جست‌وجوی استاد و پژوهشگر

Qicheng Yuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Cyber Security
• +۱ مورد دیگر

Dr. Qicheng Yu is an Associate Professor (Enterprise) at the School of Computing and Digital Media, London Metropolitan University. As Subject Standard Board Chair of Computer Science and Applied Computing, he leads the Data Analytics MSc and Information Technology (Distance Learning) MSc programs. His research spans AI, cyber security, and big data, with a focus on practical applications in education, finance, and urban development. His research interests include Artificial Intelligence , Machine Learning , and Cyber Security , with projects ranging from fraud detection to student performance prediction. Recent work highlights multimodal data analysis for fake review detection and data dashboard development for business intelligence. Fellow of the Higher Education Academy (FHEA) Member of Data Science Association and Cyber Security Systems Research Centre Recipient of Innovate UK grants (2022: £131,296; 2019: £19,200; 2015: £115,000) He supervises PhD students in data science and AI , while contributing to cross-disciplinary initiatives like the Empowering London Lab and SME cyber security clinics.

Fabrizio Silvestriمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Fabrizio Silvestri is a Professor at the Department of Computer, Automatic and Management Engineering (DIAG) of Sapienza University of Rome, where he leads the RSTLess research group focused on Robust, Safe, and Transparent Deep Learning. His research spans Web Information Retrieval, Natural Language Processing, Machine Learning, and Deep Learning with emphasis on Retrieval Augmented Generation systems. Current projects investigate document chunking strategies in RAG to minimize distraction from irrelevant information, "backtracing" user query motivations, and fundamental parallels between Chain of Thought reasoning and information retrieval processes in large language models. Professor Silvestri actively supervises bachelor and master thesis students through structured projects in NLP and RAG, including investigations into chunking methodologies, query motivation analysis, and internal reasoning mechanisms. The RSTLess group maintains an active research agenda centered on developing transparent and robust deep learning frameworks while addressing practical challenges in information access systems.

Bo Zengمشاهده پروفایل

دانشیار

• Optimization
• Robust Optimization
• Stochastic Programming
• +۷ مورد دیگر

Bo Zeng is an Associate Professor in the Swanson School of Engineering at the University of Pittsburgh. His research focuses on developing and utilizing optimization and analytics tools to address challenges in real systems, particularly in discrete optimization models with uncertainties, game theory models, and advanced data analysis and computing methods. His work is extensively applied in engineering, healthcare, and management systems. Dr. Zeng earned his PhD from Purdue University in 2007 and his BS from Xian Jiaotong University in 1998. His research interests span optimization, robust optimization, stochastic programming, power systems, demand response, renewable energy integration, high performance computing, game theory, mixed integer programming, and multilevel optimization. Analysis of Dr. Zeng's recent publications reveals a strong focus on power systems and energy applications, with significant contributions to microgrid planning, demand response modeling, renewable energy integration, and robust optimization techniques. His work demonstrates a consistent pattern of applying advanced mathematical optimization methods to solve complex problems in energy systems, with increasing attention to uncertainty modeling and risk management in power grid operations. Dr. Zeng has collaborated extensively with researchers across multiple institutions, particularly in China, reflecting the global nature of energy research and the international collaboration needed to address complex energy challenges. His publications span high-impact journals in power systems, optimization, and interdisciplinary energy research.

• Artificial Intelligence
• Network Administration
• Multi-agent Systems
• +۶ مورد دیگر

Horia Popa is a Lecturer at the Faculty of Computer Science , West University of Timișoara. He has taught courses such as Artificial Intelligence , Network Administration , and Functional and Logic Programming since the 2022-2023 academic year, with additional historical courses dating back to 2011-2012. His teaching emphasizes hands-on lab work, software tools (Jess, CLIPS, WEKA), and project-based learning. Education: Not explicitly mentioned in the text. Research: Focuses on multi-agent systems, distributed constraints, asynchronous search algorithms, and system administration. Research Interests: Horia Popa specializes in Artificial Intelligence and Multi-agent Systems , particularly in asynchronous search techniques and constraint satisfaction problems. His work explores scale-free networks, nogood processors, and distributed execution environments. He also investigates Network Administration (DHCP, firewall configuration, kernel recompilation) and Knowledge Discovery through agent-based modeling. Article Trends: His publications (2001-2015) span Computer Science , Artificial Intelligence , and Multi-agent Systems . Key subfields include Asynchronous Algorithms , Constraint Networks , Protein Folding Simulation , and Kernel-Level System Management . He frequently uses NetLogo for large-scale simulations and integrates Samba/ldap for networked environments. Teaching and Projects: Students in his courses work on projects involving Jess , Prolog , and JADE . Assignments include implementing search algorithms (A*, Hill Climbing, RBFS), configuring NIS and Samba servers, and analyzing system monitoring tools like sar and top . He emphasizes practical implementation and cross-language diversity (e.g., Racket, Prolog).

