جستجوی استاد راهنمای دانشگاهی

Mahdi Boloursaz Mashhadiمشاهده پروفایل

پژوهشگر

• Signal Processing
• Wireless Communications
• Machine Learning
• +۳ مورد دیگر

Mahdi Boloursaz Mashhadi is a researcher at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with the Information Processing and Communications Lab. He holds a Ph.D. in Electrical Engineering from Sharif University of Technology (2018), with prior research roles at the University of Central Florida and Queen's University. His expertise spans signal processing, wireless communications, machine learning applications in communication systems, and biomedical signal processing. Dr. Mashhadi's research focuses on massive MIMO channel state acquisition , deep learning-driven pilot design , and semantic communication frameworks . His recent work explores token-domain multiple access, generative AI integration in communication systems, and federated learning optimizations. He has contributed to foundational studies in sparse signal reconstruction (e.g., iterative adaptive thresholding methods) and wearable health monitoring via PPG signals. Key achievements : Best Paper Award at EWDTS 2012, multiple grants (IEEE, national/regional), and patents (e.g., US Patent 9729160). Current projects include semantic-aware power allocation in generative communications and latency optimizations in distributed deep learning frameworks. Labs/Teams : Member of the Intelligent Systems and Networks (ISN) group at Imperial, collaborating on AI-driven communication systems and edge computing solutions. His work bridges theoretical signal processing with practical implementations in 5G/6G networks, biomedical devices, and distributed machine learning ecosystems.

Ashok Srinivasanمشاهده پروفایل

استاد

• High-Performance Computing
• Supercomputing
• Massively Parallel Computing
• +۱۲ مورد دیگر

Dr. Ashok Srinivasan is the William Nystul Eminent Scholar Chair and Professor in the Department of Computer Science at the University of West Florida, within the Hal Marcus College of Science and Engineering. He is actively engaged in research, teaching, and professional leadership in high-performance computing and its applications to public policy and health. Education: Ph.D. in Computer Science, University of California, Santa Barbara, 1996 M.S. in Polymer Engineering, University of Akron, 1992 B.S. Tech (Honours) in Chemical Engineering, Regional Engineering College, Tiruchirapalli, 1987 Dr. Srinivasan's research focuses on high-performance computing, supercomputing applications, and simulation-based public policy analysis, particularly in modeling infection spread through air travel. His work integrates computational methods from quantum Monte Carlo, molecular dynamics, and GPU programming with real-world challenges in epidemiology and transportation. He leads Project VIPRA, a multi-institutional effort using supercomputers to analyze public health policies. His recent publications emphasize multiscale and parallel modeling of pedestrian and infection dynamics in transportation systems, leveraging massive computational power for policy-relevant insights. These works span disciplines including computer science, physics, public health, and urban planning. Scientific Awards and Recognitions: Fulbright Senior Research Scholar Best Paper Award at International Conference on Parallel Processing (ICPP) Featured among 12 major breakthroughs using Blue Waters supercomputer Dr. Srinivasan has served as PI or co-PI on approximately $4 million in grants from NSF, DOE, and DoD. He is a founding co-chair of the Student Research Symposium at HiPC, has organized over 15 professional events, and served on more than 40 international conference technical program committees. He actively mentors students and collaborates with researchers at institutions like IBM, Argonne National Lab, and Oak Ridge National Lab. He leads the Project VIPRA research team, which involves interdisciplinary collaboration across universities and domains such as biochemistry, mechanical engineering, and urban planning. His lab focuses on large-scale simulations for public policy impact.

• Parallel and Distributed Computing
• Cellular Automata
• Cognitive Robotics
• +۱۲ مورد دیگر

