جستجوی استاد راهنمای دانشگاهی

Colin Zarzyckiمشاهده پروفایل

دانشیار

• Meteorology
• Climate Dynamics
• Numerical Modeling
• +۷ مورد دیگر

Colin Zarzycki is an Associate Professor of Meteorology and Climate Dynamics at Penn State University, specializing in simulating extreme atmospheric phenomena and bridging weather-climate scale gaps. His work focuses on high-resolution climate modeling for tropical cyclones, snowstorms, atmospheric rivers, and other extremes, with applications in climate change impact assessment and hazard mitigation. Penn State Faculty since 2021 Former Project Scientist at NCAR (2014-2021) Ph.D. in Atmospheric Science (University of Michigan, 2014) Research interests include: Variable-resolution climate models for regional refinement Tropical cyclone dynamics in global climate frameworks Snowstorm tracking algorithms (ESTA software) Cyclone metrics (CyMeP package) Aerosol radiative forcing with spatial heterogeneity Recent publications span 2011-2024 , emphasizing tropical cyclone modeling (12+ papers), extreme weather detection algorithms (3 software tools), and climate change impacts on snowstorms. Key collaborations include NCAR and Columbia IRI partnerships. Software development highlights: Betacast - Hindcasting framework for CESM TempestExtremes - Extreme detection in climate data CyMeP - Tropical cyclone metrics package ExTraTrack - Extratropical transition analysis ESTA - Snowstorm tracking algorithm

Shantenu Jhaمشاهده پروفایل

استاد

• High-Performance Computing
• Distributed Computing
• Computational Science
• +۳ مورد دیگر

Shantenu Jha is a Professor of Computer Engineering at Rutgers University and Chair of the Department (Center) for Data Driven Discovery at Brookhaven National Laboratory. He leads the RADICAL Lab and the RADICAL-Cybertools project, focusing on middleware for large-scale scientific applications. His research bridges high-performance computing, data-driven science, and health informatics, collaborating with diverse fields like molecular sciences and high-energy physics. Education: Ph.D., Syracuse University (2004); M.Sc., IIT Delhi (1995). Research Interests : High-Performance and Distributed Computing Data-Intensive Science & Engineering Cyberinfrastructure for Science Health Computing (Personalized Medicine) Awards : NSF CAREER Award (2013) Chancellor’s Excellence in Research (2016) Rutgers Board of Trustees Fellowship (2014) Multiple best paper awards at SC/ISC conferences Grants & Funding : Supported by NSF, U.S. DOE, NIH, and UK EPSRC. Current projects include ExaWorks and quantum-HPC middleware development. Labs/Teams: PI of the RADICAL Lab, leading initiatives in FAIR principles, quantum computing integration, and autonomous laboratories.

Taehoon Kimمشاهده پروفایل

پژوهشگر

• Blockchain Technology
• Ethereum Virtual Machine (EVM)
• Smart Contracts
• +۶ مورد دیگر

Dr. Taehoon Kim is a Senior Research Associate at the Blockchain Centre at the University of Zurich, specializing in computational science, blockchain technology, and network analysis. His work bridges theoretical research with practical applications in complex systems. Education: PhD in Biosystems Science and Engineering His research focuses on blockchain dynamics, particularly EVM chains and smart contract development using Solidity. He also explores graph representation learning, network science, and high-performance computing solutions for data-intensive projects at the university's Blockchain and Distributed Ledger Technologies (BDLT) lab. Currently, no scientific awards or publications are listed in the provided text. Taehoon contributes to data observatory initiatives and integrates cloud technologies into his computational frameworks.

• Data Engineering
• Data Science
• Indoor Location-Based Services
• +۳ مورد دیگر

Tiantian Liu is an Assistant Professor in the Department of Computer Science at Aalborg University, under the Technical Faculty of IT and Design. Her research focuses on data engineering and systems, particularly in the context of indoor location-based services and data science. Research Interests: Her work spans data management, indoor positioning, spatiotemporal databases, and scalable systems. She applies techniques from data science and computer science to solve challenges in real-time data processing, data quality, and context-aware applications. The publication trend from 2020 to 2024 shows a consistent focus on data engineering, with topics including indoor LBS, query optimization, distributed systems, and data integration. Her research combines theoretical database principles with practical system implementations. Scientific Awards: Prize (1) Advising and Grants: While no formal students are listed, she has participated in externally funded research projects. She was a project participant in Data Management Foundations for Indoor LBS (2019–2021), contributing to foundational work in indoor data systems. Labs and Teams: She is a member of the Data Engineering, Science and Systems research group at Aalborg University, collaborating on data-intensive systems and applications.

