جست‌وجوی استاد و پژوهشگر

Dimitrios Skarlatosمشاهده پروفایل

استادیار

• Computer Architecture
• Security and Privacy
• Operating Systems
• +۱ مورد دیگر

Dimitrios Skarlatos is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research bridges computer architecture and operating systems with a focus on performance, security, and scalability. His work revolves around two central themes: (1) redesigning abstractions and interfaces between hardware and OS layers to improve performance and scalability, and (2) uncovering security vulnerabilities and building defenses at the hardware-OS boundary. Research Focus: Operating system security and vulnerability prevention Virtual memory systems and page table optimizations Serverless computing architectures Hardware-software co-design for security Datacenter resource management Dr. Skarlatos advises five PhD students and has published extensively on systems research. He received his PhD from the University of Illinois at Urbana-Champaign, where his dissertation received the ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award for contributions to redesigning hardware-OS abstractions.

Giuseppe Barcaمشاهده پروفایل

مدرس

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Giuseppe Barca is a Lecturer of High-Performance Computing (HPC) in the School of Computing at the Australian National University (ANU), part of the ANU College of Systems & Society. He holds leadership roles in multi-institutional collaborations, including PaCER projects and the GAMESS Exascale Computing Project. His research focuses on integrating HPC, AI, and quantum chemistry to revolutionize computational science, enabling transformative digital technologies in materials science, drug discovery, and sustainable manufacturing. Barca’s work has led to world records in computational-driven chemical modeling and the 2024 ACM Gordon Bell Prize for the EXESS software. His research interests span high-performance algorithms, machine learning for digital chemistry, and autonomous discovery protocols. He leads a group advancing quantum chemistry software like EXESS, optimized for exascale supercomputers. The group’s digital workflows automate and accelerate chemical R&D by combining quantum mechanics with AI-driven predictions. Barca’s contributions bridge computational efficiency and scientific impact, addressing challenges in scalable HPC algorithms and AI integration. Awards: 2024 ACM Gordon Bell Prize, World Record in Material Structure Modelling (2020–2021) Leadership Roles: Pawsey Centre for Extreme-scale Computing (PaCER), GAMESS Exascale Project Software: EXESS (winner of Gordon Bell Prize), AliSim-HPC, CONQUEST Barca’s team offers competitive graduate and postdoctoral openings in HPC, AI, and digital chemistry, emphasizing programming skills in C/C++, GPU architectures, and version control systems. Research themes include algorithmic innovation for quantum chemistry, generative AI for material discovery, and autonomous digital workflows for drug design.

Abdullah Al-Mamunمشاهده پروفایل

استادیار

• High-Performance Computing
• Distributed Systems
• Blockchain Technology
• +۵ مورد دیگر

Abdullah Al-Mamun is an Assistant Professor and Director of the High Performance & Secure Data Computing (HPSDC) Lab at Augusta University's School of Computer and Cyber Sciences. His research focuses on resource-efficient distributed systems, high-performance computing, and blockchain-based solutions for secure data management. He holds a Ph.D. from the University of Nevada, Reno, and a double M.Sc. from RWTH Aachen University and the University of Trento under the Erasmus Mundus program. His work has been supported by grants including an NSF CRII award ($165,000) and a Google Cloud Research Grant ($10,000). Education: Ph.D., Computer Science & Engineering (2022), University of Nevada, Reno M.Sc., Computer Science (2013), RWTH Aachen University / University of Trento (Double Degree) B.Sc., Computer Science & Engineering (Summa Cum Laude, 2009), American International University-Bangladesh Research Interests: High-Performance Blockchain Resource-Efficient HPC Extreme-Scale Data Management Secure Data Provenance Edge Computing Selected Awards: NSF CRII Award (2024–2026) Best Paper Award (Cloud'19) Outstanding Graduate Student Scholarship (UNR, 2020–2021) Service & Teaching: ACM Programming SIG Club Advisor (2023–present) Reviewer for IEEE IoT Journal, IEEE Transactions on Consumer Electronics, and others Teaches courses in Low-Level Programming, Computer Architecture, and High-Performance Computing Labs & Teams: Directs the HPSDC Lab, publishing in top-tier venues like SC, ICDE, and IPDPS.

