جست‌وجوی استاد و پژوهشگر

• Computer Science
• Algorithms
• Graph Theory
• +۴ مورد دیگر

Virginia Vassilevska Williams is a Professor at the Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science (EECS), affiliated with MIT CSAIL. She earned her Ph.D. in Computer Science from Carnegie Mellon University in 2008 and held postdoctoral positions at the Institute for Advanced Study (Princeton), UC Berkeley, and Stanford. Education: B.S. in Mathematics and Engineering from Caltech (2003); Ph.D. in Computer Science from CMU (2008) Her research focuses on combinatorial and graph-theoretic approaches to computational problems, including shortest paths , pattern detection , fine-grained complexity , and computational social choice for analyzing election manipulation and tournament structures. Recent publications highlight advances in sparse graph algorithms , cycle detection , and approximate counting using matrix multiplication techniques. She co-organized programs at the Simons Institute (2023) and Dagstuhl Seminars (2016). Scientific Awards NSF CAREER Award Google Research Fellowship Alfred P. Sloan Research Fellowship Thornton Family Faculty Research Innovation Fellowship Invited Speaker at ICM 2018 She advises current Ph.D. students including John Kuszmaul , Yael Kirkpatrick , and Zixuan Xu . Former students like Amir Abboud (Weizmann Institute) and Nicole Wein (U. Michigan) have achieved academic and industry positions.

Salma Emaraمشاهده پروفایل

استادیار

• Machine Learning
• Computer Networking
• Natural Language Processing
• +۳ مورد دیگر

Salma Emara is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto within the Faculty of Applied Science & Engineering. Her academic journey includes a B.Sc. in Electronics and Communications Engineering from the American University in Cairo (2018) and a Ph.D. in Computer Engineering from the University of Toronto (2022), supervised by Professor Baochun Li. Her research spans two domains: (1) technical work in reinforcement learning for computer networking , including adaptive bitrate selection, edge caching, and congestion control; and (2) pedagogical work focused on debugging skill development for beginner programmers and leveraging natural language processing in engineering education . She emphasizes hands-on learning through in-class activities and problem-solving assignments. Publication trends reveal a focus on reinforcement learning in networking (2018–2023) and a parallel interest in educational technology (2024). Her recent work (e.g., TextCraft ) explores NLP-driven resource recommendation for textbooks, while earlier projects (e.g., Cascade , Pareto ) address network optimization through machine learning. Scientific awards include: Faculty of Applied Science & Engineering Early Career Teaching Award (2025) Departmental Teaching Awards (2022–2024) Shortlisted for TATP Teaching Assistant Excellence (2022)

• Fluid Dynamics
• Computational Fluid Dynamics
• Hypersonic Flows
• +۵ مورد دیگر

Giuseppe Pascazio serves as a Full Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His research spans computational fluid dynamics with dual focus on aerospace applications and biomedical engineering, particularly in hypersonic flow phenomena and microwave ablation technologies for cancer therapy. His primary research interests include fluid dynamics, computational methods for high-enthalpy flows, thermochemical non-equilibrium modeling, turbulent boundary layer analysis, and biomedical device optimization. Pascazio develops advanced numerical techniques including high-order schemes, state-to-state kinetics implementations, and GPU-accelerated solvers to address complex flow physics in atmospheric entry and medical applications. His work bridges fundamental gas dynamics with practical engineering solutions for spacecraft thermal protection and minimally invasive cancer treatments. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) High-fidelity simulation of hypersonic flows with detailed chemistry using state-to-state kinetics, (2) Development of robust numerical methods for shock-capturing in thermochemically non-equilibrium flows, and (3) Biomedical applications focusing on microwave ablation probe design and microcapsule transport in vascular systems. His aerospace work emphasizes atmospheric reentry physics while biomedical research targets cancer therapy optimization. Pascazio has participated in significant research projects including "PrInCE" (Innovative Processes for Energy Conversion) and "INNOVHEAD" (Advanced technologies for reduction of polluting emissions in Heavy Duty engines). His collaborative work involves industrial partnerships in aerospace and medical device sectors, though specific grant details beyond project names aren't provided. He maintains active research output with over 50 publications demonstrating consistent contributions to high-speed aerodynamics and biomedical fluid dynamics.

