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Sophia ShaoView profile

Associate Professor

• Computer Architecture
• Integrated Circuits
• Domain-Specific Accelerators
• +5 more

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

Jeffrey D. BlanchardView profile

Professor

• Applied Harmonic Analysis
• Compressed Sensing
• Matrix Completion
• +7 more

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

Alireza OlamaView profile

Researcher

• Parallel Computing
• Distributed Computing
• Machine Learning
• +3 more

Alireza Olama is a Postdoctoral Researcher in the Department of Information Technology at Åbo Akademi University's Faculty of Science and Engineering. His work bridges parallel and distributed computing with machine learning and numerical optimization, contributing to the UN Sustainable Development Goals through algorithmic advancements. Doctoral research: Distributed framework for sparse convex optimization (2019, Universidade Federal de Santa Catarina) Master's thesis: Lyapunov Based Hybrid Model Predictive Control (2017) Research spans: Developing novel algorithms for distributed convex optimization (e.g., ADMM variants, Augmented Lagrangian methods) Creating software tools for sparse convex programming Applying optimization techniques to energy management systems Advancing GPU-accelerated machine learning frameworks Recent publications focus on ℓ0 sparsity, distributed consensus optimization, and hybrid control systems. Active in the academic community through conference presentations and collaborations with institutions like Norwegian University of Science and Technology (NTNU) and KTH Royal Institute of Technology.

Stefano CagnoniView profile

Associate Professor

• Evolutionary Computation
• Soft Computing
• Neural Networks
• +9 more

Stefano Cagnoni is an Associate Professor in the Department of Computer Engineering at the University of Parma, Italy. He holds a PhD in Bioengineering from the University of Florence and has been a key academic figure since joining the University of Parma in 1997. His research spans soft computing, evolutionary computation, neural networks, and their applications in computer vision and biomedical imaging. PhD in Bioengineering, University of Florence (1993) Bachelor’s in Electronic Engineering, University of Florence (1988) His primary research interests include evolutionary computation , neural networks , pattern recognition , and computer vision . He has pioneered work in genetic programming, particle swarm optimization, and GPU-accelerated computing for complex system analysis. His applied research focuses on biomedical image segmentation, autonomous robotics, and signal processing, with a strong emphasis on real-world implementation and industrial collaboration. The recent publications highlight a shift toward interdisciplinary applications, including machine learning for food authenticity, financial document analysis, and healthcare monitoring systems. These works reflect a consistent use of advanced computational intelligence techniques across diverse domains such as food science, finance, and medical informatics. Scientific Awards: Evostar 2009 Award for outstanding contributions to Evolutionary Computation Stefano Cagnoni has supervised numerous research projects funded by MIUR, CNR, ASI, ENEA, and the EU (including the Marie Skłodowska-Curie MIBISOC project). He has also led industrial collaborations, such as a computer vision-based train pantograph inspection system that led to a patented industrial product. He has organized major international events like EvoApplications and WIVACE and has held editorial roles in journals such as the Journal of Artificial Evolution and Applications. He leads research in evolutionary computation and soft computing, having founded GSICE (now WIVACE) and co-chaired MedGEC. His lab focuses on developing intelligent systems for image analysis, signal processing, and autonomous navigation, often using bio-inspired algorithms and parallel computing architectures.

Panagiota FatourouView profile

Professor

• Parallel and Distributed Computing
• Concurrent Data Structures
• Transactional Memory
• +8 more