John-Thones Amenyo is an Assistant Professor in the Department of Mathematics & Computer Science at York College, City University of New York (CUNY), where he has served full-time since September 2008. He also held adjunct positions at the same institution from 2000 to 2008. He holds a PhD in Electrical Engineering from Columbia University and a BS in Electrical Engineering & Computer Science from MIT. PhD, Electrical Engineering, Columbia University MPhil, Electrical Engineering, Columbia University MS, Electrical Engineering & Computer Science, Columbia University BS, Electrical Engineering & Computer Science, MIT His research focuses on parallel and distributed computing, cellular automata, cognitive robotics, UAV drone systems for public health (especially malaria vector control), neuro-architectures, and educational technologies using Computer Algebra Systems. He has pioneered projects like MedizDroids and KOM for mosquito control and developed models such as CR/SARAMA for conscious robotics. His work bridges computer science, engineering, public health, and education. The most recent publications highlight trends in digital twins for enterprise automation, UAV-based landscape engineering, and serious games for STEM education. His articles span topics from neuronal CDMA to end-user parallel programming, reflecting a deep integration of biological inspiration with computational systems. York College - CUNY, Bridging the Gap Seminar (2015) CETL Title III STEP Grant (2007–2008) NSF Graduate Research Assistantship at Columbia (1986–1991) MIT UROP Awards (1977–1979, 1978) Bell Labs LUPT Grant (1981–1982) Dr. Amenyo has mentored numerous undergraduate researchers through programs like LSAMP and the York College Summer Research Program, guiding projects in biosensors, AI for healthcare, and drone technologies. He has secured grants from PSC-CUNY and NSF I-Corps, and has contributed to curriculum development, including modernizing CS/ISM programs and coordinating Math 119 on Computer Algebra Systems. He actively serves on college committees, advises student clubs like the Video Game Development Association, and organizes grant workshops. He leads research in intelligent systems for public health, including the MedizDroids project for mosquito control and innovations in wildfire prevention. His lab supports student-driven projects in robotics, IoT, and health informatics, fostering innovation in both academic and societal contexts.

George Ntavazlis Katsarosمشاهده پروفایل

پژوهشگر ارشد

• Wireless Communications
• MIMO Systems
• Open RAN
• +۴ مورد دیگر

George Ntavazlis Katsaros is a Research Fellow at the Institute for Communication Systems, University of Surrey, and affiliated with the 5G/6G Innovation Centre. His research project, Massively Parallel Non-Linear Processing Architectures for 6G Communications , focuses on architectural and algorithmic design of real-time, power-efficient MIMO PHY for future wireless systems and Open RAN, supervised by Konstantinos Nikitopoulos. Research Interests: Katsaros specializes in 6G wireless technologies, with emphasis on non-linear signal processing, Open RAN frameworks, and energy-efficient MIMO systems. His work bridges hardware-aware algorithm design and practical implementation challenges in 5G/6G networks, targeting scalability and real-time operability. Publication Trends: Recent articles (2022–2023) explore non-linear processing’s role in enhancing MU-MIMO throughput, reducing base-station hardware requirements, and mitigating power consumption in software-based 5G PHY layers. Key themes include Open RAN compliance, over-the-air validation, and carbon footprint optimization. Affiliations: He contributes to the 5G/6G Innovation Centre, a leading research hub advancing Open RAN standards and energy-aware PHY processing architectures.

Daniel Appelöمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Wave Propagation
• +۳ مورد دیگر

Professor Daniel Appelö is affiliated with Virginia Tech's College of Science and Department of Mathematics. He also participates in the Computational Modeling and Data Analytics (CMDA) program. Research focuses on numerical algorithms for differential equations in engineering and natural sciences Specializes in wave propagation (acoustic, elastic, electromagnetic) and quantum computing applications Develops artificial boundary conditions for unbounded domains His career includes faculty positions at Michigan State University (MSU), University of Colorado Boulder, and University of New Mexico (UNM), as well as postdoctoral work at Caltech and Lawrence Livermore National Laboratory (LLNL). At LLNL, he co-developed massively parallel methods for seismology and embedded boundary methods for wave equations. Scientific Awards Hans Werthen Prize postdoc at UNM Education: PhD in Numerical Analysis at NADA, KTH, under Gunilla Kreiss. Thesis work on perfectly matched layer (PML) methods established stability criteria for hyperbolic systems.

Boris Grotمشاهده پروفایل

استاد

• System architectures for datacenters
• Processor architecture and microarchitecture
• Memory systems and interconnection networks
• +۳ مورد دیگر

Boris Grot is a Professor holding the Personal Chair of Computer Systems and Architecture in the School of Informatics at the University of Edinburgh, where he leads research on optimizing large-scale datacenter efficiency through hardware-level innovations in server architectures, memory systems, and interconnect networks. His academic background includes a PhD in Computer Science from The University of Texas at Austin (thesis: scalability and quality-of-service in on-chip networks) followed by post-doctoral research at EPFL's Parallel Systems Architecture Lab. Research focuses on critical infrastructure challenges driven by three converging factors: mobile/embedded system constraints (battery, thermal, form factor), massive-data applications (search, social networking), and enterprise cloud migration. Key problem domains include: Re-inventing server architectures for specialized datacenter workloads Overcoming semiconductor voltage scaling limitations Addressing the 1.3% global energy footprint of datacenters (growing rapidly) Designing football-field-scale facilities handling 5-20 MW power loads He directs the EASE Lab investigating these challenges, emphasizing the economic and environmental urgency as datacenter deployment accelerates worldwide. No scientific awards or student advisees were specified in the source material, and grant details remain undocumented in the provided text.

