جست‌وجوی استاد و پژوهشگر

Katherine A. Yelickمشاهده پروفایل

استاد

• High-Performance Computing
• Programming Systems
• Parallel Algorithms
• +۴ مورد دیگر

Katherine A. Yelick is the Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and Vice Chancellor for Research at UC Berkeley. She holds a joint appointment as Senior Faculty Scientist at Lawrence Berkeley National Laboratory, where she previously served as Associate Laboratory Director for Computing Sciences (2010-2019) and Director of NERSC (2008-2012). Her research spans high-performance computing, programming systems, parallel algorithms, and computational genomics, with leadership roles in projects like ExaBiome and Bebop. Ph.D. in EECS from MIT (1991) M.S. in EECS from MIT (1985) B.S. in EECS from MIT (1982) Yelick specializes in parallel programming languages , automatic performance tuning , and exascale computing , co-inventing Unified Parallel C (UPC) and Titanium. Her recent work focuses on genomic data analysis , GPU-accelerated algorithms , and communication-avoiding techniques for large-scale scientific computing. Her publications reveal trends in bioinformatics , GPU parallelism , and exascale-ready frameworks , with keywords spanning computational biology, algorithms, and cloud computing. Awards include the ACM-IEEE Ken Kennedy Award (2015), National Academy of Engineering (2017), and AAAS Fellowship (2018). Leadership : ExaBiome (exascale microbiome analysis), Bebop (optimization group) Mentorship : Co-advising Alok Tripathy and Reginald Frank As a key figure in Lawrence Berkeley National Laboratory 's Computing Sciences division, she oversees NERSC, ESnet, and CRD, driving interdisciplinary applications in climate, fusion, and biomedical research.

Chonglin Zhangمشاهده پروفایل

استادیار

• Kinetic Theory
• Rarefied Gas Dynamics
• Hypersonic Flows
• +۸ مورد دیگر

Chonglin Zhang is an Assistant Professor in the Department of Mechanical Engineering at the University of North Dakota. His research focuses on kinetic theory-based particle simulation methods, particularly Direct Simulation Monte Carlo (DSMC) and Particle-in-Cell (PIC), applied to hypersonic flows, rarefied gas dynamics, aerothermodynamics, micro/nanoscale flows, and plasma physics using High Performance Computing (HPC). PhD : Aerospace Engineering and Mechanics, University of Minnesota (2013) MS : Aerospace Engineering and Mechanics, University of Minnesota (2010) BE : Engineering Mechanics, Beijing University of Aeronautics and Astronautics (2007) His research involves developing advanced DSMC simulation codes with adaptive mesh refinement for hypersonic and rarefied flows, including applications in planetary probe aerothermodynamics, nano-scale flow modeling, and tokamak plasma physics. Publications emphasize nonequilibrium energy transfer models, shock wave simulations, and validation against molecular dynamics data. Recent articles demonstrate expertise in DSMC/PIC algorithms, hypersonic non-equilibrium flows, and computational validation. Awards include the University of Minnesota Doctoral Dissertation Fellowship and multiple scholarships from Beijing University of Aeronautics and Astronautics. Doctoral Dissertation Fellowship (2012-2013) John & Jane Dunning Copper Fellowship (2009) Renmin Scholarship (2004-2006) Freshmen Scholarship (2003)

Christos Strydisمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Hardware Acceleration
• Reconfigurable Computing
• +۶ مورد دیگر

Dr. Ir. Christos Strydis is an Associate Professor jointly affiliated with the Erasmus Medical Center (Neuroscience department) and Delft University of Technology (Quantum & Computer Engineering department). He is also the founder and head of the Neurocomputing Laboratory and a senior member of the IEEE . His roles span academic research, engineering leadership at Neurasmus BV , and teaching at both institutions. BSc, MSc, and PhD in Computer Engineering from the Technical University of Crete and Delft University of Technology 2011–present: Post-Doctoral Researcher, Chief Engineer, and faculty member at Erasmus Medical Center 2016–present: Assistant and Associate Professor at Erasmus Medical Center Dr. Strydis’s research lies at the intersection of neuroscience and computer engineering , focusing on high-performance computing (HPC) , low-power embedded systems , and functional ultrasound (fUS) imaging . His work bridges hardware acceleration, reconfigurable computing, and brain simulations to advance neural implants and neuroimaging technologies. His recent publications highlight innovations in exascale FPGA architectures for brain simulations, AI chip performance analysis , real-time spike detection algorithms , and secure health data integration via cloud HPC. Themes span from hardware security to cerebellar signal processing, emphasizing scalability and energy efficiency. At Erasmus and Delft, he supervises PhD, MSc, and BSc students , teaches bachelor- and master-level courses , and leads grants funded by the ICT Delft Research Centre , Google Inc. , and national/EU projects. His lab, the Neurocomputing Laboratory , focuses on translating computational neuroscience into clinical applications.