Dr. Panagiota Fatourou is a Professor in the Department of Computer Science at the University of Crete and a senior researcher at the Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH). She holds a dual affiliation, contributing significantly to both institutions. She has previously served as faculty at the University of Ioannina and held postdoctoral positions at the Max-Planck-Institut für Informatik and the University of Toronto. She has also been a visiting professor at École Polytechnique Fédérale de Lausanne (EPFL). Her research spans the theoretical and practical foundations of parallel and distributed computing. She is a leading expert in concurrent data structures, transactional memory, and the use of persistent memory in system design. Her work integrates theoretical rigor with practical implementation, often publishing in top-tier venues like PPoPP, PODC, SPAA, and PVLDB. Recent publications highlight her focus on efficient garbage collection for multiversioning, persistent data structures, and distributed data series similarity search, reflecting a trend towards high-performance, fault-tolerant systems for modern hardware. Best Paper Award, PPoPP 2022 Marie-Curie Individual Fellowship FSMP Distinguished Professor Fellowship ACM Distinguished Speaker Featured ACM Member Dr. Fatourou has a strong record of leadership and mentorship. She has advised numerous MSc and PhD students and is actively recruiting for her PERSIST project, funded by HFRI. She has led major research initiatives, including the EU-funded TransForm network and the GreenVM project. Her leadership extends to the broader community, where she has served as Past Chair of the ACM Europe Council, Chair of the Greek ACM-W Chapter, and on the steering committees of PODC and OPODIS. She has also co-organized key events on gender equality in computing, such as the Summits on Gender Equality in Computing (GEC) and the WISTEM Summer School. She is deeply involved in the academic service, frequently serving on program committees for premier conferences such as SPAA, NETYS, and SRDS. Her laboratory and research team at FORTH ICS and the University of Crete focus on building the theoretical underpinnings and practical implementations of next-generation concurrent and distributed systems.

Christoph SchäferView profile

Researcher

• Computational Physics
• High Performance Computing
• Smoothed Particle Hydrodynamics
• +6 more

Christoph Schäfer is a German astrophysicist at the University of Tübingen, affiliated with the Institute for Astronomy and Astrophysics and its Computational Physics department. His work focuses on numerical particle methods, particularly Smoothed Particle Hydrodynamics (SPH), to model planetary formation, collisions, and protoplanetary disc dynamics. University of Tübingen, Institute for Astronomy and Astrophysics, Computational Physics Expert in SPH simulations, high-performance computing, and planet formation Developed the miluph and miluphcuda SPH codes Dr. Schäfer's research spans impact physics, binary star systems, and dust dynamics. His work has been applied to missions like DART and includes modeling Mercury's formation, circumbinary disc interactions, and collisional dust production. He teaches computational astrophysics, numerical methods, and advanced astronomy courses, with a strong emphasis on hands-on programming exercises. His lab ( Numerical Particle Methods Group ) develops GPU-optimized simulation tools, and he has contributed to comparative studies of disc-planet interactions and small body dynamics in the solar system. He maintains an ORCID profile and is recognized in the astronomical community via the asteroid (5364) Christophschafer , discovered in 1980. Contact: ch.schaefer@uni-tuebingen.de (professional) or cm.schaefer@gmail.com (private).

Amirali BaniasadiView profile

Professor

• Computer Architecture
• Low-power Design
• Power-aware Architectures
• +4 more

Amirali Baniasadi is a Professor and Graduate Advisor in the Department of Electrical & Computer Engineering at the University of Victoria. He holds a BS from Tehran University, an MS from Sharif University, and a PhD from Northwestern University, with professional engineering (PEng) certification. His research focuses on low-power design, power-aware architectures, VLSI interconnect, and high-performance processors, with applications extending to GPU optimization and machine learning hardware integration. Education: BS, Tehran University MS, Sharif University PhD, Northwestern University Research Interests: Development of energy-efficient processor architectures GPU architecture optimizations and performance analysis Machine learning applications in hardware design and signal processing Quantum annealing for signal processing challenges Security and reliability in network-on-chip systems Recent Trends in Publications: Dr. Baniasadi’s work increasingly integrates AI and deep learning with traditional hardware domains, such as using neural networks for phase unwrapping in SAR imaging, lightweight edge detection networks, and explainable AI frameworks for signal-based models. His research also explores GPU design space optimizations, power-aware architectures, and security against thermal attacks in NoCs. Advising & Grants: Supervised over 15 graduate students, including prominent alumni now in academic and industry roles (e.g., Assistant Professors at Lakehead and Syracuse Universities). Active in securing funding for interdisciplinary projects combining computer architecture with emerging fields like quantum computing and healthcare AI. Labs & Teams: Leads a research group focusing on high-performance computing and low-power systems, collaborating with industry partners on GPU optimization and embedded processor design.

