering'. Currently teaches courses on graphics processor architectures and programming, including Practical Computer Science: Architecture and Programming of Graphics and Coprocessors . Research Interests: Direct volume rendering, physically based rendering, AMR visualization, GPGPU computing, and FPGA programming. His work emphasizes low-latency interaction and efficient algorithms for supercomputers/multi-GPU systems. Awards: Honorable Mention @EGPGV 2020, Best Paper Awards @EGPGV 2018 (for Rapid k-d Tree Construction ), @VDA 2017 (for Ray Clipping ), and @PDCS 2012 (for Distributed Volume Rendering Architecture ). Key Projects: ExaBrick (AMR rendering), Visionaray (ray tracing), Virvo (volume rendering library), and HPSC TerrSys (high-performance computing in terrestrial systems). Grants & Services: Co-Chair of IEEE LDAV 2022 Posters, Program Committee member for IEEE VIS and EGPGV. Regular reviewer for journals/conferences like TVCG and IEEE VR. Labs/Teams: Part of the Center for Data and Simulation Science (CDS), focusing on visualization algorithms for exascale computing and large-scale scientific data.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +۳ مورد دیگر

Enes Bajrovic is a researcher affiliated with the Faculty of Computer Science, focusing on high-performance computing (HPC), big data processing, and performance portability. His work spans task-based parallelism, runtime systems, and optimization frameworks for heterogeneous architectures. He has contributed to major European projects like PEPPHER and AutoTune, which aim to advance HPC software tools and autotuning methodologies. His research emphasizes practical applications of parallel computing in domains such as mobile networks and scientific simulations. Education: Dipl.-Ing. Dr.techn., BSc in Computer Science His research interests include developing frameworks for compute- and data-intensive applications, leveraging technologies like Kubernetes, OpenCL, and Intel Xeon Phi coprocessors. He has authored numerous peer-reviewed publications on topics such as pipeline patterns, autotuning algorithms, and hybrid execution models. His work bridges theoretical advancements in parallel computing with real-world software engineering challenges. Bajrovic has collaborated on projects funded by the European Commission’s FP7 program, contributing to deliverables like runtime systems, tuning frameworks, and benchmarking tools. His research also addresses the integration of big data processing with HPC, particularly in telecommunications and distributed computing environments.