Jeffrey F. Naughton is a Professor at the University of Wisconsin and works at Google Inc in Madison, WI, USA. His academic career spans decades with significant contributions to database systems, data mining, and privacy-preserving analytics. Research Interests : Distributed query processing and optimization Machine learning integration with relational databases Differential privacy in data analysis Scalable data warehousing systems Energy-efficient database architectures Temporal data management Article Trends : Over the past decade, Naughton's publications demonstrate a trajectory from foundational database optimization to modern challenges in scalable analytics and privacy-preserving techniques. Key themes include query execution prediction, workload summarization, and system design for big data environments. Scientific Awards : ACM Software System Award (2008) - Recognizing his contributions to database software systems Collaborative Network : He has collaborated with leading researchers including AnHai Doan (entity matching), Somesh Jha (privacy), and Stratis Viglas (query optimization), producing impactful work in SIGMOD, VLDB, and ICDE venues.
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).
Pierre-Yves Schobbens is a Full Professor at the University of Namur, Faculty of Computer Science, specializing in software verification and formal methods. He serves as the Director of the Research Group on the Foundations of Computer Science (FOCUS) and holds leadership roles including President of the Research Center on Information Systems Engineering (PReCISE), Chair of the International Affairs Commission for the Faculty of Computer Science, and Chair of the Doctoral Commission for Exact Sciences at the university. Education: Bachelor in Philosophy, Université Catholique de Louvain (UCL), 1982 Master in Applied Mathematics and Economics, UCL, 1983 Master in Computer Engineering, UCL, 1984 Doctorate in Computer Science, UCL, 1992 Research Interests: Professor Schobbens specializes in software product lines, software verification, formal methods, agent-oriented software, and model checking. His research focuses on developing rigorous approaches for software development and verification, particularly in the context of variability-intensive systems. He has made significant contributions to the field of featured transition systems, which enable the verification of software product lines. His work bridges theoretical computer science with practical applications, addressing challenges in real-time systems, adaptive software, and database performance. Recent research directions include applying artificial intelligence techniques to software quality assurance, energy-aware computing, and the development of context-aware systems. Research Trends: Professor Schobbens' recent publications demonstrate a strong focus on the intersection of formal methods and emerging technologies. His work increasingly incorporates AI and machine learning techniques to address traditional software engineering challenges, particularly in software verification and testing. There's a notable emphasis on energy efficiency in computing systems, variability modeling for database performance testing, and the application of formal methods to self-adaptive systems. His research maintains a strong theoretical foundation while addressing practical concerns in software development. Scientific Awards: Most Influential Paper Award, VAMOS 2024 (ten-year award) Most Influential Paper Award, Software Product Lines Conference 2020 Most Influential Paper Award, International Requirements Engineering Conference 2016 Best Presentation Award, SAFECOMP 2012 Advising and Grants: Professor Schobbens has supervised 94 students across various levels. He leads multiple significant research projects including SQUAL.AI (Software Quality through Artificial Intelligence, 2025-2026), ERNEST (schEduler foR eNErgy autonomouS ioT, 2024-2025), and CYBEREXCELLENCE (Cyber Security Excellence project within the Walloon Region, 2022-2027). His research has been consistently funded since 1999, demonstrating sustained impact and relevance in his field. Laboratories and Research Teams: Professor Schobbens directs the Research Group on the Foundations of Computer Science (FOCUS) and is a key member of the Research Center on Information Systems Engineering (PReCISE). He also contributes to the Namur Digital Institute (NADI) and Namur Research Institute for Life Sciences (Narilis). His research group focuses on formal methods for software engineering, particularly addressing challenges in software product lines, model checking, and adaptive systems.