• Computer Architecture
• Parallel Programming
• Compilers
• +۶ مورد دیگر

Martha A. Kim is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science. She serves as a Member of the Data Science Institute, Center Co-Chair of the Center for Computing Systems for Data-Driven Science, and Chair of the Computer Engineering Program. Professor Kim's research focuses on computer architecture with particular emphasis on the hardware-software interface. Her work develops tools and designs that help computer systems operate efficiently and intuitively, including database accelerators for energy-efficient queries, techniques for optimizing parallel software to better utilize hardware resources, and improving the versatility of specialized hardware accelerators. She leads the ARCADE Lab where she conducts research in computer architecture, parallel programming, compilers, and low-power computing. Her publication record shows consistent output at top-tier conferences including ASPLOS, MICRO, MEMOCODE, ISLPED, DAC, DaMoN, CC, and CGO, with recent work spanning video transcoding in the cloud, pipelined dataflow circuits, thermal monitoring, and database acceleration. Professor Kim has received several prestigious awards: 2013 Rodriguez Family Award 2015 Edward and Carole Kim Faculty Involvement Award 2013 NSF CAREER award 2016 Anita Borg Early Career Award She currently advises three doctoral students (Martha Barker, Thomas Repetti, and Andrea Lottarini) and has previously graduated PhD students Melanie Kambadur (2016) and Lisa Wu (2014). Her research is supported by C-FAR, DARPA, Google, Intel, and NSF. Professor Kim teaches Fundamentals of Computer Systems, Computer Architecture, and Principles and Practice of Parallel Programming courses at Columbia University. The ARCADE Lab provides opportunities for students to work on cutting-edge research in hardware-software interface optimization, with applications in energy efficiency, database acceleration, and parallel computing systems.

Binghao Liمشاهده پروفایل

دانشیار

• Indoor Positioning
• Pedestrian Navigation
• Satellite Positioning
• +۳ مورد دیگر

Associate Professor Binghao Li leads the MIoT & IPIN Lab at the School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney. He holds a PhD in Spatial Information Systems from UNSW and advanced degrees in Civil and Electrical/Mechanical Engineering from Tsinghua University and Beijing Jiaotong University. Expertise in indoor/outdoor positioning systems Pioneering mine IoT applications Leader in pedestrian navigation research His research spans indoor positioning technologies, satellite navigation, and mining IoT solutions. Key grant projects include: 2021 CRC-P grant ($2m) for underground mine LoRa networks 2020 ARC Research Hub ($5m) for connected sensors 2018 Digital Grid Seed Funding for indoor navigation 2015-2019 ARC Linkage grants for positioning systems Award highlights: 2019 Best Paper & Presentation Awards 2010 VC's Post-Doctoral Fellowship 2004-2005 student research awards He supervises research in indoor positioning and mine IoT, and teaches courses including ENGG1000 Engineering Design and MINE8710 Mine Slope Stability.

Kawashima Ryotaمشاهده پروفایل

دانشیار

• Network Function Virtualization
• Cloud-Native Networking
• High-Performance Computing
• +۳ مورد دیگر

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.

• Computational Linguistics
• Natural Language Processing
• Czech Language Processing
• +۴ مورد دیگر

Zuzana Nevěřílová serves as an Assistant Professor at Masaryk University's Department of Czech Language within the Faculty of Arts. She also maintains external cooperation with the Department of Machine Learning and Data Processing at the Faculty of Informatics, demonstrating interdisciplinary engagement between linguistic theory and computational approaches. Her research interests span computational linguistics with particular emphasis on Czech language processing, natural language processing methodologies, and development of language resources. Her work bridges theoretical linguistics with practical computational applications, focusing on areas such as named entity recognition, text analysis, word embeddings, and language resource development. Dr. Nevěřílová's recent publication trends (2021-2025) reveal strong focus on Czech language technologies, including named entity processing in parallel corpora, efficient language models, and educational applications of NLP. Her work demonstrates consistent contributions to Slavonic language processing communities through workshops like RASLAN and conferences like TSD. Her scientific contributions include extensive publications in proceedings of international conferences and workshops, software development for language resources, and educational materials for digital humanities. She has supervised various projects related to language technology and has contributed to significant resources like the New Encyclopedic Dictionary of Czech and VerbaLex lexical database. Her research has practical applications in language services, educational technology, and digital humanities. Dr. Nevěřílová works within the Institute of the Czech Language at Masaryk University, contributing to research teams focused on Czech language processing, corpus development, and computational lexicography.

• Temporal Databases
• Time Series Analysis
• Database Systems
• +۴ مورد دیگر

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .

Umakishore Ramachandranمشاهده پروفایل

استاد

• Distributed Systems
• Edge Computing
• Sensor Networks
• +۵ مورد دیگر

Umakishore Ramachandran is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing, where he directs the Embedded Pervasive Lab. He received his Ph.D. from the University of Wisconsin-Madison in 1986 and has led transformative initiatives including the Online MS in Computer Science (OMSCS) program. His research spans distributed systems, edge computing, and real-time sensor networks, with applications in smart surveillance and connected vehicles. His research interests include architectural design of parallel/distributed systems, large-scale situation awareness using camera networks, cloud-edge continuum optimization, and latency-sensitive applications for geo-distributed infrastructures. Recent work focuses on elevating edge computing to parity with cloud resources. His publications show strong emphasis on edge computing innovations (MicroEdge, FogStore), real-time video analytics (EVA, ClairvoyantEdge), and adaptive mobile systems (Foresight). Trends include multi-tier architectures, quality-of-experience optimization, and scalable processing for IoT workloads. Major Awards: IEEE Fellow (2014) NSF Presidential Young Investigator (1990) ACM/IFIP Middleware Best Paper (2022) 3x College of Computing Dean's Awards He has advised 40+ PhD students, with recent graduates at Google, Microsoft, and academia. Current NSF/CPS grants support his work on geo-distributed latency-sensitive applications. He co-leads the STAR Center and Samsung-funded embedded software programs. His Embedded Pervasive Lab develops systems like Stampede (stream processing) and DFuse (sensor fusion), with testbeds in the Aware Home and transportation networks. Teams collaborate with Intel, Microsoft, and Bosch on edge-AI deployments.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +۳ مورد دیگر

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.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.

Rajiv Guptaمشاهده پروفایل

استاد

• Parallel Computing
• Compilers
• Computer Architectures
• +۴ مورد دیگر

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.

Sang-Hoon Kimمشاهده پروفایل

دانشیار

• Operating Systems
• Memory Systems
• Storage Systems
• +۴ مورد دیگر

Sang-Hoon Kim is an Associate Professor in the Department of Software and Computer Engineering and Department of Artificial Intelligence at Ajou University, South Korea. He leads the Systems Software Lab (Paldal Hall 1004-2) and maintains active collaborations with Virginia Tech as a Visiting Scholar since August 2024. His academic journey includes a Ph.D. in Computer Science from KAIST (2016) under advisors Seungryoul Maeng and Jin-Soo Kim, and a B.S. in Computer Science from KAIST (2002). His research spans operating systems, memory management, and storage systems with focus on mobile platforms, heterogeneous architectures, and SSD technologies. Key interests include memory fragmentation control , distributed thread execution , key-value storage optimization , and resource disaggregation . His work bridges theoretical innovation with practical system implementations, particularly for mobile and datacenter environments. Kim's publication portfolio shows consistent output in top-tier venues including USENIX FAST, VLDB, ICDCS, and ASPLOS. His research demonstrates evolution from mobile memory management (2015-2017) toward distributed systems and hardware-aware software (2019-present), with recent emphasis on resource-disaggregated environments and heterogeneous-ISA computing. The 2024 Best Paper Award at USENIX FAST highlights his impact in storage systems research. Best Paper Award at USENIX FAST'24 Multiple patents including US-9588912B2 for memory control He directs significant research projects funded by ETRI, NRF, and US ONR, including current work on memory-centric computing systems (2020-2023) and disaggregated non-volatile memory systems using RDMA (2018-2020). His Systems Software Lab maintains strong industry partnerships with Samsung Electronics and NHN, with prior projects improving Android memory management and developing SSD-based storage systems for large-scale internet services.

Anil Madhavapeddyمشاهده پروفایل

استاد

• Planetary Computing
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Anil Madhavapeddy is the Professor of Planetary Computing at the University of Cambridge Computer Laboratory, where he co-leads the Energy & Environment Group and is a member of the Systems Research Group. He is also a Fellow at Pembroke College where he serves as Director of Studies in Computer Science. Madhavapeddy completed his PhD from the University of Cambridge in 2003 and his BEng in Information Systems Engineering from Imperial College in 1999. He holds a JM Keynes Fellowship since 2022 for his work combining computer science with economics, and serves on the management committee of the Cambridge Conservation Initiative where he co-directs 4C (Cambridge Centre for Carbon Credits) and the Centre for Earth Observation. His research spans computer systems and programming languages with a strong focus on applying these technologies to global conservation, biodiversity, and climate change challenges. He leads the OCaml Labs group and has made significant contributions to open-source projects including OCaml, Docker, Xen, and OpenBSD. His work often bridges computer science with environmental science, developing computational approaches to address planetary-scale challenges. Madhavapeddy's recent publications demonstrate a clear trajectory toward integrating programming language research with environmental monitoring and conservation. His work spans from foundational programming language techniques to applied geospatial computing systems, with increasing emphasis on biodiversity measurement, carbon credit systems, and planetary-scale environmental monitoring. JM Keynes Fellowship (2022-present) As an educator, Madhavapeddy teaches undergraduate courses including Foundations of Computer Science, Software & Security Engineering, and Cloud Computing. He mentors MPhil and PhD students and co-founded the award-winning book 'Real World OCaml' (2nd Edition, 2022). He has co-founded several companies including Unikernel Systems, High Energy Magic, Segfault, and Tarides to translate research into real-world impact. Madhavapeddy leads the OCaml Labs group at Cambridge and works closely with the Energy & Environment Group, collaborating with colleagues from Plant Sciences, Zoology, Economics, and NGOs including UNEP-WCMC and the IUCN. His current efforts are primarily focused on conservation technology through partnerships with organizations like Canopy PACT.