Hang Liuمشاهده پروفایل

استادیار

• High-Performance Computing
• Graph Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. Hang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. He previously held faculty positions at Stevens Institute of Technology (2019-2022) and University of Massachusetts Lowell (2017-2019). His research focuses on high-performance computing systems leveraging GPUs, FPGAs, and SSDs for graph analytics, machine learning, and numerical methods. He holds a Ph.D. from George Washington University (2017) and a B.E. from Huazhong University of Science & Technology (2011). Education: Ph.D. Electrical and Computer Engineering, George Washington University, 2017 B.E. Software Engineering, Huazhong University of Science and Technology, 2011 Research Interests: Dr. Liu's work spans high-performance computing (HPC), graph algorithms, machine learning acceleration, and numerical methods optimization. He develops novel architectures and algorithms for efficient processing of large-scale graphs and ML models on heterogeneous hardware, with applications in scientific computing and data analytics. His systems research emphasizes algorithm-hardware co-design for FPGAs and GPUs to achieve energy-efficient performance. Awards: Notable achievements include the IEEE CS TCHPC Early Career Award (2022), NSF CAREER Award (2021), and multiple best paper awards. He leads projects funded by NSF and industry partnerships, including AWS and Lawrence Berkeley National Lab. Advising & Grants: Advisor for multiple award-winning senior design projects and research grants. His work on AWS GraphChallenge and Berkeley Lab SRP Fellowships demonstrates sustained industry-academia collaboration. Labs/Teams: Active in the Rutgers High-Performance Systems (RHPS) lab, focusing on scalable graph analytics and ML infrastructure. Collaborates with interdisciplinary teams on AI-driven scientific discovery initiatives like DeepSpeed4Science.

• Computational Biology
• Systems Biology
• Comparative Genomics
• +۴ مورد دیگر

Dr. John Parkinson is a computational biologist and Senior Scientist at the Hospital for Sick Children, with cross-appointments in the Departments of Biochemistry and Molecular Genetics at the University of Toronto. His research integrates computational systems biology and comparative genomics to study parasites, pathogens, and microbiomes, focusing on their evolutionary strategies and host interactions. Roles: Research Professor, Computational Biologist, Senior Scientist Collaborations: Works with clinicians, parasitologists, immunologists, and bioinformaticians Platforms: Utilizes SciNet (University of Toronto's HPC) His research spans three main areas: (1) computational and experimental analysis of parasite genomes, (2) microbiome dynamics in diseases like cystic fibrosis and IBD, and (3) evolutionary innovations in pathogens. Techniques include metagenomics, network biology, and constraint-based metabolic modeling. Key trends across his publications include cross-species genomic comparisons , metabolic pathway elucidation in parasites, and functional interrogation of microbiomes through metatranscriptomics. Collaborative work on apicomplexan parasites (Toxoplasma, Plasmodium) highlights evolutionary strategies for host persistence. Scientific Awards CIHR New Investigator (2006-2011) Ministry of Research and Innovation - Early Researchers Award (2007-2012) NATO Postdoctoral Fellowship (1995-1996) Wellcome Trust Mathematical Biology Fellowship (1991-1995) Trainees in the Parkinson Lab acquire expertise in machine learning , metagenomics , and bioinformatics pipelines , supported by access to high-performance computing and wet-lab facilities for Toxoplasma proteomics. The lab maintains active collaborations with clinicians and international sequencing consortia.

Gideon Gogoviمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Molecular Dynamics Simulations
• +۵ مورد دیگر