Aijuan Dongمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +۷ مورد دیگر

Aijuan Dong is a Professor and Department Chair of Computer Science & Information Technology at Hood College, where she teaches both undergraduate and graduate courses. Her academic work spans a broad range of computer science disciplines, with strong emphasis on practical and theoretical aspects of computing education and research. Her research interests include artificial intelligence, machine learning, health informatics, database technologies, cloud computing, knowledge representation and inference, and computer science pedagogy. Currently, she focuses on designing and applying scalable deep learning models and causal reasoning approaches to analyze large and diverse datasets. Dr. Dong earned her Ph.D. in Computer Science from North Dakota State University and a Master of Science in Computer Science from Minnesota State University. These educational foundations support her interdisciplinary research and teaching excellence. While no recent articles are listed, her research trajectory suggests strong engagement with data-intensive AI methods, particularly in health and educational domains, combining deep learning with causal inference for robust insight generation. Scientific Awards: No awards listed. Dr. Dong advises students at both undergraduate and graduate levels and leads curriculum development as Department Chair. She has not disclosed specific grant funding, but her research areas suggest potential involvement in federally or institutionally supported projects related to AI in health and education. She is active in academic leadership and professional engagement, as evidenced by her departmental leadership role and presence on LinkedIn. While no formal lab or research team is mentioned, her focus on scalable models implies collaborative, data-driven research efforts.

Yu David Liuمشاهده پروفایل

استاد

• Software Systems
• Energy Efficiency
• Performance Optimization
• +۶ مورد دیگر

Yu David Liu is a Professor at the School of Computing, State University of New York at Binghamton. He received his Ph.D. from Johns Hopkins University under Scott Smith. Research interests include Software Systems Energy Efficiency Reliability Performance Optimization Security Unmanned Aerial Vehicles Data-Intensive Software Side-Channel Attack Mitigation His work spans runtime systems, compilers, and programming languages for cross-cutting concerns. Key trends in recent publications: 2025-2024 focus on TEE security , UAV regulation , and energy-aware data processing . Earlier works explore secure caches , Green JVM methods , and SLAM system bottlenecks . Scientific Awards NSF CAREER Award (2010) Google Faculty Research Award (2011) Outstanding Research Achievement Award (2018, SUNY Binghamton CS Dept) Outstanding Research Achievement Award (2019, Watson School) Advising: Mentored 11 Ph.D. students (including 2025 Distinguished Dissertation Award winner Timur Babakol) and 14 M.S./B.S. students. Current advisees include Kerem Arikan (Ph.D.), Joseph Raskind (Ph.D.), and Huaxin Tang (Ph.D.). Grants: Funded by NSF awards 2053391 and 2215016 . Former NSF grants include 1910532, 1815949, and 1823260. Labs: Leads the programming language group at SUNY Binghamton, collaborating on UAV software (JCopter, ICRA'21), energy-efficient systems (Vesta, PLDI'24), and security (TEE-SHirT, NDSS'24).

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +۷ مورد دیگر

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.

• Cloud Computing
• Resource Management
• Container Technology
• +۹ مورد دیگر

Veerle Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). She is affiliated with the Internet Technology and Data Science Lab and has been actively contributing to research in cloud computing, resource management, and related fields for over two decades. Her research interests focus on cloud computing technologies, including containerization, resource management strategies, multi-tenancy architectures, and migration of legacy systems to cloud environments. She has also explored applications in medical software, edge computing, and wireless sensor networks. Her work spans both theoretical frameworks and practical implementations, often validated through experimental testbeds like Raspberry Pi clusters. Dr. Ongenae's publication record shows a clear evolution from foundational mathematical work in the 1990s to contemporary cloud computing research. Her recent publications (2015-2024) demonstrate expertise in containerized cloud environments, resource allocation strategies, and the application of cloud technologies to various domains including medical software and educational frameworks. She frequently collaborates with researchers like Filip De Turck, Pieter-Jan Maenhaut, and Bruno Volckaert. Her work shows particular strength in bridging simulation with experimental validation, as evidenced by her Raspberry Pi testbed research. The publications reveal consistent contributions to both journal articles (A1 type) and conference papers (C1, P1 types), with a focus on practical implementations and performance evaluation. Her research has practical applications in medical software migration, educational technology, and distributed sensor networks, demonstrating the interdisciplinary nature of her work which connects computer science with healthcare, education, and industrial applications.