• Parallel Computing
• High-Performance Computing (HPC)
• Programming Models
• +6 more

Maria Luisa Gil Gómez is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture and the Barcelona Higher Technical School of Telecommunications Engineering. She holds a PhD in Computer Science and leads research in parallel computing, high-performance computing (HPC), and programming models. Her work focuses on optimizing computational workflows, fault tolerance in distributed systems, and leveraging multi-GPU architectures for scientific simulations. She has contributed to projects like the Programming Models group (PM) at UPC and collaborates with the Barcelona Supercomputing Center (BSC-CNS). Affiliations: UPC Department of Computer Architecture, BSC-CNS, PM Research Group Research Interests: Parallel Programming Models, HPC, GPU Computing, Cellular Automata, Education Technology Her research spans over 147 documented activities, including journal articles, conference presentations, and funded projects. Notable contributions include studies on BCI in music education, distributed algorithms for ARM-based clusters, and the OpenCAL++ framework for parallel cellular automata simulations. She has also explored educational methods integrating creativity and game-based learning in engineering.

Thomas HobigerView profile

Professor

• Satellite Navigation
• Inertial Navigation
• Sensor Fusion
• +7 more

Thomas Hobiger is a Professor and Head of the Institute of Navigation at the University of Stuttgart, Germany, and serves as Dean of Studies for Geodesy and Geoinformatics under Faculty 6: Aerospace Engineering and Geodesy. His research focuses on satellite navigation, inertial navigation systems, sensor fusion, and geodetic applications such as precise orbit determination and GNSS meteorology. He leads a team developing high-precision positioning solutions for autonomous vehicles and airspace surveillance systems. Hobiger holds a PhD from Vienna University of Technology (2005) and has held tenured positions at institutions including Chalmers University of Technology (Sweden) and Japan’s National Institute of Information and Communications Technology (NICT). Notable awards include the AGU Geodesy Section Award (2018) and the NVIDIA Global Impact Award (2016 finalist). He is actively involved in editorial roles for journals like Journal of Geodesy and holds leadership positions in international geodetic organizations. Research interests span navigation hardware/software development, real-time kinematic positioning, and interdisciplinary projects combining GNSS with meteorology and oceanography. His work emphasizes practical applications through collaborations with industry and space agencies, such as JAXA and ESA.

• Theoretical Computer Science
• Distributed Computing
• Approximation Algorithms
• +4 more

Mohit Daga is a Researcher at KTH Royal Institute of Technology, Sweden, specializing in theoretical computer science, distributed computing, and algorithmic research. He holds an MS in Computer Science from IIT Madras (2018). His current role involves research engineering and supervising master thesis projects across Machine Learning, AI, and GPU Programming. Education: MS in Computer Science, Indian Institute of Technology Madras (2018) Research Interests: His work focuses on distributed algorithms, approximation methods, and dynamic systems. He has explored applications in network analysis, optimization, and medical imaging. Recent projects include developing consensus protocols for DAG-based systems and analyzing multi-shot broadcast stability in distributed networks. Awards: Google Summer of Code Scholarship (2020) Advising & Grants: Supervised 9 master’s theses since 2020. Research internships include Cystar Labs (IIT Madras), IMDEA Networks Lab, and Xerox Research. His work on resource allocation at Xerox led to a patented solution. Labs/Teams: Contributed to projects at KTH’s Department of Computer Science and collaborated with IMDEA Networks and Cystar Labs. Part of the team developing the AciSpace CubeSat payload integration system.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +5 more