• Numerical Relativity
• Gravitational Wave Astronomy
• Computational Astrophysics
• +۳ مورد دیگر

Zachariah Etienne is an Associate Professor in the Department of Physics at the University of Idaho , where he conducts cutting-edge research in Numerical Relativity and Gravitational Wave Astronomy . He holds a PhD in Physics from the University of Illinois (2009) and dual BS degrees in Mathematics and Physics from Indiana University (2003) . Etienne is actively involved in developing computational tools like NRPy+ and GRoovy to enhance simulations of compact binary systems and relativistic astrophysics phenomena. Education PhD, Physics, University of Illinois (2009) BS, Mathematics, Indiana University (2003) BS, Physics, Indiana University (2003) His research spans Numerical Relativity , General Relativity , and High-Performance Computing , with a focus on gravitational wave sources such as binary black holes , neutron star mergers , and direct collapse black holes . Etienne has contributed to the Einstein Toolkit ecosystem and projects like BlackHoles@Home , which aim to democratize access to numerical relativity simulations through open-source frameworks and consumer-grade hardware. Etienne’s work emphasizes code development, algorithmic efficiency, and collaborative research in numerical relativity. Projects like NRPyElliptic and superB highlight his efforts in solving elliptic equations and simulating high mass ratio black hole binaries, while his studies on debris disks and bar-mode instabilities explore astrophysical phenomena through computational models.

• Numerical Analysis
• High-Performance Computing
• Computational Mathematics
• +۴ مورد دیگر

Maciej Woźniak serves as a university professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, actively contributing to research and teaching staff while participating in the Computer Science Discipline Council and College of the Faculty. His office is located at D-17, ul. Kawiory 21, III, 4.58, with primary contact via macwozni@agh.edu.pl. His research spans computational mathematics and high-performance computing, specializing in parallel algorithms for numerical simulations including isogeometric analysis, finite element methods, and multi-frontal solvers. Key applications address environmental challenges like hail suppression and urban smog reduction, alongside biomedical modeling of tumor growth and airborne pathogen dispersion. Methodological innovations focus on GPU acceleration, shared-memory architectures, and efficient integration techniques for partial differential equations. Analysis of his 2019-2024 publications reveals consistent advancement in parallel computational frameworks for environmental and biomedical applications, demonstrating interdisciplinary impact through experimental validation of hail cannons, smog reduction technologies, and pathogen dispersion modeling during the COVID-19 pandemic. His work bridges theoretical algorithm development with real-world problem solving across physics, engineering, and life sciences. Scientific Awards: No specific awards were documented in the provided sources. Student advising, research grants, laboratory affiliations, and educational background details beyond his PhD, DSc, and Eng. titles remain unspecified in the available materials, though his extensive publication record suggests active mentorship and collaborative research leadership.

Daisaku Yokoyamaمشاهده پروفایل

استادیار

• Parallel and Distributed Processing
• Combinatorial Search
• Game Tree Search
• +۴ مورد دیگر

