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Daisaku YokoyamaView profile

Assistant Professor

• Parallel and Distributed Processing
• Combinatorial Search
• Game Tree Search
• +4 more

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.

• Building Information Modeling
• Digital Twinning
• Information Management
• +5 more

Prof. Dr.-Ing. Sebastian Esser serves as Group Lead for Information Management at the Chair of Computing in Civil and Building Engineering at Technical University of Munich. His research focuses on advancing Building Information Modeling (BIM) methodologies, particularly in infrastructure and railway applications. He contributes significantly to international standardization efforts including IFC-Road and IFC-Rail projects, and leads research initiatives such as RIMcomb and BauPuls360. Dr. Esser's research spans several critical areas in digital construction: Graph-based version control systems for BIM collaboration Digital twin development for infrastructure management Semantic modeling of built environments BIM-based regulation checking for railway infrastructure Interdisciplinary model coordination techniques Knowledge representation in civil engineering His work bridges theoretical computer science with practical civil engineering applications, focusing on improving data interoperability and workflow efficiency in construction projects. Analysis of his recent publications reveals a strong emphasis on graph-based approaches to BIM challenges. His research has evolved from foundational work on BIM programming interfaces to sophisticated implementations involving knowledge graphs, semantic reasoning, and digital twin architectures. Key trends include increasing integration of semantic web technologies with BIM standards, development of specialized query interfaces like GraphQL for construction data, and application of formal methods to infrastructure modeling problems. Dr. Esser actively supervises numerous bachelor's and master's theses annually, with recent topics covering graph-based entity alignment, BIM-GIS integration for flood assessment, incremental model updates, and digital twin implementations. His teaching portfolio includes courses such as Bau- und Umweltinformatik, BIM.fundamentals, BIM.infra, and Semantic Modeling of the Built World, demonstrating his commitment to educating the next generation of digital construction professionals. He is involved in multiple research initiatives including DFG FOR 5672 (The information backbone of robotized construction), SPP 2187 (Adaptive modularized constructions), and AM2PM (Additive to Predictive Manufacturing). His laboratory work spans the BIM-Lab and related computational infrastructure supporting his research in digital construction technologies.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +3 more

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

• network security
• network measurement and analysis
• cloud computing
• +4 more

Dr. Kuai Xu serves as a Professor of Computer Science at Arizona State University's School of Mathematical and Natural Sciences within the New College of Interdisciplinary Arts and Sciences. He maintains affiliations with the Center for Cybersecurity and Trusted Foundations, focusing on securing critical network infrastructures. His academic credentials include: Ph.D. in Computer Science, University of Minnesota (2006) M.S. in Computer Science, Peking University, China (2001) B.S. in Computer Science, Peking University, China (1998) Dr. Xu's research centers on network security , network measurement , and IoT systems , with significant contributions to smart home security, network behavior analysis, and social media epidemiology. His work bridges theoretical modeling and practical implementations for real-world network challenges. Recent publications (2022-2025) reveal a concentrated focus on IoT security frameworks, machine learning-driven activity inference from device traffic, and pandemic surveillance through social media analysis. Key themes include DNS security hardening, attack graph modeling, and privacy-preserving spatial analytics in smart environments. Through courses like ACO 331 (Network Forensics Analysis) and ACO 361 (Secure Coding Concepts), Dr. Xu mentors students in cybersecurity fundamentals while supervising research via ACO 399 and individualized instruction (ACO 499). His teaching integrates cutting-edge research into classroom applications. As an active affiliate of ASU's Center for Cybersecurity and Trusted Foundations, Dr. Xu contributes to interdisciplinary initiatives addressing emerging threats in home networks, IoT ecosystems, and social media platforms.

Tinghao FengView profile

Assistant Professor

• Time Series Analysis
• Data Visualization
• Machine Learning
• +3 more

Tinghao Feng serves as an Assistant Professor at Appalachian State University, specializing in time-oriented data analysis and visualization systems. His research develops comprehensive tools for exploring temporal patterns across environmental science, healthcare, and finance domains through innovative data mining algorithms and user-friendly software applications. His primary research interests focus on Time Series Analysis and Data Visualization, with significant contributions to Machine Learning applications. He investigates methods for visualizing complex temporal data, developing pattern recognition algorithms, and creating interactive analysis frameworks applicable to environmental monitoring, clinical informatics, and financial forecasting systems. Recent publications reveal a strong emphasis on visual analytics for temporal data, spanning environmental applications (honey bee monitoring, landslide prediction), healthcare informatics (patient messaging systems, pandemic modeling), and remote sensing. Key developments include the TimePool system for univariate time series querying and EVis for environmentally driven event analysis, demonstrating cross-disciplinary impact. Dr. Feng maintains active demonstration systems including TimePool for temporal query visualization and Polynomial Similarity Dependency Trees for linguistic structure analysis, reflecting his commitment to translating research into practical analytical tools for complex data exploration.