Gideon Gogovi is an Assistant Professor in the Department of Biostatistics and Health Data Science at Lehigh University, affiliated with the Health, Science & Technology (HST) Building. Previously, he served as an Assistant Professor of Data Science at the University of Houston – Downtown. His research spans computational sciences, bioinformatics, and interpretable machine learning. PhD in Computational Sciences and Informatics, George Mason University MS in Mathematics, West Virginia University MPhil in Applied Mathematics, Kwame Nkrumah University of Science and Technology BSc in Mathematics, University of Cape Coast Dr. Gogovi specializes in molecular dynamics simulations of viral proteins and soft matter, combining computational biology with explainable AI to study structural and thermodynamic properties. He also explores epidemiological compartmental models and complex data visualization via interactive dashboards. His work includes high-performance computing (HPC) and numerical methods for health science applications. Research articles highlight his contributions to tertiary structure modeling of the Rift Valley Fever Virus L protein (2019), molecular dynamics simulations of calcium/strontium (2020), and polymer-solvent interactions in polyacrylamide systems (2020). These studies reflect his focus on interdisciplinary computational approaches bridging materials science, virology, and data science. Contact: gig323@lehigh.edu | Office: HST 004

• Biomedical Simulation
• Finite Element Method (FEM)
• High Performance Computing (HPC)
• +۳ مورد دیگر

Maximilian Schuster is a Research Associate at the Chair for Computational Analysis of Technical Systems (CATS) at RWTH Aachen University, Germany, a position held since May 2019. His work focuses on computational modeling of biomedical systems, particularly cardiovascular applications involving mechanical circulatory support devices. Education: Master of Science in Mechanical Engineering, Technical University, Berlin (2016-2019) Study Abroad as Fulbright Fellow at University of Southern California (2017-2018) Bachelor of Science in Mechanical Engineering, University of Applied Sciences, Berlin (2013-2016) Dr. Schuster's research centers on simulating human cardiac physiology with implanted left ventricular assist devices (LVADs), investigating blood flow stagnation and fluid-structure interactions in the left ventricle. His methodological expertise spans Finite Element Method (FEM) implementations, High Performance Computing (HPC), and reduced-order modeling for complex biomedical systems. Current work extends to dynamic ventricular movement modeling to capture device-heart interactions. His 2021-2024 publications demonstrate consistent focus on LVAD hemodynamics, employing computational techniques to analyze washout efficiency, pulsatility effects, and mesh deformation strategies. Key contributions include stabilized FEM formulations, radial basis function interpolation for ventricular motion, and reduced-order modeling of blood flow dynamics. Scientific Awards: Fulbright Fellowship Dr. Schuster has supervised two theses: Nico Dirkes' seminar research on FEM-ODE coupling and Farshid Farroknezhad's master's investigation of LVAD flow patterns. His research is supported through CATS institutional funding and likely biomedical engineering grants focused on computational biomechanics and device optimization. As part of CATS' fluid-structure interaction work group within RWTH Aachen's Center for Computational Engineering Science, he collaborates on interdisciplinary projects advancing numerical methods for technical systems with emphasis on cardiovascular applications and medical device integration.

Jaspal Subhlokمشاهده پروفایل

استاد

• Educational Technologies
• AI in Education
• Parallel and Distributed Computing
• +۳ مورد دیگر

Jaspal Subhlok is a Professor of Computer Science and Electrical and Computer Engineering at the University of Houston. He served as Chair of the Department of Computer Science from 2010 to 2019. His research focuses on educational technologies, AI-driven learning tools (e.g., the Videopoints project ), and high-performance computing (HPC). He is also the co-founder and former CEO of Videopoints LLC (2016-2022). Subhlok's educational background includes a B.A. in Computer Science from the Indian Institute of Technology, Kharagpur, and a PhD from Rice University. His work emphasizes integrating technology into education, such as indexed captioned searchable videos and AI-generated lecture summaries. He has received multiple awards for teaching, research, and service from the University of Houston. His research team has developed tools like the High Performance Systems Lab (HPSL) and the Lecture Video Visual Objects (LVVO) Dataset . He has advised numerous PhD and MS students, focusing on topics ranging from parallel computing to AI in education. Subhlok also co-hosted the radio show Border Crossings from 2002 to 2008, blending his academic work with public engagement.

Narcis Fernandez Fuentesمشاهده پروفایل

دانشیار

• Bioinformatics
• Structural Bioinformatics
• Protein Design
• +۵ مورد دیگر

Narcis Fernandez-Fuentes is an Associate Professor at the Institute of Biological, Environmental & Rural Sciences (IBERS) at Aberystwyth University. He was previously a Lecturer at the Leeds Institute of Molecular Medicine (2007-2011) and a Research Associate at Albert Einstein College of Medicine (2004-2007). He holds an M.Sc. in Biotechnology and a Ph.D. in Computational Biology from Universitat Autonoma de Barcelona. His work spans Bioinformatics, Structural Bioinformatics, and Plant Bioinformatics, focusing on protein structure prediction, biomolecular interactions, and computational approaches to plant breeding. Protein design and structure-to-function relationships Peptide-based modulation of protein interactions Genomic analysis of abiotic stress responses in plants SNPs modeling and functional impact assessment Recent articles highlight expertise in computational peptide design for SARS-CoV-2, ulvan lyase characterization, and structural modeling of inflammasomes and protein families. His grants include projects funded by the Welsh Government, BBSRC, and Covestro. Marie Curie Training Fellowship EMBO and FEBS Short Term Fellowships Boehringer Ingelheim Fonds Fellowship