Edgar Vadimovich Vatamanitsaمشاهده پروفایل

استادیار

• Client-server programming
• Java and Android pattern development
• Application information systems
• +۶ مورد دیگر

Edgar Vadimovich Vatamanitsa serves as an Assistant Professor in the Department of Computer Science at Chernivtsi National University, specializing in cross-platform software development and cloud computing applications. His work bridges academic research with industry practice through active engagement in both university teaching and commercial software development. Education: Yuriy Fedkovych Chernivtsi National University (2013): Systems Software, Software Engineer Dr. Vatamanitsa's research centers on Java/Android pattern development, client-server architectures, and distributed systems. He pioneers practical implementations of design patterns in cloud environments, with significant contributions to optical field simulation, semiconductor modeling, and intelligent transportation systems. His methodology emphasizes cross-platform efficiency and real-world applicability of theoretical computing concepts. Analysis of his 2022-2024 publications reveals dominant themes in cloud-based optical computation (AWS infrastructure), Java pattern implementation for Android, and cross-platform solutions for traffic management and semiconductor physics. His work consistently integrates distributed systems with domain-specific challenges in optics, printing technology, and educational analytics, demonstrating exceptional versatility across engineering disciplines. Professional Activities: Member of Bukovina Information Technology Cluster (since 2022) Senior Software Developer at EPAM SYSTEMS Certificate in Cross-platform Programming and Information Security (Ternopil National Technical University, 2023) Tech Summer Bootcamp for Teachers participant (2023)

Nikolaos Papaspyrouمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Formal Verification
• +۴ مورد دیگر

Nikolaos Papaspyrou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and a member of the Software Engineering Laboratory . His research focuses on the theory and implementation of programming languages, including semantics, type systems, compilers, static analysis, and formal verification. Since October 2021, he has been on leave from NTUA, working as a Software Engineer for Google in the memory management team for the V8 JavaScript and WebAssembly engine. He previously served as Director of the Division of Computer Science (2017-2019) and was on sabbatical with Google's compiler group in Munich (2015-2016). His work includes the RELEASE project (EU FP7 STREP) for reliable large-scale server software and uncertainty handling in distributed databases (European Social Fund). Ph.D. and Diploma in Electrical and Computer Engineering from NTUA M.Sc. in Computer Science from Cornell University His research interests span programming languages , software engineering , and formal verification , with recent publications on coinductive proofs in Liquid Haskell, concurrency semantics, and quantum compilation. He has supervised over 50 diploma projects and mentored numerous students now at institutions like MIT, Princeton, and UC Berkeley. Awards include conference organizing and program committee roles, though no formal scientific prizes are listed.

Matthias Baitschمشاهده پروفایل

استاد

• Construction Informatics
• Numerical Methods
• Structural Optimization
• +۳ مورد دیگر

Matthias Baitsch serves as Professor of Construction Informatics and Numerical Methods in the Department of Civil and Environmental Engineering at Bochum University of Applied Sciences, where he concurrently heads the BIM Institute. His academic trajectory includes research assistant and senior engineer roles at Ruhr-University Bochum (2000-2009), academic coordination at the Vietnamese-German University (2009-2012), and an acting professorship at the University of Kassel (2012-2014). His educational foundation comprises: Civil Engineering studies at the University of Dortmund (1991-1997) under the interdisciplinary "Dortmund Model" Doctorate from Ruhr-University Bochum (2003) on geometric imperfection-based optimization of compressive beam structures Professor Baitsch's research integrates computational mechanics with civil engineering practice, specializing in construction informatics, numerical optimization, and high-order finite element methods. His work pioneers distributed optimization frameworks, structural health monitoring for wind energy infrastructure, and BIM-based construction informatics. Key methodological contributions include hp-FEM implementations, parallel optimization algorithms, and mobile structural analysis tools. Analysis of his recent publications reveals three dominant research trajectories: (1) Advanced numerical methods for structural optimization under uncertainty, (2) Health monitoring-driven lifetime prediction for wind turbine systems, and (3) Computational modeling of tunnel environments using viscoacoustic inversion techniques. These threads demonstrate consistent focus on robust numerical implementations and real-world civil engineering applications. As Head of the BIM Institute, he leads institutional efforts in digital construction technologies, fostering industry-academia collaboration on building information modeling standards and applications. His teaching portfolio spans foundational mathematics, numerical methods, and computer science for civil engineering students, emphasizing practical computational skills.