Zhanyang Zhangمشاهده پروفایل

استاد

• Wireless Communication and Networks
• Cyber-Physical Systems & IoT/Sensor Networks
• Numerical Modelling and Simulation
• +۶ مورد دیگر

Dr. Zhanyang Zhang is a Professor of Computer Science at the City University of New New York, jointly appointed at the College of Staten Island and the CUNY Graduate Center. His expertise bridges wireless networking, cyber-physical systems, IoT, numerical simulation and high-performance computing, with recent emphasis on 5G/edge intelligence, ocean oil-spill modelling, and assistive technologies for the visually impaired. Education: Ph.D. in Computer Science, City University of New York M.S. in Computer Science, College of Staten Island B.E. in Computer Engineering, Jilin University, Changchun, China Research Interests: Dr. Zhang's research integrates communication theory, data science and large-scale simulation. He develops protocols for next-generation wireless networks, designs energy-efficient algorithms for underwater and terrestrial sensor systems, and applies lattice-Boltzmann methods to environmental disasters such as oil-spill propagation and urban flooding. His group also explores deep-learning-driven anomaly detection in 5G and edge-computing offloading strategies using game-theoretic and reinforcement-learning frameworks. Publication Trends: Across the past decade his scholarly output has shifted from foundational sensor-network coverage and localisation toward interdisciplinary applications: assistive smart-city services, GPU-accelerated ocean-surface transport models, and AI-based traffic and flood prediction. A recurrent methodological thread is the fusion of stochastic modelling (Markov, game, queueing) with high-performance simulation to derive scalable, real-time solutions. Grants & Projects: He currently leads or co-leads projects funded by the U.S. National Science Foundation and multiple CUNY schemes, totalling more than USD 1 M since 2020. Topics range from assistive cyber-connections for the visually impaired to next-generation oil-spill forecasting assimilating large sensor datasets. Labs & Teams: Dr. Zhang heads the Wireless & Cyber-Physical Systems Research Group at CSI, maintains affiliations with the CUNY High-Performance Computing Center, and supervises doctoral students within the Ph.D. Program in Computer Science at the Graduate Center.

Weizhen Maoمشاهده پروفایل

استاد

• Computer Science
• Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Weizhen Mao is a Professor in the Department of Computer Science at the College of William and Mary. His research focuses on algorithms, theoretical computer science, parallel computing, distributed systems, and networking. He holds an office in McGlothlin-Street Hall 114 and can be reached at wm@cs.wm.edu. Teaching Responsibilities: Professor Mao instructs a range of courses including Discrete Structures (CSci 243), Algorithms (CSci 303), Theory of Computation (CSci 663), and advanced topics in algorithms and computation at both undergraduate and graduate levels. His courses emphasize foundational concepts in computer science theory and practical applications. Research Interests: His work spans algorithm design, scheduling theory, distributed computing, and network protocols. Recent publications address challenges in sensor networks, RFID systems, vehicular communication, and parallel processing. His research often bridges theoretical guarantees with practical implementations in real-world systems. Professional Contributions: With over 50 publications in top conferences and journals (e.g., IEEE Transactions on Wireless Communications, ACM MobiHoc), his work has impacted areas like energy-efficient networking, optimal storage placement in sensor networks, and GPU cluster optimization. He maintains active collaborations with industry and academic partners to advance computational methodologies.

Mathias Brieuمشاهده پروفایل

استاد

• Mechanics of Materials
• Computational Mechanics
• Soft Polymers
• +۵ مورد دیگر

Mathias Brieu is a Professor in Mechanical Engineering at California State University, Los Angeles. He specializes in solid mechanics, computational mechanics, and the mechanics of soft polymers and bio-polymers for biomedical applications. His research focuses on material characterization, constitutive modeling, and numerical methods, particularly in pelvic soft tissues and polymer sustainability. Education: Habilitation à Diriger des Recherches (H.D.R.) in Physics, University of Lille (2005) PhD in Mechanical Engineering, Ecole Normale Supérieure Paris-Saclay (1999) - Highest Distinction MS in Mechanical Engineering, University Paris-Sorbonne (1996) - Highest Distinction Agrégation de Mécanique (French National Examination for Higher Education) Research Interests: Experimental and computational mechanics of soft materials Patient-specific simulation of pelvic systems for medical device design Bio-polymers and sustainability in polymer applications Medical imaging integration with biomechanical modeling Publications Trends: Recent work emphasizes pelvic biomechanics, composite material behavior under dynamic loads, and medical device innovation. His articles bridge experimental methods (e.g., MRI analysis) with computational models to address clinical challenges in gynecology and surgical planning. Awards: PhD 'Très honorable avec félicitations du jury' MS 'Mention Très Bien' Agrégation de Mécanique (French Ministry) Grants & Advising: While specific grants are not detailed, his work implies sustained funding for interdisciplinary research. He has advised students through his academic roles, with no named advisees listed. Labs & Teams: Engages in collaborative projects involving medical imaging, material testing, and biomechanical simulation, though specific lab names are not mentioned.