• Computer Architecture
• High-Performance Computing
• Hardware Transactional Memory
• +۷ مورد دیگر

Adria Armejach Sanosa is a Senior Lecturer in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing in Europe. His research spans computer architecture, high-performance computing, memory systems, and hardware acceleration for genomics and machine learning. PhD from UPC His research interests focus on optimizing computer systems for performance and efficiency, particularly in the areas of hardware transactional memory, cache optimization, RISC-V architectures, and acceleration of bioinformatics workloads. He investigates how to improve data movement, prefetching, and parallelism in large-scale heterogeneous systems. His work combines architectural innovations with practical implementations on real-world HPC platforms. The most recent articles reflect a strong trend towards high-performance computing for genomics, sparse data handling, and efficient hardware/software co-design. Topics include genomics benchmarking on ARM processors, tensor marshaling, RTL simulation scalability, and low-precision training for deep neural networks. These works demonstrate a consistent focus on bridging architectural research with real-world applications in science and AI. HiPEAC Paper Award 2024 HiPEAC Paper Award Armejach has advised several doctoral students, including J. Pavón, G. López, and J. Osorio. He has been involved in numerous competitive R&D+i projects such as Digital Autonomy for RISC-V in Europe, Laboratorio Zettaescala de Barcelona, and Genome Analysis Acceleration on HPC Architectures. These projects are often funded by national and European programs, indicating strong recognition and support for his research. He collaborates extensively within the CAP (High-Performance Computing) research group and with key figures like Miquel Moreto, Mateo Valero, and Osman Unsal. He is a member of the CAP research group and contributes to initiatives like the Laboratory for Open Computer Architecture and systems (RISC-V Chip Development) and the Barcelona Zettascale Lab. These labs focus on open hardware, European technology sovereignty, and next-generation supercomputing. His work on Metro-MPI for RTL simulation and hardware accelerators for databases highlights his contributions to both design automation and data-intensive computing.

Georgi Gaydadjievمشاهده پروفایل

استاد

• Reconfigurable Computing
• Advanced Computer Architectures
• Parallel Memory Systems
• +۷ مورد دیگر

Georgi Gaydadjiev is a Professor in Innovative Computer Architecture at the University of Groningen's Faculty of Science and Engineering. Previously, he held roles including Chair Professor at TU Delft and Chalmers University of Technology, and served as VP of Dataflow Software Engineering at Maxeler Technologies. He holds a PhD from TU Delft and has over 35 years of industry and academic experience, focusing on reconfigurable computing, high-performance systems, and energy-efficient architectures. His research spans embedded systems, fault tolerance, and scalable architectures. Education: MSc Electrical Engineering (TU Delft), PhD (TU Delft), studies at Voenmeh (Baltic State Technical University). Research interests include reconfigurable computing, advanced architectures, parallel systems, and HPC. He leads projects funded by EU, Google, and Swedish Research Councils, addressing exascale computing and customized hardware. His work has been recognized with awards like the CES Design Showcase (1999) and best paper awards at ICS'10 and WiSTP'07. He advises PhD students and oversees labs like Maxeler IoT-Labs BV, focusing on deploying dataflow technology beyond data centers.

Petr Kravchukمشاهده پروفایل

مدرس ارشد

• Quantum Field Theories
• Conformal Field Theories (CFTs)
• Conformal Bootstrap
• +۴ مورد دیگر

