جست‌وجوی استاد و پژوهشگر

• Data Management Systems
• Database Systems
• Operating Systems
• +۴ مورد دیگر

Prof. Jana Giceva Makreshanska is a Professor of Database Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. Her research focuses on systems support for data management, leveraging modern hardware architectures including operating systems, compilers, and computer architecture. She holds a PhD from ETH Zurich (2017) and previously served as faculty at Imperial College London. Notable awards include the ERC Starting Grant (2024), VMware Early Career Faculty Award (2019), and ETH Medal (2018). Education: PhD in Computer Science, ETH Zurich (2017) Research roles at Microsoft Research and Oracle Labs Research Interests: Her work addresses holistic system design challenges in data management, including optimizing query processing for modern hardware, compiler integration, and emerging architectures like chiplets. She explores efficient data processing techniques for big data and transactional systems. Recent Contributions: Her recent work includes innovations in disaggregated systems (HotOS 2023), chiplet-aware scheduling (2025), and universal graph data structures (VLDB 2022). Her publications span topics like NVMe array optimization, FPGA-based partitioning, and hybrid OLTP/OLAP systems. Awards: ERC Starting Grant (2024) Best Paper Runner-Up at VLDB 2022 ACM SIGMOD Highlights Award (2023) Teaching & Service: Current courses include 'Cloud-Based Data Processing' and 'Data Processing on Modern Hardware'. She chairs diversity initiatives at SoCC 2023, serves on program committees for OSDI, VLDB, and SIGMOD, and leads gender equality efforts at TUM. Labs/Teams: Maintains the Lehrstuhl III (I3) database systems group at TUM, collaborating on projects like LakeVilla and ARCAS.

Joakim Åströmمشاهده پروفایل

مدرس

• Fire Safety Engineering
• Combustion Science
• Emission Factors
• +۳ مورد دیگر

Joakim Åström is a Lecturer and Doctoral student at the Division of Fire Safety Engineering, Lund Institute of Technology (LTH), Lund University. His work contributes to UN Sustainable Development Goals related to sustainable cities and communities. He specializes in experimental fire dynamics, oxygen reduction systems, and combustion analysis. Research Interests: Fire Safety Engineering, Combustion Science, and Aerosol Dynamics. His recent studies focus on oxygen concentration effects in fires, emission scaling in built environments, and fire survivable cable systems. Collaborations include projects on warehouse oxygen reduction systems and fire toxicity assessments. Recent Articles: 5 publications (2022–2024) in journals like Fire Safety Journal and Fire Technology, plus conference papers. Grants: Led the 'Review of Oxygen Reduction Systems for Warehouse Storage applications – Phase 2' (2020–2024). Labs/Teams: Active in the LTH Profile Area: Aerosols and the Division of Fire Safety Engineering research group.

Kevin Tangمشاهده پروفایل

استاد

• Networks
• Control and Optimization of Computer Networks
• Control Theory
• +۸ مورد دیگر

Kevin Tang is a Professor in the School of Electrical and Computer Engineering at Cornell University, where he has been a faculty member since 2007. His research spans networks, control, optimization, and game theory, with a strong focus on the control and optimization of computer networks. He is affiliated with the Department of Electrical and Computer Engineering and leads a research group focused on information networks and systems. Ph.D., Electrical Engineering, California Institute of Technology, 2006 M.S., Electrical Engineering, California Institute of Technology, 2002 M.S., Electronics Engineering, Tsinghua University, 2001 B.E., Electronics Engineering, Tsinghua University, 1999 Professor Tang's research interests include networks, control theory, optimization theory, and game theory , with applications in computation systems, information theory, and decision systems. His work bridges theoretical foundations with practical implementations in networking and distributed systems. His recent publications show a strong trend in network optimization, privacy-preserving systems, and control of networked systems , appearing in top venues such as IEEE/ACM Transactions on Networking, SIAM Journal on Optimization, NeurIPS, and ICML. Themes include network coding, multipath transport, synchronization, and mathematical optimization for network performance. Notable scientific awards include: Presidential Early Career Award for Scientists and Engineers (PECASE), 2012 IBM Faculty Award, 2018 ACM Senior Member, 2019 First Place in AT&T SDN Network Design Challenge, 2016 Michael Tien '72 Excellence in Teaching Award, 2011 He has advised students such as J. Cheng and S. Tseng, and has been actively involved in research grants and projects related to networked systems and control. From 2016 to 2019, he served as Director of Graduate Studies for the ECE department at Cornell. His research group, accessible at networks.ece.cornell.edu , investigates the interplay between economics, control, and networking in modern distributed systems.