Daisaku Yokoyama is an Assistant Professor at the Institute of Industrial Science, University of Tokyo, where he works in Department 3 of the Kitsuregawa-Toyoda Laboratory. His research focuses on parallel and distributed processing, combinatorial search, game tree search, and other search processes. He is also involved in the development of "Gekisashi," a computer shogi (Japanese chess) player. His academic background includes: March 1998: Graduated from the Department of Electronic and Information Engineering, Faculty of Engineering, The University of Tokyo March 2000: Completed Master's course in Information Engineering at the University of Tokyo 2002.3: Graduated from the Doctoral Program in Information Engineering, Graduate School of Engineering, The University of Tokyo September 2006: Obtained a PhD in Science from the Graduate School of Frontier Sciences, University of Tokyo Daisaku Yokoyama's research interests primarily center around parallel and distributed computing systems, with a particular focus on combinatorial search algorithms and game tree search techniques. His work bridges theoretical computer science with practical applications, especially in the domain of computer shogi where he has developed "Gekisashi." Beyond game AI, his research has expanded into big data analytics, particularly in transportation systems where he analyzes passenger flows in metro networks and driver behavior using vehicle recorder data. His work demonstrates a consistent thread of applying parallel processing techniques to solve computationally intensive problems across various domains. Yokoyama's publication record shows a clear evolution from foundational work in parallel combinatorial optimization (PopKern library) to more applied research in computer shogi and eventually to big data applications in transportation systems. His early work established frameworks for parallel search algorithms, while more recent publications demonstrate applications of these techniques to real-world problems involving massive datasets from metro systems and vehicle recorders. His research consistently emphasizes the importance of domain-specific knowledge in optimizing parallel algorithms. His notable scientific achievements include: DBSJ Best Paper Award 2014 for "Application and Evaluation of a Bayesian-Based Monte Carlo Tree Search Algorithm to Shogi" Game Programming Workshop Excellent Paper Award (awarded twice) Throughout his career, Yokoyama has been actively involved in academic service, serving on editorial boards, program committees, and as an organizer for numerous conferences and workshops related to programming, parallel computing, and game AI. His work on the Gekisashi shogi engine represents a long-term research project that has evolved from basic search algorithms to sophisticated AI systems, demonstrating both theoretical rigor and practical implementation skills. He is part of the Kitsuregawa-Toyoda Laboratory at the Institute of Industrial Science, University of Tokyo, which focuses on advanced computing systems, database technologies, and large-scale data processing. The laboratory provides a collaborative environment for research spanning theoretical computer science to real-world applications in transportation analytics and game AI.

Juan Carlos Pichel Camposمشاهده پروفایل

استاد

• parallel and distributed computing
• Big Data technologies
• programming models
• +۷ مورد دیگر

Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.

• Bioinformatics
• Genome Informatics
• Computational Biology
• +۳ مورد دیگر

Dr. Kassian Kobert is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering, the Institute for Bioinformatics Infrastructure (BIBI), and the Center for Biotechnology (CeBiTec). His primary research base is within the Genome Informatics Group, where he focuses on computational approaches to genomic analysis and biological data processing. He maintains an active research presence with recent publications extending to 2022. Dr. Kobert's research interests center on bioinformatics and computational biology, with particular expertise in algorithm development for genomic data analysis. His work bridges computer science and biological applications, focusing on creating efficient computational methods for handling large-scale genomic datasets. His research spans phylogenetic inference, sequence alignment, viral evolution, and the development of specialized software tools for next-generation sequencing data processing. The interdisciplinary nature of his work connects computer science theory with practical biological applications, particularly in evolutionary biology and genomics. Analysis of Dr. Kobert's publication record reveals a consistent focus on developing computational methods for biological data analysis. His work demonstrates a trajectory from fundamental algorithm development to practical software implementation, with notable contributions including the PEAR read merger and ExaBayes phylogenetic analysis tool. His research shows strong emphasis on computational efficiency, particularly for handling the massive datasets generated by modern genomic technologies. The publications indicate expertise in both theoretical computer science aspects (algorithm design, computational complexity) and practical biological applications (viral evolution, phylogenetic analysis). Dr. Kobert appears to have significant involvement in software development for bioinformatics applications, with several publications describing tools that have become standard in genomic analysis workflows. His work on parallel computing approaches suggests engagement with high-performance computing environments necessary for modern genomic research.

Timothy E Reddyمشاهده پروفایل

دانشیار

• Gene Regulation
• Statistical Genetics
• Functional Genomics
• +۴ مورد دیگر

Timothy E Reddy serves as an Adjunct Professor in the Department of Biostatistics & Bioinformatics and Associate Professor in Molecular Genetics and Microbiology at Duke University. He is a core faculty member of the Duke Center for Statistical Genetics and Genomics and the Center for Combinatorial Gene Regulation, with appointments spanning biostatistics, molecular genetics, and biomedical engineering. His research integrates computational and experimental approaches to dissect gene regulatory mechanisms underlying complex diseases. Education: Ph.D. in Genetics, Boston University (2007) Dr. Reddy's research centers on noncoding genetic variation, epigenome editing, and statistical genomics. He pioneers CRISPR-based high-throughput functional assays to annotate regulatory elements and elucidate disease mechanisms in conditions like multiple sclerosis, schizophrenia, and polycystic ovary syndrome. His work bridges genome-wide association studies with mechanistic validation through epigenome editing and multi-omics integration, emphasizing combinatorial effects of regulatory variants. His recent publications demonstrate a cohesive trajectory in developing and applying functional genomics tools, particularly CRISPR-based epigenome editing screens and statistical frameworks for variant interpretation. Key themes include cell-type-specific regulatory networks, noncoding variant pathogenesis, and translational applications for complex disease therapeutics across neurological, metabolic, and immune disorders. Dr. Reddy leads multiple NIH-funded initiatives including the Duke FUNCTION Center (NHGRI), with current grants spanning CRISPR-based GWAS refinement (Open Philanthropy), noncoding variation in multiple sclerosis (NIAID), and schizophrenia genomics (NIMH). His collaborative projects involve institutions including Northwestern University and Mount Sinai, focusing on metabolic and reproductive disorders through integrated omics approaches. As co-director of the Duke Center for Statistical Genetics and Genomics, he drives interdisciplinary efforts to map combinatorial noncoding causes of disease. His lab develops cutting-edge epigenome editing technologies like HyperCas12a for multiplexed regulatory screens while maintaining active collaborations with clinical researchers for translational applications.