Nikolas Herbstمشاهده پروفایل

استاد

• Software Performance Engineering
• Cloud Computing
• Serverless Computing
• +۵ مورد دیگر

Nikolas Herbst is a Professor and Chair of Software Engineering at the Department of Computer Science, University of Würzburg. He leads research in Software Performance Engineering, High-Performance Data Processing, and Autonomic Computing. His work focuses on Cloud and Serverless Computing, Elasticity, and Time Series Analysis. He currently serves as JMU Chief Information Security Officer (CISO) and holds leadership roles in SPEC Research Groups and ICPE Steering Committees. Education: PhD in Computer Science (Karlsruhe Institute of Technology, 2018) Master of Computer Science (Karlsruhe Institute of Technology, 2012) Research Interests: His lab develops tools like CHAMELEON, TELESCOPE, and BUNGEE for cloud elasticity and performance analysis. He emphasizes benchmarking, resource demand estimation, and self-aware systems. Recent projects include real-time forest monitoring (ROOT) and serverless scientific computing (SOS). Teaching: Teaches Operating Systems, Performance Engineering & Benchmarking, and Self-Aware Computing at both undergraduate and graduate levels since 2012. Awards: 10 Year Most Impact Paper Award (ACM/SPEC ICPE 2023) SPEC Kaivalya Dixit Distinguished Dissertation Award (2019) IBM PhD Fellowship (2014) Grants & Projects: Coordinates DFG-funded projects like bidt-ROOT (2023–2026) and SOS (2025–2029). Leads development of open-source tools for cloud performance analysis, including WCF (Workload Classification & Forecasting).

Gabriel Kerekesمشاهده پروفایل

پژوهشگر ارشد

• Geodesy
• Terrestrial Laser Scanning
• Engineering Surveying
• +۵ مورد دیگر

Gabriel Kerekes serves as a Postdoctoral Researcher and Group Leader of 3D Data Acquisition and Monitoring at the Institute of Engineering Geodesy Stuttgart (IIGS), part of the Faculty of Aerospace Engineering and Geodesy at the University of Stuttgart. He is actively affiliated with the Cluster of Excellence IntCDC (Integrative Computational Design and Construction for Architecture), contributing to cutting-edge research in computational construction methodologies. Dr. Kerekes specializes in terrestrial laser scanning (TLS), engineering surveying, and 3D data acquisition systems. His research addresses critical challenges in point cloud processing, stochastic modeling of geodetic measurements, and robotic total station networks for real-time construction monitoring. He develops innovative solutions for deformation analysis, geometric quality control of bio-based building elements, and integration of multi-sensor systems in architectural applications, with significant contributions to biomimetic shell construction and infrastructure monitoring projects. No scientific awards or fellowships were documented in the provided materials. Dr. Kerekes has supervised ten Master's and Bachelor's students since 2018, with research spanning TLS intensity analysis, atmospheric error modeling, and robotic positioning systems. His grant-funded projects include RP 16-2 (Spider Crane Robotic Platform) and AP 7 (Integrated Space-Time Point Cloud Modeling) under the IntCDC cluster. As leader of the 3D Data Acquisition and Monitoring team, he drives advancements in geodetic measurement technologies for next-generation construction processes.

Josep Lluís Berral Garcíaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cloud Computing
• Deep Learning
• +۴ مورد دیگر

Josep Lluís Berral García is an Associate Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia · BarcelonaTech (UPC). He is actively engaged in teaching and research, with a strong focus on Artificial Intelligence, Cloud Computing, and sustainable computing practices. He leads innovative educational initiatives and is affiliated with the CROMAI research group and the Barcelona Supercomputing Center (BSC-CNS). Research Interests: Artificial Intelligence and Deep Learning Cloud and High-Performance Computing Resource Orchestration and Management Sustainable and Ethical AI AI Education and Pedagogy His recent research and teaching projects center on integrating sustainability and ethical responsibility into AI education, using active learning methodologies. The trend in his work shows a consistent focus on optimizing computing resources through AI, particularly in cloud and HPC environments, with increasing emphasis on environmental impact and responsible innovation. Scientific Awards: UPC Award for Quality in University Teaching 2025 (Teaching Initiative for Newly Recruited Professors) Advising and Grants: While specific students are not listed, his leadership in competitive R&D+i projects and innovation initiatives indicates active supervision and grant-funded research. His involvement in multiple competitive and non-competitive R&D projects demonstrates sustained funding and research leadership. Labs and Teams: He is a key member of the CROMAI (Computing Resources Orchestration and Management for AI) research group at UPC and maintains a strong collaborative link with the Barcelona Supercomputing Center (BSC-CNS), leveraging the MareNostrum supercomputing infrastructure for AI and systems research.