Michael A. Thomasمشاهده پروفایل

استاد

• Bacteriophage Genomics
• Evolutionary Biology
• Environmental Toxicology
• +۳ مورد دیگر

Michael A. Thomas is a Professor in the Department of Biological Sciences at Idaho State University, where he has served since 2003. His research focuses on bacteriophage biology and genomics with applications in combating antibiotic-resistant infections, alongside investigations into environmental impacts of pharmaceuticals on neurological health. Dr. Thomas' educational background includes: Ph.D. in Entomology from Pennsylvania State University (2000) M.S. in Biology from Kansas State University (1994) B.S. in Biology from the University of Nebraska, Lincoln (1991) Postdoctoral fellowship at the Medical College of Wisconsin (2000-2003) on the Rat genome project His research program spans bacteriophage host range evolution, genome annotation of novel phages (particularly Microbacterium-infecting clusters EG and EE), and environmental neurotoxicology examining psychoactive pharmaceuticals' links to autism and Alzheimer's disease. He employs genomic, proteomic, and bioinformatic approaches to study viral evolution and contaminant impacts on biological systems. Recent publications (2022-2025) reveal dual research trajectories: bacteriophage discovery/annotation (Rowlf, SallyK, KillerTomato) and environmental neurotoxicology, with increasing emphasis on educational innovation through course-based research experiences. No scientific awards or fellowships are documented in the provided materials. Dr. Thomas mentors undergraduate and graduate researchers in his Life Sciences 436 laboratory, developing course-based research experiences including the Bacteriophage Discovery & Bioinformatics Lab (BIOL 1111/1112). His teaching portfolio spans Genetics, Organic Evolution, and Senior Seminar courses. Research funding appears focused on phage genomics and environmental toxicology through NIH and NSF mechanisms, though specific grants aren't detailed. His laboratory maintains active bacteriophage isolation and sequencing capabilities, supporting his inclusive Research and Education Community (iREC) model for undergraduate science education reform, which integrates research and teaching through structured course-based experiences.

Patrick Erdeltمشاهده پروفایل

استاد

• Data Science
• Database Management Systems
• Performance Benchmarking
• +۴ مورد دیگر

Prof. Patrick Erdelt is a Professor at the Department of Mathematics of Berlin University of Applied Sciences (BHT). He leads the Laboratory for Numerical Mathematics and serves as Deputy Head of Studies for the Data Science program. His roles include membership in the AKO and PA committees of the Data Science + X research network. His research focuses on database systems, performance benchmarking, cloud-native computing, and numerical methods leveraging GPUs. Erdelt is actively involved in developing benchmarking tools like the DBMS-Benchmarker and orchestrating experiments via Kubernetes in cloud environments. He collaborates with industry partners through projects such as OCIDA, addressing customer churn through data-driven strategies. Erdelt is the Central Point of Contact for the Amazon Web Services University Program and contributes to open-source projects including PyPI packages and Docker images. His work emphasizes reproducible experiments and scalable cloud-based benchmarking frameworks, published in venues like TPCTC and the Journal of Open Source Software.

Felix Alexander Gersمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Neural Networks
• +۶ مورد دیگر

Prof. Felix Alexander Gers is a Professor of Multimedia Development at the Berlin University of Technology’s Department of Computer Science and Media since 2005. He holds a PhD from EPFL (Switzerland) in AI systems, focusing on Recurrent Neural Networks with LSTM. His expertise spans Artificial Intelligence, Deep Learning, NLP, and Game-Based Learning (GBL). He pioneered the CoDi algorithm (Cellular Automata-based neural modeling) during his work at ATR (Japan) and received ATR’s Best Patent Award. His research includes virtual microbiology laboratories, dialogue systems for avatars, and neural network applications in drug development and 3D data classification. He co-leads the Data Science Group at BHT and has been a fellow in the “Excellence in Teaching” project since 2011, emphasizing virtual lab simulations. Notable projects include Brain Bots (NLP-driven lab assistants) and GBL in microbiology. Education: Diploma in Physics, Leibniz University Hannover PhD in AI Systems (LSTM), EPFL (Switzerland) Research Interests: Neural Networks (RNN/LSTM) Natural Language Processing Virtual Laboratories and GBL Industrial AI Applications Key Projects: Brain Bots: Voice-enabled lab assistants in virtual microbiology labs CoDi Algorithm: CA-based neural structure simulation GBL in the Lab: Game-based microbiology experiments Awards: ATR Best Patent Award (CoDi) Labs/Teams: Member of the Data Science Group and Online Learning Lab at BHT.