Bin Ren is an Assistant Professor in the Department of Computer Science at the College of William & Mary, where he has been a faculty member since Fall 2016. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University (2014) and was a postdoctoral research associate at Pacific Northwest National Laboratory from 2014 to 2016. Research Interests: His work centers on high-performance computing, compiler techniques, and machine learning systems, with a focus on enabling real-time and energy-efficient deep neural network execution on mobile and edge devices. He explores compiler optimizations, DNN pruning, neural architecture search, and GPU memory management to improve system performance and efficiency. Publication Trends: His recent publications (2023–2025) reveal a strong focus on compiler-aware deep learning systems, mobile and edge AI, and performance optimization across heterogeneous platforms. Key themes include DNN acceleration, memory efficiency, real-time inference, and hardware-software co-design. His work frequently appears in top-tier venues such as ASPLOS, SC, CVPR, and PLDI. Scientific Awards: NSF CAREER Award, 2021 Best Paper Award, SC 2020 Best Student Paper Nomination, SC 2020 Jeffress Trust Award, 2020 ISLPED Design Contest First Place, 2020 Student Cluster Reproducibility Challenge Paper, SC 2019 Best Paper Award, CGO 2013 SIGPLAN Research Highlights, 2013 Advising and Grants: Bin Ren has advised numerous Ph.D. and master’s students, many of whom have co-authored influential papers. His research has been supported by competitive grants, including the NSF CAREER Award. He actively mentors students in areas of parallel computing, compiler design, and machine learning systems. He has also received funding from the Jeffress Trust Awards and other sources to support interdisciplinary research. Professional Service: He has served in leadership roles such as Program Co-Chair for PPoPP'25 and HIPS'21, Track Co-Chair for ICPP'24 and HiPC'24, and Artifact Evaluation Co-Chair for PPoPP'24 and ALENEX'25. He is a frequent reviewer for top journals and conferences including TPDS, TACO, NeurIPS, and SC. Teaching: He teaches courses such as CS304 (Computer Organization) and CS642 (Compiler Techniques for High Performance Computing), contributing to both undergraduate and graduate education in systems and programming. Lab and Team: His research group focuses on system-software co-design for efficient AI deployment. Collaborators include researchers from institutions like Pacific Northwest National Laboratory and The Ohio State University. His team works on real-world applications in healthcare, autonomous systems, and scientific computing.
Carlo Curino is a researcher at Microsoft Research , focusing on database systems, cloud computing, and machine learning integration. He has collaborated extensively with institutions including MIT, Microsoft, and the University of Wisconsin-Madison. His research spans Geo-distributed data analytics Automated configuration tuning Tensor-based database systems Data lake optimization Spark performance engineering Recent publications highlight his work on AI-driven systems like MotherNet and Rockhopper , alongside contributions to query processing over compressed data and log-structured tables. Collaborators include prominent figures such as Raghu Ramakrishnan and Jesús Camacho-Rodríguez . Key projects involve LST-Bench (cloud storage benchmarking), AutoComp (data compaction), and PyFroid (commodity workstation analytics). His work bridges database optimization with modern machine learning demands in enterprise environments.
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.
Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au
Elita Pakalnickienė serves as an Associate Professor at Vilnius University's Faculty of Mathematics and Informatics. Her teaching record spans from the 2016/2017 academic year through the upcoming 2025/2026 semester, demonstrating her longstanding commitment to the institution. Her academic expertise focuses on database technologies and information systems, with specialization in: Database Management Systems Relational Database Theory Data Modeling Information Retrieval Systems Database Security Query Optimization Professor Pakalnickienė regularly teaches Database Management Systems exercises to multiple student subgroups (including MIF-Wholesale groups 1, 2, 316, and 321) across both autumn and spring semesters, maintaining a consistent teaching schedule with sessions offered during daytime (16:00-18:00) and evening (18:00-20:00) hours. As indicated by her title 'Dr.', she has achieved the highest academic degree in her field, though specific details about her doctoral research are not provided in the available information.
George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.
Malte Schwarzkopf is an Associate Professor of Computer Science at Brown University. He was previously a postdoc at MIT CSAIL (2016-2019) and earned his PhD at the University of Cambridge (2016). His research spans distributed systems, operating systems, and privacy-preserving systems, focusing on building high-performance, user-friendly systems through novel abstractions. Education: PhD, University of Cambridge, 2016 MA, University of Cambridge, 2013 BA, University of Cambridge, 2009 His work addresses critical challenges in datacenter scheduling, privacy compliance in web applications, and secure multi-party computation. Recent projects include Quicksand for resource allocation and Sesame for GDPR compliance by design. Scientific awards include the Google ML and Systems Junior Faculty Award (2025), NSF CAREER Award (2021), Amazon Research Award (2024), and multiple best paper/test-of-time awards at NSDI (2015), EuroSys (2013, 2023), and VLDB (2025). He has advised numerous students, including Justus Adam, Lillian Tsai, and Jon Gjengset, who are now at institutions like MIT, Google, and Stanford. His research is supported by NSF, Amazon, Google, Microsoft, and VMware.