• Computer Science
• Parallel Computing
• Data Preservation
• +۳ مورد دیگر

Dr. Hongnai Zhang is an Assistant Professor of Marketing at the University of La Verne's College of Business, within the Marketing and Law department. His research focuses on computational methods, parallel computing, data preservation, machine learning, and power systems. He has contributed to advancements in Hessian-based simulation techniques, sensor network efficiency, and deep learning models. His work spans interdisciplinary fields, including collaborations on chemical dynamics simulations and energy-efficient data dissemination mechanisms. Key research interests include optimizing algorithms for high-performance computing, improving data storage in low-duty-cycle sensor networks, and developing stable recurrent neural networks for power load forecasting. His publications reflect expertise in computational science, parallel processing, and applied mathematics, with notable contributions to journals like the Journal of Chemical Physics and IEEE conferences.

Ying Liمشاهده پروفایل

دانشیار

• Wireless Sensor Networks
• Drone Networks
• Ad Hoc Networks
• +۴ مورد دیگر

Ying Li is an Associate Professor of Computer Science at Colby College, located in Waterville, Maine. She holds a Ph.D. in Computer Science from the University of New Hampshire, and M.Eng. and B.Eng. degrees from Hubei University of Technology in China. Her research focuses on wireless networks, particularly in energy-efficient communication strategies for resource-limited environments such as drone networks, ad hoc networks, and delay-tolerant networks. She has contributed to protocols for improving network connectivity, data dissemination, and mission-oriented systems. Professor Li teaches courses including Computer Organization, Programming Languages, and Advanced Computer Networks. Her work emphasizes practical solutions for challenges in network security, service availability, and computing efficiency. Education: Ph.D. in Computer Science, University of New Hampshire M.Eng. in Applied Technology, Hubei University of Technology B.Eng. in Computer Science and Technology, Hubei University of Technology Research Interests: Dr. Li’s research spans Wireless Sensor Networks , Drone Networks , Ad Hoc Networks , and Delay-Tolerant Networks . She explores energy-efficient trajectory planning, multi-agent reinforcement learning for collaborative drone operations, and protocols to enhance reliability in intermittently connected systems. Recent efforts include studies on erasure coding for IoT communication and containerized network performance evaluation. Her work often bridges theoretical algorithm design with real-world network challenges. Grants & Advising: Professor Li has advised numerous students on research projects, including work on energy-aware routing, multi-agent systems, and drone replacement strategies. She collaborates on grants related to network optimization and resilience, though specific funding details are not detailed in the provided texts. Labs & Teams: While no dedicated lab is explicitly named, her research involves experimental setups in Colby’s Computer Science department, focusing on simulations and prototyping for network protocols and drone systems.

Abbas El Gamalمشاهده پروفایل

استاد

• Network Information Theory
• Digital Imaging Devices
• Field-Programmable Gate Arrays (FPGAs)
• +۳ مورد دیگر

Abbas El Gamal is the Hitachi America Professor in Stanford University's School of Engineering and a Senior Fellow at the Precourt Institute for Energy. He holds a PhD in Electrical Engineering from Stanford (1978) and has been a faculty member since 1981. His research focuses on network information theory, digital imaging, and FPGAs. Key roles include Department Chair (2012-2017) and Director of the Information Systems Laboratory (2003-2012). Awards include the 2016 Hamming Medal and 2012 Shannon Award. He co-founded companies like Pixim and Inscopix, commercializing imaging technologies. Education: B.Sc. Honors, Cairo University (1972) M.S. Statistics, Stanford (1977) Ph.D. Electrical Engineering, Stanford (1978) Research Interests: El Gamal’s work spans network information theory (e.g., relay channels, interference), CMOS imaging sensors, and smart grid technologies. Notable contributions include the textbook *Network Information Theory* (2011), and patents in imaging and communication systems. Professional Contributions: Member, U.S. National Academy of Engineering President, IEEE Information Theory Society (2014) Recipient of over 30 patents and 230+ publications Industry Roles: Founded LSI Logic Research Lab, Actel, and Silicon Architects. Served as VP of Synopsys and advisor to biotech startups.