• Computer Science Education
• Programming Education
• Computer Architecture
• +۴ مورد دیگر

Sohum Sohoni is a Professor in the School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering at Arizona State University. His research spans computer science education, programming pedagogy, computer architecture, and image quality assessment with GPU acceleration. He has developed innovative educational approaches for computer architecture courses using the PLP instruction set architecture and has contributed significantly to understanding programming education through analysis of error messages and embedded questions. His research interests focus on Computer Science Education , particularly programming education techniques, computer architecture pedagogy, and engineering education methods. He has made substantial contributions to understanding how students learn programming concepts, develop effective debugging skills, and comprehend computer architecture principles. His work bridges theoretical computer science concepts with practical educational applications, emphasizing hands-on learning experiences and innovative assessment methods. His publication trends reveal a strong focus on computer science education research with particular emphasis on programming education (2015-2018), computer architecture education using PLP (2014-2017), and image quality assessment with GPU acceleration (2012-2018). His work consistently combines theoretical computer science concepts with practical educational applications, demonstrating his commitment to improving how computing concepts are taught and learned. His advising work includes mentoring students like Christopher Mar, Harsha B. M. Kadekar, Shaowen Lu, Thien D. Phan, and Vignesh Kannan, who have co-authored publications with him across various computing education topics. His research has been supported through various educational technology projects focused on online learning environments, software engineering education, and computer architecture instruction. He has been actively involved in developing virtualized learning environments for IoT education, creating innovative approaches for software engineering education, and designing effective methods for teaching computer architecture concepts. His work demonstrates a consistent commitment to improving computing education through evidence-based pedagogical approaches and technological innovation.

• Computational Geometry
• Numerical Simulation
• GPU Programming
• +۵ مورد دیگر

César Blecua Udías is a Professor and Subdirector at the Escuela Superior de Enseñanzas Técnicas (Architecture) at Universidad CEU Cardenal Herrera in Valencia. He holds a PhD in Architecture (2017) and teaches courses such as Structural Mechanics, Steel Structures, and New Technologies in Dentistry. His research focuses on computational methods for physical process modeling, including software development for architectural structures, CFD simulations, and GPU-accelerated graphics. He coordinates university choirs and chamber orchestras, reflecting his interdisciplinary engagement. Education: Bachelor of Architecture: Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Valencia (2004) PhD in Architecture: Universidad CEU Cardenal Herrera (2017, Summa Cum Laude) Research Interests: His work bridges architecture and computing, developing tools for structural analysis, thermal simulations in Mediterranean courtyards, radiosity lighting algorithms, and hardware-accelerated image compositing. He is part of the 'Métodos computacionales para la modelización de procesos físicos' research group, focusing on quadrilateral meshing and finite element method implementations. Grants/Advising: Supervised the doctoral thesis 'Análisis computacional de mallados cuadrangulares en geometrías complejas para implementación en el método de los elementos finitos.' Labs/Teams: Leads the 'Putthephysicsinthecomputer' computational methods group and coordinates the university's Orfeón and Camerata (choir and chamber orchestra).

• High-Performance Computing
• LiDAR Technology
• Computer Vision
• +۵ مورد دیگر

David López Vilariño is a Lecturer at the University of Santiago de Compostela, affiliated with the Department of Electronics. His research focuses on LiDAR data processing, FPGA acceleration for high-performance computing (HPC), and embedded systems. He teaches courses such as Fundaments of Electronic Instrumentation , Heterogenous Programming , and The Physics of Computing , contributing to bachelor’s and master’s programs in Physics, Informatics Engineering, and High Performance Computing. His research interests span LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He has developed algorithms for LiDAR data analysis, FPGA-based motion estimation, and GPU-accelerated medical image processing. Notable contributions include tools like the Open Lidar Visualizer and Analyser for 3D point cloud visualization. Dr. Vilariño’s work bridges computer architecture, signal processing, and geomatics. His recent publications emphasize optimizing HPC workloads using FPGAs and Intel OneAPI, as well as automated LiDAR-based detection of power lines, road points, and pedestrian zones. He collaborates on interdisciplinary projects involving parallel computing, embedded vision systems, and real-time surveillance applications. Teaching responsibilities include coordinating courses for the Máster Universitario en Computación de Altas Prestaciones, a joint program with the University of A Coruña. No academic awards are listed, but his active role in teaching and research grants underscores his contributions to the field.