• Research Data Management
• Data Governance
• Data Sharing Practices
• +۳ مورد دیگر

Chris Wiley serves as the Physical Sciences and Engineering Research and Data Services Librarian and Associate Professor at the University of Illinois at Urbana-Champaign. Based at the Grainger Engineering Library, he focuses on data management, research practices, and digital accessibility across scientific disciplines. Research Interests: Specializing in data governance and stewardship across engineering and physical sciences Advancing FAIR data principles and open repository systems Developing accessible data visualization tools Researching data privacy frameworks for scientific contexts Exploring cloud storage limitations for academic institutions Creating educational resources for data management transitions Contact: Email: cawiley@illinois.edu Phone: 217-300-5801 Location: Grainger Engineering Library, 1301 W. Springfield Ave., Urbana, IL 61801

• Computer Architecture
• Parallel Computing
• Compiler Technology
• +۳ مورد دیگر

Keiji Kimura is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering. He earned his Doctor of Engineering from Waseda University and has held academic positions at the university since 1999, progressing from Research Associate to Assistant Professor (2004-2005), Associate Professor (2005-2012), and Professor (2012-present). He is affiliated with multiple professional organizations including ACM, IEEE Computer Society, The Institute of Electronics, Information and Communication Engineers, and Information Processing Society of Japan. His research focuses on computer architecture, particularly parallel computing systems and compiler technology. Kimura has made significant contributions to the development of the OSCAR (Optimally Scheduled Advanced Multiprocessor) automatic parallelizing compiler framework. His work spans multiple areas including multicore processor architecture, power reduction techniques for embedded systems, non-volatile memory systems, and parallelization methods for heterogeneous architectures. His research interests specifically include Multiprocessor Architecture and Parallelizing Compiler development, with applications in real-time systems and energy-efficient computing. Analysis of his recent publications reveals a strong focus on practical implementations of parallel computing technologies across diverse hardware platforms including RISC-V, ARM, and heterogeneous multicore systems. His work demonstrates a consistent trajectory from theoretical compiler development toward practical applications in embedded systems, security, and non-volatile memory technologies. The publications show increasing emphasis on RISC-V architecture, persistent memory programming, and power-efficient computing solutions. MEXT Award for Science and Technology (Research category), 2014.04 Ministry of Education, Culture, Sports, Science and Technology (MEXT) Kimura has served on numerous prestigious conference program committees including PACT, IPDPS, HPCA, and LCPC. His research has been supported through collaborations with major technology companies and government initiatives such as the METI/NEDO project entitled "Multicore Technology for Realtime Consumer Electronics." His work with the OSCAR compiler framework has demonstrated significant performance improvements and power reductions in real-world applications. He leads research in the APAL laboratory (http://www.apal.cs.waseda.ac.jp/) at Waseda University, focusing on advanced parallel processing technologies. His team works on compiler-directed approaches to solve challenges in heterogeneous multicore architectures, with particular emphasis on making parallel programming more accessible while optimizing for both performance and power efficiency. Current research directions include RISC-V secure boot verification, non-volatile memory systems, and GPU-based persistent memory solutions.