Luca Castelli Aleardiمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Discrete Mathematics
• Computational Geometry
• +۸ مورد دیگر

Luca Castelli Aleardi is an Assistant Professor at the Laboratoire d'Informatique (LIX) of École Polytechnique, France, since September 2008. His research focuses on theoretical computer science, computational geometry, and discrete mathematics, particularly on Schnyder woods , graph drawing , and compact data structures for planar and surface-embedded graphs. PhD in Computer Science from École Polytechnique (2006) DEA in Algorithmique from Université Paris 6 (2003) Laurea in Applied Mathematics from University of Milan (2002) Erasmus exchange at Université de Versailles-St. Quentin (1999) His work spans graph encoding , mesh compression , and higher genus triangulations , with applications to data visualization, geometric algorithms, and network analysis. Recent publications in SoCG and GD emphasize practical implementations of theoretical constructs, such as toroidal Schnyder woods and SCARST data structures . He has supervised 15+ research internships (2008–2024) on topics including planar graph drawing and dynamic Laplacian systems . Software tools developed by his team include Jcg (Java library for computational geometry) and SCARST implementations.

Afid Nur Kholisمشاهده پروفایل

پژوهشگر

• Computational Hydrology
• Environmental Informatics
• Software Development for Simulation
• +۳ مورد دیگر

Afid Nur Kholis is a Researcher at the Helmholtz Centre for Environmental Research - UFZ in the Department of Environmental Informatics . His work focuses on Scientific 3D Visualization , Virtual Reality applications , and software development for environmental simulations , particularly through contributions to the open-source OpenGeoSys and OGSTools platforms. He collaborates on computational hydrosystems , reactive transport modeling , and environmental data integration . Research Interests: Primarily centered on multi-layered domain boundary rendering , groundwater flow modeling , and interactive visualization for hydrological systems. His projects address saline intrusion , radionuclide migration , and urban microclimate impacts . Publications: Recent work includes versions of OpenGeoSys (2025) and OGSTools (2025), with a focus on environmental informatics and open-source simulation frameworks . Earlier contributions (2023-2024) involve hydro-mechanical effects in clay rocks and multi-compartment water dynamics .

• Computational Neuroscience
• Multiscale Modeling
• Synaptic Plasticity
• +۱۲ مورد دیگر

David C Sterratt is a Lecturer in the School of Informatics at the University of Edinburgh, affiliated with the Institute for Adaptive and Neural Computation. He serves as Deputy Director of Learning & Teaching (Operations) and has been a faculty member since 2000. His academic background includes a PhD in computational neuroscience and an undergraduate degree in Physics. His research centers on computational neuroscience, particularly multiscale modeling of neurons, synaptic plasticity, and the development of neural topographic maps. He has developed key software tools such as Retistruct for reconstructing retinal anatomy and KappaNEURON for integrating stochastic rule-based models with deterministic neuronal simulations. His work bridges molecular-level biochemical networks and electrical activity in neurons, offering insights into how synaptic proteomes interact with neuronal dynamics. Dr. Sterratt's teaching focuses on data science, statistics, and sustainability. He co-designed and organizes the large second-year undergraduate course Informatics 2 - Foundations of Data Science and teaches the new course Modelling of Systems for Sustainability . He supervises undergraduate and postgraduate projects and is open to PhD supervision, currently co-supervising students Domas Linkevicius and Susana Román García. His recent publications reveal a strong trend in integrating multi-level biological data into computational models, particularly in retinotopic map formation, synaptic scaling, and familiarity memory. These works combine mathematical modeling, simulation, and analysis to address fundamental questions in neural development and function. Fellow of the Higher Education Academy (HEA) Dr. Sterratt has been actively involved in academic leadership and sustainability initiatives, having served as Energy Coordinator for the Informatics Forum from 2011 to 2019. He has secured research funding through collaborations and software development, and his textbook Principles of Computational Modelling in Neuroscience (with Bruce Graham, Andrew Gillies, Gaute Einevoll, and David Willshaw) is a key educational resource in the field. He is a dedicated mentor and educator, contributing significantly to curriculum development and student training. He is part of the Institute for Adaptive and Neural Computation, a leading research group in neural computation and machine learning, and collaborates extensively with researchers such as David Willshaw, Bruce Graham, and Arjen van Ooyen. His work continues to advance the integration of molecular, cellular, and systems-level neuroscience through computational approaches.