Dr. Petr Kravchuk is a Senior Lecturer in Theoretical Physics at the Department of Mathematics, King's College London, within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD from the California Institute of Technology (2018) and has held postdoctoral positions at the Institute for Advanced Study (2018–2021) and the Simons Center for Geometry and Physics (2021). His research focuses on non-perturbative aspects of Quantum Field Theories (QFTs), particularly Conformal Field Theories (CFTs), emphasizing the conformal bootstrap framework and numerical methods. He has received prestigious grants including the European Research Council (ERC) Consolidator Grant (2023–2028) and the Royal Society University Research Fellowship (2023–2025). Key research areas include the structure of CFT spectra (e.g., Regge trajectories, light-ray operators), automorphic forms in bootstrap equations, and applications of high-performance computing (HPC) to extract precise CFT data. His work bridges mathematical rigor with physical insights, addressing topics like AdS/CFT correspondence, asymptotic observables, and detector models in weakly coupled theories. Grants & Projects: CFTSPEC: Spectra of Conformal Theories (ERC Grant) Non-perturbative Approaches to Thermal CFTs (Royal Society) Awards: ERC Consolidator Grant (2023) Royal Society Fellowship (2023) His publications span topics from stress-tensor bootstrap calculations to fusion of conformal defects, with a focus on foundational advancements in CFT methodology. Collaborative work includes applications to AdS gravity and numerical implementations like the blocks_3d software package.

Paolo Bientinesiمشاهده پروفایل

استاد

• High-Performance Computing
• Numerical Linear Algebra
• Tensor Operations
• +۸ مورد دیگر

Paolo Bientinesi is a Professor at the Department of Computing Science, Umeå University, and Director of the High Performance Computing Center North (HPC2N). His research bridges theoretical and applied computer science, focusing on optimizing computational workflows through domain-specific innovations. Research Interests: Core Areas: Automatic generation of algorithms and code, numerical linear algebra, tensor operations, performance modeling, and computer music. Interdisciplinary Applications: Materials science, molecular dynamics, computational chemistry, computational biology, and computational physics. His work emphasizes leveraging architecture-specific and problem-specific knowledge to develop high-performance solutions. Publication Trends (2022-2026): Recent articles demonstrate a focus on mixed-precision computing, tensor decompositions, linear algebra algorithms (e.g., FLOPs optimization, matrix chains), and music information retrieval (e.g., automatic drum transcription, DJ cue points). Work frequently intersects with parallel computing, performance diagnostics, and machine learning. Leadership: Heads the research group High-Performance and Automatic Computing , driving projects in algorithm automation and computational efficiency.

Roman Iakymchukمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Parallel Programming
• High Performance Computing
• +۳ مورد دیگر

Roman Iakymchuk is an Associate Professor at the Department of Computing Science within Umeå University, Sweden. His work focuses on numerically reliable and sustainable algorithmic solutions, emphasizing accuracy, reproducibility, and energy efficiency in high-performance computing environments. Research Interests: Numerical linear algebra, parallel programming models, sustainable computing, and optimization of distributed systems. Teaching: Courses on scientific computing, CUDA/OpenMP/MPI-based parallel programming, and floating-point error correction. The articles highlight his contributions to mixed-precision computing, numerical reliability in Krylov solvers, and energy-efficient practices in HPC systems. His work spans algorithm design, tool-assisted implementation, and European collaborative projects like CEEC.

Octavio Castillo Reyesمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Geophysics
• Electromagnetic Modeling
• +۵ مورد دیگر

Octavio Castillo Reyes is a full-time Professor at the Department of Computer Architecture, Polytechnic University of Catalonia (UPC), and an Associate Researcher at the Barcelona Supercomputing Center (BSC) within the Wave Phenomena Group. He holds a PhD in Computer Architecture from UPC (2017) with an international distinction for his thesis on HPC-based electromagnetic geophysical modeling. His research focuses on high-performance numerical methods for geophysical applications, particularly in electromagnetic modeling and inversion using parallel computing frameworks. Education: PhD in Computer Architecture (UPC, 2017), M.Sc. in Networks and Telecommunications (UAV, 2011), B.Sc. in Computational Systems Engineering (ITSX, 2007). Research Interests: HPC algorithms, geoelectromagnetic modeling, parallel programming, edge finite element methods, and applications in geothermal energy and reservoir characterization. He leads the development of the PETGEM code, an open-source HPC tool for 3D CSEM modeling used in geophysical studies. His work bridges computational science and geophysics, emphasizing scalability and efficiency in supercomputing environments. Publications and Projects: Over 20 peer-reviewed articles, 60+ conference presentations, and multiple funded projects (e.g., Collinder Venture Builder Programme , AIR: Air for European Resilience ). His recent work addresses groundwater imaging, metallic infrastructure challenges, and renewable energy applications. Awards: JCC2015-BSC Prize for 'Your Thesis in Three Minutes,' National System of Researchers (CONACyT, Level I), and UPC's Excellence Cum Laude PhD Award. He is a member of the Mexican Supercomputing Network and certified by ANECA/AQU for teaching excellence.