Dustin Richmondمشاهده پروفایل

استادیار

• Computer Architecture
• FPGA
• RISC-V
• +۴ مورد دیگر

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Kaikai Liuمشاهده پروفایل

دانشیار

• Intelligent and Autonomous Systems
• Mobile and Cyber-Physical Systems (CPS)
• Artificial Intelligence of Things (AIoT)
• +۱۲ مورد دیگر

Kaikai Liu is an Associate Professor and Cisco Corporate Chair Professor in the Department of Computer Engineering at San José State University, Charles W. Davidson College of Engineering. He is actively engaged in research, teaching, and leadership in intelligent and autonomous systems, cyber-physical systems, and AIoT. His work bridges academia and industry, with collaborations involving NSF, Intel, Cisco, Arista, and the Department of Health of Hawaii. Research Interests: Intelligent and Autonomous Systems Mobile and Cyber-Physical Systems (CPS) Artificial Intelligence of Things (AIoT) Smart Sensing and Data Mining Edge Computing and Next-Generation Communication Systems His research spans both theoretical and applied domains, focusing on real-world deployments in smart cities, emergency response, behavioral health, and assistive technologies. He emphasizes system prototyping, data-driven analytics, and human-centered design. Recent Research Trends: His recent publications and projects show a strong focus on integrating AI with edge computing and sensing, particularly in cooperative perception (RINGS), mental health (OHANA), disaster communication, and intelligent urban infrastructure. There is a clear trajectory toward multi-modal, data-intensive systems that operate in real-time and support societal resilience. Scientific Awards: IEEE SWC 2017 Best Paper Award IEEE SECON 2016 Best Paper Award ACM SenSys 2016 Best Demo - Runner-up 2020 College of Engineering Award for Excellence in Scholarship Faculty Mentoring Award for CSU Student Competition 2018 Apple WWDC Scholarship (2013, 2014) National Scholarship of China Multiple University of Florida and SJSU Research Professor Awards Advising and Grants: Dr. Liu has mentored student teams to national recognition, including a first prize in the 2017 Juniper SDN Throwdown. He has secured significant research funding as Principal Investigator from the National Science Foundation (NSF), Knight Foundation, Intel, Cisco, Arista, and the U.S. Department of Health. Notable grants include the five-year OHANA Center (SAMHSA), RINGS (NSF), and multiple industry-funded projects on edge AI and smart infrastructure. Labs and Teams: He leads a research group focused on intelligent systems and edge computing, with a history of developing full-stack prototype systems like the SJGateway. He has led teams in the NSF Big Learning Center and the OHANA Center of Excellence, fostering interdisciplinary collaboration across engineering, public health, and social sciences.

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.

Rodrigo Brunoمشاهده پروفایل

استادیار

• Systems
• Programming Languages
• Cloud Computing
• +۴ مورد دیگر

Rodrigo Bruno is an Assistant Professor at Instituto Superior Técnico (University of Lisbon) and Senior Researcher at INESC-ID Lisbon. His research bridges Systems and Programming Languages with a focus on optimizing language runtimes for cloud environments (Microservices, Serverless). He previously worked at Oracle Labs Zurich and ETH Zurich. Research Areas Systems Programming Languages Cloud Computing Serverless Computing Operating Systems Key projects include Shell (FCT-funded serverless platform), Graalvisor (polyglot runtime virtualization), and Naos (RDMA networking in Java). His work appears in top venues like NSDI, EuroSys, ATC, and SoCC. Scientific awards include the EdgeEmu Best Paper Award , Best Young Researcher 2022 (INESC-ID), and multiple teaching excellence recognitions. He has served on program committees for EuroSys, NSDI, ATC, and more. Rodrigo Bruno leads a dynamic team including PhD candidates Vasyl Lanko, Bernardo Ribeiro (co-advised), and MSc students like André Páscoa. His research integrates with production systems such as the V8 JS Engine and OpenJDK HotSpot JVM. Current teaching includes Cloud Computing and Virtualization , Computer Systems Engineering , and IT Infrastructure Management . His service spans organizing committees for SESAME, MoreVMs, and artifact evaluation for SOSP.