Mian M. Hamayunمشاهده پروفایل

دانشیار

• Computer Science
• Transaction Level Modeling (TLM)
• MPSoC Simulation
• +۱۵ مورد دیگر

Mian M. Hamayun is an Associate Professor of Computer Science and Head of Subject Group at the University of Birmingham Dubai campus, part of the College of Engineering and Physical Sciences. He holds a PhD in System Modelling, Simulation & Virtualization from the University of Grenoble Alpes, France, and has academic qualifications from Joseph Fourier University and COMSATS Institute of Information Technology, Pakistan. PhD in System Modelling, Simulation & Virtualization, University of Grenoble Alpes, 2013 MS in Informatics (Parallel & Distributed Systems), Joseph Fourier University, 2009 MSE in Software Engineering (Gold Medalist), COMSATS, 2005 PGCHE, University of Birmingham, 2021 FHEA and SFHEA, University of Birmingham (2021, 2024) His research spans Transaction Level Modeling (TLM) , MPSoC simulation , cloud computing , virtualization , Internet of Things/Vehicles , edge AI , and TinyML . He focuses on performance estimation, binary translation, and system-level simulation for embedded and distributed systems. The recent publications reflect a strong trend in IoT and vehicular networks , secure telemedicine , cloud orchestration , and embedded system simulation . His work integrates virtualization with security, real-time constraints, and machine learning on edge devices. Senior Fellow of the Higher Education Academy (SFHEA), 2024 Fellow of the Higher Education Academy (FHEA), 2021 Dr. Hamayun has supervised multiple research students and contributed to projects involving fatigue detection, person re-identification, retinal image analysis, and secure surgery frameworks. He has taught core computer science courses including Operating Systems, Networks, Data Structures, Algorithms, and Functional Programming at both undergraduate and postgraduate levels. His prior roles include Assistant Professor at NUST SEECS, R&D Engineer in France, and Lecturer at IUT1. He leads research in virtual prototyping and edge intelligence at the Dubai campus, contributing to smart city and cognitive systems initiatives. His lab work involves hybrid simulation environments, secure automotive platforms, and distributed IoT systems.

Mitra Nasriمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۴ مورد دیگر

Mitra Nasri is an Assistant Professor at the Eindhoven University of Technology, affiliated with the College of Engineering's Department of Electrical Engineering. She contributes to the High Tech Systems Center and EAISI Foundational, focusing on interconnected resource-aware intelligent systems. Research Focus: Real-Time Systems, Scheduling Algorithms, Embedded Systems, Fault-Tolerant Computing, and Cyber-Physical Systems. Key Contributions: Development of scheduling frameworks for multi-rate task chains, response-time analysis techniques, and containerization strategies for real-time distributed applications. Her recent work includes advancements in weakly-hard timing constraints, parallel global scheduling, and cloud integration for embedded systems. She actively collaborates on projects like SAM-FMS and COMP4DRONES. Scientific Awards: Best Paper Award - RTAS 2022 Best Paper Award - RTNS 2016 Outstanding Paper Awards at RTAS 2017, 2022 and RTSS 2020 She teaches courses in Real-Time Systems, Operating Systems, and Automotive Software, and participates in organizing conferences like Embedded Systems Week and CompSys.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +۷ مورد دیگر