Wang-chien Lee is an active Associate Professor in Computer Science and Engineering, specializing in machine learning, data mining, and graph optimization. His work spans domains including social networks, wireless sensor systems, and location-based services. Key research focus areas: Recommendation systems, Graph neural networks, and Social network analysis Pioneering applications in traffic safety, VR configuration, and blockchain marketing His publications demonstrate expertise in transfer learning, deep learning frameworks, and heterogeneous network modeling. Recent work explores traffic crash prediction, social-aware VR systems, and NFT marketing optimization. Current projects include: Learning Latent Representations of Heterogeneous Information Networks Link Quality Estimation for Wireless Sensor Networks Community Clickthrough Model Development
Kawashima Ryota is an Associate Professor at Chubu University in the Department of Information Engineering, Network Field. He holds a PhD in Information Science from The Graduate University for Advanced Studies (2010) and a Master's in Software Information Science from Iwate Prefectural University (2007). Prior to his academic career, he worked as an R&D Engineer at Stratosphere, Inc. and ACCESS CO., LTD. His research focuses on: High-performance cloud-native networking and NFV infrastructure optimization CPU cache efficiency and I/O parallelization in virtualized environments Transparent acceleration techniques for distributed databases and key-value stores Low-latency solutions for beyond-5G network architectures Recent publications (2020-2024) demonstrate strong emphasis on: Optimizing cloud-native network functions for terabit-class throughput Reducing virtualization overhead through hardware-aware resource allocation Developing proxy-based acceleration layers for NewSQL and distributed KVS Addressing latency/jitter challenges in next-generation networks Awards & Honors: IEEE FNWF'24 Best Paper Award (2024) IEICE ICM Research Award (2023) IEEE NFV-SDN Best Paper Award (2018) IEICE Communications Society Best Paper Award (2017) Research Leadership: Secured competitive grants including JSPS Kakenhi (¥16.9M direct funding) for terabit-class cloud networking research. Mentored award-winning students including IEEE Nagoya Section Excellent Student Award recipient Yuki Taguchi. Active in industry collaborations with Bosco Technologies on NFV efficiency.
Assistant Professor Nikola Tanković, Ph.D. (born 1986 in Pula, Croatia) is affiliated with the Faculty of Informatics in Pula at Juraj Dobrila University of Pula . He received his B.Sc. (2009) and Ph.D. (2017) in Computer Science from University of Zagreb Faculty of Electrical Engineering and Computing. His roles include Project Leader of EDIH Adria , Vice Dean for Science and Industry Relations (2020-2024), and Member of University Informatics Committee (2019-present). Teaching: Offers courses in Programming, Software Engineering, Distributed Systems, and Web Applications Research: Focuses on Software Modeling, Quality Optimization, Distributed Systems, and Machine Learning applications across domains His 15 most recent publications span Cloud Computing , Peer-to-Peer Learning , Medical Imaging Optimization , and Business Process Modeling . Awards include the Rector's Award for SCORE 2009 Championship . He advises students on projects related to database systems, distributed architectures, and AI applications. Current initiatives include EDIAH Adria (European Digital Innovation Hub) and EVOSOFT (Software Evolution Analysis).
Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.
Ying Liu is Professor and Chair in Intelligent Manufacturing at Cardiff University's Mechanical and Manufacturing Engineering department, where he leads the High-value Manufacturing Group and directs his eponymous research lab. His affiliations include continuous roles at Cardiff since 2018, emphasizing leadership in industrial digitalization. His research spans: AI/ML Engineering : Generative design, LLMs for manufacturing Q&A, and synthetic data generation. Smart Manufacturing : Digital twins for predictive maintenance, human-robot collaboration, and sustainable production. Industrial Informatics : Knowledge graphs for fault diagnosis and multi-domain data fusion. Recent publications (2022-2025) show a focus on Industry 5.0, with 63% emphasizing AI integration (deep learning, Transformers) and 37% addressing sustainability (energy optimization, circular economy). Trends indicate growing work on human-centric systems and LLM-driven industrial automation. He leads Ying Liu's Lab , which pioneers projects in cyber-physical systems, collaborative robotics, and battery digital twins. No awards or grants are detailed, but his editorial roles (e.g., Journal of Manufacturing Systems special issues) highlight scholarly influence.