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

Carsten GriwodzView profile

Professor

• Computer Networks
• Distributed Systems
• Network Security
• +5 more

Carsten Griwodz is a Professor in the Department of Informatics (IFI) at the University of Oslo, specializing in digital infrastructure and security. He leads the Distributed Infrastructure and Security (DIS) research group and contributes to several specialized labs including the Sustainable Immersive Networking Lab (SINLAB), Imagine Beyond 5G Blockchain Lab, and the AliceVision Association. Professor at Department of Informatics, University of Oslo Section leader for DIS: Distributed Infrastructure and Security Group member in Networks and Distributed Systems (ND) Active in sustainable immersive networking and blockchain labs Co-founder of AliceVision open-source photogrammetric framework His research focuses on network performance, edge computing, and immersive technologies. He has pioneered work in cloud gaming QoE, real-time 3D reconstruction, and low-latency systems. Current projects explore redirected walking in VR, GPU programming for tracking, and sustainable networking solutions. Recent publications (2021-2024) demonstrate his expertise in network delay analysis, point cloud compression, virtual reality environments, and GPU-accelerated systems. His work bridges computer science fundamentals with cutting-edge applications in multimedia, security, and health informatics. He supervises numerous master's theses covering diverse topics from wireless streaming challenges to medical imaging advancements, consistently mentoring on topics related to network optimization, immersive technologies, and GPU computing. Supervised 40+ master's theses (2004-2024) Thesis topics span network performance, GPU programming, VR systems, and multimedia processing Active in both theoretical and applied research domains Mentoring interests include HCI, distributed systems, and real-time processing Contributes to education through research-led supervision As a permanent faculty member with extensive publication records and active research groups, Griwodz maintains significant influence in academic circles through both his technical contributions and educational mentorship.

Sergio PirozzoliView profile

Professor

• Computational Fluid Dynamics
• Turbulence
• High Performance Computing
• +4 more

Sergio Pirozzoli is a Full Professor at the Department of Mechanical and Aerospace Engineering , Sapienza University of Rome. His research focuses on Computational Fluid Dynamics (CFD) , Turbulence , High-Performance Computing , and Supersonic/Hypersonic Flow modeling. He specializes in developing advanced numerical solvers for compressible and turbulent flows, with applications to aerospace, thermal engineering, and environmental systems.

Christopher James HenryView profile

Associate Professor

• Machine Learning
• Deep Learning
• Domain Adaptation
• +5 more

Christopher James Henry is an Associate Professor in the Department of Computer Science at the University of Manitoba , Faculty of Science . He also serves as an Adjunct Professor in the Department of Applied Computer Science at the University of Winnipeg . Roles: Associate Professor, Department of Computer Science, University of Manitoba Adjunct Professor, Department of Applied Computer Science, University of Winnipeg Research Focus: Machine learning and deep learning, particularly in digital agriculture and remote sensing Domain adaptation and generative image models General-purpose GPU computing (GPGPU) and image analysis Labs & Teams: Co-founder of the Applied Parallel Computing and Collaborative Research Laboratory Co-leader of the TerraByte research group , specializing in embedded/robotic systems for agricultural dataset creation

• Big Data Processing
• Heterogeneous Computing
• GPU Acceleration
• +4 more

Maria Xekalaki is a Research Associate and PhD student in the School of Computer Science at the University of Manchester, affiliated with the Advanced Processor Technologies research group. Her work focuses on accelerating Big Data stacks using heterogeneous hardware resources while optimizing cost and energy efficiency. SDG Contributions: Sustainable Development Goals (SDGs) related to energy efficiency and poverty eradication Research Interests: Her research spans heterogeneous computing, GPU acceleration, Java application optimization, and energy-efficient Big Data processing. She explores techniques for leveraging RISC-V vectorization, improving cross-language interoperability, and advancing GPU-accelerated Fully Homomorphic Encryption (FHE) systems. Article Trends: Her publications emphasize transparent acceleration of Java programs, FHE optimization for privacy-preserving machine learning, and heterogeneous resource management. Key technologies include TornadoVM, OpenCL, and FPGA integration with Big Data frameworks.