Francisco Facchineiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Nondifferentiable Optimization
• Complementarity Problems
• +۴ مورد دیگر

Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.

Edward P. O'Brien, Jr.مشاهده پروفایل

استاد

• Protein Folding
• Codon Usage
• Translation Quality
• +۴ مورد دیگر

Edward P. O'Brien, Jr. is a Professor of Chemistry at Penn State University. He is affiliated with multiple institutes, including the Institute for Computational and Data Sciences (ICDS), Penn State Cancer Institute, and Huck Institutes of the Life Sciences. His research focuses on protein folding, translation quality, codon usage, and molecular entanglement phenomena. Institution: Penn State University Department: Chemistry Key Research Areas: Protein Folding, Codon Optimization, Translation Dynamics, Structural Entanglement Recent Publications (2024-2025) reveal computational and experimental investigations into protein misfolding mechanisms, SARS-CoV-2 ACE2 interactions, and lasso entanglements in folded proteins. His work combines biophysical modeling with machine learning approaches to understand translation elongation speeds and molecular physiology. Grants & Projects include NSF-funded initiatives like the National Synthesis Center for Emergence in Molecular and Cellular Sciences (2024-2029) and interdisciplinary collaborations with researchers in climate modeling, cyberinfrastructure, and RNA biology. Current projects emphasize co-translational folding and massively parallel assays.

• Theoretical astrophysics
• Core-collapse supernovae
• Neutrino transport and oscillations
• +۳ مورد دیگر

Ryuichiro Akaho is a tenure-track Assistant Professor in the Faculty of Science and Engineering, School of Advanced Science and Engineering at Waseda University, Japan. He earned his B.S., M.S., and Ph.D. in physics from the same university and is internationally recognized for developing general-relativistic Boltzmann neutrino transport codes applied to core-collapse supernovae and neutron-star cooling. Education: Ph.D. in Science, Waseda University, 2021–2024 M.S. in Advanced Science and Engineering, Waseda University, 2019–2021 B.S. in Advanced Science and Engineering (Physics), Waseda University, 2015–2019 Research Interests: Akaho’s theoretical work centers on neutrino radiation hydrodynamics in extreme astrophysical environments. He investigates how neutrinos drive supernova explosions, how flavor oscillations modify neutrino spectra, and how fallback accretion affects late-time neutrino emission from protoneutron stars. His simulations incorporate full general relativity, multidimensional transport, and state-of-the-art nuclear physics inputs. His recent studies reveal the universality of collisional flavor instabilities and quantify the detectability of supernova neutrinos by experiments such as Super-Kamiokande. These results bridge nuclear physics, high-energy astrophysics, and multimessenger astronomy. Awards & Honors: Young Scientist Presentation Award, Asia Nuclear Physics Association, 2025 HPCI Achievement Award (高エネルギー天体現象中の量子運動論的ニュートリノ輸送シミュレーション), RIST, 2024 Grants & Projects: PI, JSPS Grant-in-Aid for Early-Career Scientists, “The fate of massive stars explored by the first principle calculations in general relativity with nuclear physics,” 2024–2029 Co-I (completed), JSPS grant on black-hole formation and Boltzmann neutrino transport, 2023–2024 Teaching & Service: Since 2024 he has taught “Science and Engineering Experiment 1A” and “Physics Exercise B” to undergraduates at Waseda. He holds leadership roles in the Astronomical Society of Japan, Japan Physical Society, and two national astrophysical consortia (理論天文学宇宙物理学懇談会 and 宇宙核物理連絡協議会).