Drago Žagarمشاهده پروفایل

استاد

• Communication Networks and Protocols
• Codes and Coding
• Quality of Service in Networks
• +۷ مورد دیگر

Drago Žagar is a Full Professor at the Faculty of Electrical Engineering, Computer Science and Information Technologies (FERIT), University of Osijek, where he also serves as Head of Department. He has been permanently appointed since 2015 and has a long-standing affiliation with the institution since 1990, with brief periods at other units within the same university. His educational background includes a B.Eng. (1990), M.Sc. (1995), and Ph.D. (2002), all from the Faculty of Electrical Engineering and Computing in Zagreb. His research spans key areas in modern networking and information systems, including communication networks and protocols , quality of service , cybersecurity , and IoT applications in agriculture and logistics . His recent work emphasizes energy-efficient monitoring, anomaly detection using machine learning, smart labels for supply chains, and adaptive video streaming. The 15 most recent publications reflect a strong trend in applying sensor networks and data analytics to real-world problems in agriculture, network security, and multimedia systems. The interdisciplinary nature of his work bridges computer networks, industrial informatics, and smart technologies. He has led significant projects such as Smart Label for Measuring and Monitoring Product Storage , IoT Ecosystems for Agriculture , and Boosting Telecommunications Engineering Education under Erasmus+. He also managed national and EU-funded initiatives focused on broadband access in rural areas and IPv6 security. He has mentored numerous students and authored key educational materials, including laboratory manuals in computer networks and electronics. His teaching includes courses on Computer and Mobile Networks and Computer Systems Security . He is actively engaged in research, with publications in journals such as Sensors , Electronics , Journal of Industrial Information Integration , and Telecommunications Systems . His work is indexed in Google Scholar, Scopus, Web of Science, and CRORIS.

Maikel Lázaro PÉREZ GORTمشاهده پروفایل

پژوهشگر

• Computer Science
• Data Security
• Database Systems
• +۳ مورد دیگر

Maikel Lázaro PÉREZ GORT is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics (DAIS) at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity Communications and teaches advanced courses on relational data watermarking in doctoral programs. His primary research focuses on database security, specifically developing robust watermarking techniques for relational data integrity protection against attacks in untrusted environments. Teaching Activities: Doctoral Course (DM226/2021): 'Relational Data Watermarking: Fundamentals' and 'Robust Schemes' Past courses include 'Programming and Laboratory-2' for undergraduate Computer Science students and 'Lab of Human-Centered Design' for Digital Management programs. Research Interests: His work addresses challenges in data watermarking resilience, including topology optimization for watermark sources, query impact analysis, and prevention of additive attacks. Recent efforts emphasize earth observation data integrity and quantile-based distortion minimization techniques. Publications: Over 13 peer-reviewed articles from 2017-2025, focusing on watermarking methodologies for relational databases, earth observation systems, and forensic image analysis. Key themes include brute-force attack resilience, semantic-driven marking, and high-quality key generation schemes. Awards/Grants: No specific awards or grants mentioned in the provided materials. Labs/Teams: Affiliated with the DAIS department's research groups working on information systems security and complexity science.

• Information Theory
• Coding Theory
• Signal Processing
• +۳ مورد دیگر

Ingemar Ragnemalm is an Associate Professor at the Department of Electrical Engineering (ISY), Linköping University, affiliated with the Information Coding (ICG) research group within the Faculty of Science and Engineering. His work is centered on theoretical and applied aspects of information and coding theory. The research interests of Ingemar Ragnemalm include information theory, coding theory, signal processing, and digital communications. These areas are critical for reliable data transmission and storage systems, particularly in noisy environments. His affiliation with the Information Coding group suggests a strong focus on error-correcting codes and efficient data representation. No recent publications were listed in the available university search results, so trends in publication output cannot be determined at this time. No scientific awards or honors were mentioned in the available data. There is no publicly available information regarding student supervision, research grants, or advising roles. Similarly, details about laboratories, research teams, or collaborative projects are not accessible through the current university portal entries.