• High-Performance Computing (HPC)
• Scientific Computing
• Numerical Simulation
• +۳ مورد دیگر

Lionel Boillot is an Expert Engineer and holds a PhD in the MAGIQUE-3D project team at Inria, located in Bordeaux, France. His work focuses on High-Performance Computing (HPC), Scientific Computing, and numerical simulation techniques for geophysical applications. He specializes in parallel algorithms, discontinuous Galerkin methods (DGM), and optimizing wave propagation simulations in anisotropic media. His research emphasizes hardware optimization, including vectorization via SIMD and GPU acceleration, and task-based parallel programming models for large-scale simulations. Education: PhD in Applied Mathematics and Parallel Algorithmic Optimization (2014), Université de Pau et des Pays de l'Adour, France. Research Interests: Boillot's work spans HPC architecture, absorbing boundary conditions for elastic waves, and seismic imaging. He has contributed to developing efficient algorithms for TTI (Tilted Transverse Isotropic) media modeling and optimizing simulations using runtime systems like StarPU. His research also explores collaborative tools in geology, such as VR-based seismic analysis (Sismage-VR). Key Projects: Collaborations include work with TOTAL (via MATHIAS symposia), the Depth Imaging Partnership (INRIA-TOTAL), and international workshops like SIAM PP and EAGE. His publications span conferences such as SIAM, SEG, and ECCOMAS, focusing on parallel computing, boundary conditions, and geophysical applications. Affiliations: Active member of the MAGIQUE-3D team, contributing to both theoretical and applied aspects of computational geophysics. His work intersects applied mathematics, computer science, and geoscience to advance computational methods in resource exploration and seismic analysis.

Tia Newhallمشاهده پروفایل

استاد

• Parallel and Distributed Systems
• Computer Science Education
• Memory Management
• +۴ مورد دیگر

Tia Newhall is the Centennial Professor of Computer Science at Swarthmore College , where she has been a faculty member since 2002. Her research focuses on parallel and distributed systems , with a strong emphasis on educational innovations. PhD in Computer Science (1999) from the University of Wisconsin under Bart Miller Co-PI on NSF:DUE:IUSE grant (2022-26) for interactive computing systems materials Developer of the Dive into Systems online textbook Research Interests include: Parallel and distributed systems Memory management for cluster computers GPU programming and visualization tools Computer science education reform Curriculum development for systems courses Open educational resources Recent Article Trends show a strong focus on teaching systems concepts and parallel computing to undergraduates through: Interactive educational tools (ASM Visualizer) Hybrid teaching methods Textbook development (Dive into Systems) Cluster storage virtualization Parallel dynamic programming GPU programming for non-experts Scientific Awards : ACM SIGCSE Special Projects Award (2019) for Dive into Systems textbook Advising and Grants : Mentored numerous student co-authors across her publications. Collaborated on grants including: NSF:DUE:IUSE (2022-26) NSF:CNS:CSR (2011-16) CRA-W:CREU (2004-05)

Ali Aghaeifarمشاهده پروفایل

پژوهشگر ارشد

• Magnetic Resonance Imaging
• High-field MRI
• B0 Field Shimming
• +۵ مورد دیگر

Dr. Ali Aghaeifar is a Postdoctoral Researcher at the Department for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics in Tübingen, Germany. His research focuses on developing advanced magnetic resonance imaging techniques, particularly for ultra-high field (9.4T) brain imaging systems. His research interests span multiple domains of MRI physics and engineering, with emphasis on B0 field shimming , motion correction , and computational simulation of MR phenomena. Aghaeifar has developed several open-source software frameworks including SpinWalk for spin dynamics simulation, TurboBloch for Bloch equation solving, and AMoCo for motion correction in MRI. His recent publications demonstrate expertise in optimizing multi-coil shimming systems, with significant improvements over conventional spherical harmonic approaches. His work bridges physics, engineering, and neuroscience to solve practical challenges in high-field MRI. As a software developer, he has created multiple computational tools implemented in C++ with GPU acceleration using CUDA, and provides interfaces for MATLAB and Python users. His Biot-Savart simulator enables precise calculation of magnetic fields from arbitrary coil geometries.