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

Anrin Chakrabortiمشاهده پروفایل

استادیار

• Cryptography
• Systems Security
• Cloud Computing Security
• +۷ مورد دیگر

Anrin Chakraborti is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago, affiliated with the College of Engineering. His work bridges cryptography, systems, and theory to build privacy-preserving technologies for real-world challenges. Ph.D. in Computer Science from Stony Brook University (2020) Postdoctoral Researcher at Duke University B.Tech in Computer Science from Jadavpur University, India Chakraborti’s research focuses on three core areas: Secure Cloud Computing: Developing cryptographic mechanisms to protect data confidentiality and integrity in cloud environments against side-channels and malicious providers. Privacy in Collaborative Systems: Designing tools for private information sharing in cyber-physical systems and collaborative learning pipelines, including autonomous vehicle communication. Deniable Communication & Storage: Creating storage and messaging systems resistant to coercion, backdoors, and key compromise through plausibly deniable encryption. His publications demonstrate a consistent focus on privacy-enhancing technologies, oblivious protocols, and secure storage solutions. Notable contributions include Wink (deniable secure messaging), INVISILINE (plausibly-deniable storage), and ORAM-based protocols for cloud data privacy. He teaches graduate courses like CS 588 (Security & Privacy in Networked Systems) and CS 594 (Applied Cryptography), emphasizing hands-on implementation of secure systems.

• High performance computing
• Computational Science and Engineering
• Scientific computing
• +۷ مورد دیگر

Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.

Paul Bealeمشاهده پروفایل

استاد

• Theoretical Physics
• Statistical Mechanics
• Thermodynamics
• +۹ مورد دیگر

Paul Beale is a Professor in the Department of Physics at the University of Colorado Boulder, where he has been a faculty member since 1984 and currently serves as Chair of the Department. He holds a Ph.D. in Physics from Cornell University (1982) and a B.S. from the University of North Carolina at Chapel Hill (1977). His academic career has been centered at CU Boulder, where he advanced from Assistant to Associate and then to full Professor. Ph.D., Physics, Cornell University, 1982 B.S., Physics, University of North Carolina at Chapel Hill, 1977 Paul Beale's research is in theoretical condensed matter physics, with a focus on statistical mechanics and thermodynamics. His work spans a broad range of topics including phase transitions, critical phenomena, ferroelectrics, hysteresis, grain boundary kinetics, and Monte Carlo methods. He has made significant contributions to the exact calculation of energy distributions in the two-dimensional Ising model and developed a novel class of scalable parallel pseudorandom number generators based on Pohlig-Hellman exponentiation ciphers. His research integrates analytical theory, computational modeling, and applications in materials science and complex systems. His recent publications reflect a strong trend in computational and theoretical physics, particularly in developing robust algorithms for simulation and advancing fundamental understanding of phase behavior in soft and condensed matter systems. His work on pseudorandom number generation has implications for high-performance computing and statistical sampling. He has also contributed to physics education research, focusing on curriculum transformation and quantum mechanics instruction. Notable scientific contributions include: Co-author of the textbook Statistical Mechanics (with R.K. Pathria), a standard graduate-level reference. Development of exact methods for the 2D Ising model partition function. Innovation in scalable pseudorandom number generation for parallel computing. Paul Beale has advised numerous students and collaborated widely, though specific advisees are not listed. He has held significant administrative roles, including Director of the Honors Program and Associate Dean for Natural Sciences. He maintains active research through publications, code development, and academic leadership, with no indication of retirement. He is associated with the following labs and research groups: Condensed Matter Laboratory (former Director, 1999–2001) Statistical Physics and Computational Modeling Group (implied through research topics and code repositories)

Alessandro Fogliمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Data Analytics
• Cluster Resource Management
• +۴ مورد دیگر

Alessandro Fogli is a PhD Student at Imperial College London in the Department of Computing, affiliated with the Large-Scale Data & Systems (LSDS) Group . His research focuses on systems support for data analytics in cloud environments, including distributed systems, resource management, and query processing. Education PhD in Computer Science, 2019–Present, Imperial College London MSc in Computer Science, 2015–2017, Roma Tre University BSc in Computer Science, 2012–2015, Roma Tre University His research spans Distributed Systems , Databases , Data Analytics , and Modern Hardware . Recent work examines chiplet-based processor architectures and runtime mapping systems, with applications in performance optimization and hardware-aware query execution. Scientific Contributions Co-developed CHARM (2025), a runtime mapping system for chiplet heterogeneity Published in VLDB (2024) on OLAP processing for chiplet-based CPUs Contributed to HeatWave at Oracle Labs, improving query offloading to in-memory accelerators