Guilherme B. Xavierمشاهده پروفایل

عضو هیئت علمی

• Quantum Communications
• Quantum Key Distribution
• Quantum Cryptography
• +۱۱ مورد دیگر

Guilherme B. Xavier is a Senior Associate Professor and Head of Division at the Information Coding Division (ICG), within the Department of Electrical Engineering at Linköping University, Sweden. He leads the Quantum Communications Laboratory, focusing on experimental quantum communications using optical fibers and integrated photonics. He previously served as an Assistant Professor at Universidad de Concepción in Chile, where he established a quantum communication lab. Research Interests: His work spans quantum key distribution , high-dimensional quantum information , entanglement distribution , multicore optical fibers , and integrated photonics for quantum technologies . He investigates fundamental quantum phenomena such as wave-particle duality and nonlocality, with applications in secure communication. Recent Research Trends: His latest publications (2024–2025) emphasize energy-time and time-bin entanglement verified in integrated photonics, loophole-free Bell tests , and the equivalence between entropic uncertainty and complementarity . Earlier work (2013–2020) focused on high-dimensional QKD, long-distance entanglement, and multicore fiber beam splitters for quantum information processing. Scientific Awards and Recognition: Coordinator of the SECRET project funded by the Swedish Research Council (SEK 1.4 million). Advising and Grants: He mentors several PhD students including Daniel Spegel-Lexne and Joakim Argillander. He has secured competitive research funding, notably coordinating the European SECRET project, which aims to enhance quantum communication security. His lab has developed secure quantum random number generators and high-dimensional beam splitters. Labs and Teams: He founded and heads the Quantum Communications Laboratory at Linköping University, established in 2018. The lab collaborates extensively with researchers across Europe and Latin America and is part of the Information Coding Division, working closely with experts like Jan-Åke Larsson and Alvaro Alarcón.

Weichen Guمشاهده پروفایل

استادیار

• Quantum Information
• Operator Algebra
• Number Theory
• +۳ مورد دیگر

Weichen Gu is a Zassenhaus Assistant Professor at The Ohio State University, specializing in quantum information, operator algebra, and number theory. His research bridges functional analysis, harmonic analysis, and mathematical physics, with a focus on quantum entropy, central limit theorems, and noncommutative structures. He holds a PhD from the University of New Hampshire and has taught at institutions including the University of Chinese Academy of Sciences. Research interests include quantum convolution, Möbius disjointness, and operator algebraic structures in factors. His work has been published in journals like Proceedings of the National Academy of Sciences and Journal of Operator Theory . Notable talks include presentations at JMM 2024 and Brigham Young University's Number Theory Seminar. Teaching experience spans over five years, with roles as instructor and teaching assistant for courses in multidimensional calculus, differential equations, and linear algebra. Active in academic communities, Gu collaborates with institutions like Harvard's Mathematical Picture Language Project and TAMU's Noncommutative Geometry Seminar.

Jiapan Guoمشاهده پروفایل

استادیار

• Machine Learning
• Pattern Recognition
• Medical Image Analysis
• +۴ مورد دیگر

Jiapan Guo is a Tenure Track Assistant Professor at the University of Groningen, affiliated with the Faculty of Science and Engineering's Intelligent Systems — Bernoulli Institute. His expertise lies in machine learning, pattern recognition, and medical image analysis. He holds dual roles as a Researcher in the Machine Learning Lab - Data Science Center in Health (DASH) and the Department of Radiation Oncology at the UMCG. His research focuses on applying deep learning to medical imaging challenges, particularly in oncology outcomes prediction and agricultural automation. Key areas of research include developing advanced algorithms for PET/CT image analysis, tumor segmentation, and real-time object detection in complex environments. He collaborates across disciplines to bridge computer science and healthcare, contributing to clinical decision support systems. Publications highlight innovations in multi-modal imaging, radiomics, and lightweight YOLO models optimized for agricultural applications.