جست‌وجوی استاد و پژوهشگر

Kexin Rongمشاهده پروفایل

استادیار

• Database Systems
• Machine Learning
• Data Management
• +۶ مورد دیگر

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Tech, leading the D2I Lab as part of the Georgia Tech Database Group . She also holds an affiliation with VMware Research Group by Broadcom. Education: Ph.D. in Computer Science (2021) from Stanford University, advised by Peter Bailis and Philip Levis B.S. in Computer Science (2015) from California Institute of Technology Research Interests focus on democratizing data science through systems that integrate database management , machine learning , and human-computer interaction . Key projects include SketchQL for sketch-based video retrieval, Inshrinkerator for deep learning checkpoint compression, and Lotus for ML preprocessing pipeline characterization. Scientific Awards include Amazon Research Award (2024) NSF Awards #2335881 (2024) and #2333737 (2023) Catherine M. and James E. Allchin Early Career Professorship (2022) SIGMOD Distinguished PC Award (2024) SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention (2022) Advising includes Ph.D. students: Rajveer Bachkaniwala, Hongbin Zhong Co-advised students: Hantian Zhang (with Xu Chu), Renzhi Wu (with Xu Chu) Former Ph.D. advisee: Peng Li (2023 graduate) Lab & Collaborations : Leads the D2I Lab at Georgia Tech, which has received funding from NSF and Bosch Research. The lab actively collaborates with institutions like Stanford University and contributes to open-source projects including MacroBase and ASAP .

• Artificial Intelligence
• Computational Logic
• Knowledge Representation
• +۳ مورد دیگر

Atefeh Keshavarzi Zafarghandi is a Visiting Fellow at the Faculty of Science and the Network Institute of Vrije Universiteit Amsterdam. She specializes in Artificial Intelligence with a focus on formal argumentation frameworks, computational logic, and knowledge representation. Her research explores topics like abstract dialectical frameworks, semantic analysis of reviews, and inconsistency-tolerant query answering. Key areas of research include computational complexity of argumentation semantics, expressiveness of logical frameworks, and applying AI to real-world problems such as product review analysis and knowledge base querying. She has collaborated internationally on projects involving computational logic and trust management systems. Recent work includes contributions to IFIP Trust Management conferences, ICWE web engineering events, and Nonmonotonic Reasoning workshops. Her publications address both theoretical foundations (e.g., semantics analysis) and applied AI applications (e.g., review usefulness classification). Dr. Zafarghandi holds a strong track record of peer-reviewed publications (15+ outputs) and teaches advanced courses like Knowledge Representation. She actively contributes to interdisciplinary research at the intersection of computer science and formal logic.

Jacopo Urbaniمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Data Stream Reasoning
• Semantic Web
• +۴ مورد دیگر

Jacopo Urbani is an Associate Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science, Computer Systems department, and the Network Institute. His research focuses on Knowledge Graphs, Stream Reasoning, and Distributed Computing. He explores scalable reasoning techniques for large-scale datasets, integrating semantic technologies with real-time data processing. His work emphasizes practical applications of logic-based systems, including existential rules, probabilistic reasoning, and trigger graphs for efficient knowledge base materialization. Recent contributions address challenges in handling dynamic data streams and enhancing the scalability of semantic web technologies. Urbani has published extensively in top venues like the Semantic Web Conference (ESWC) and the International Conference on Principles of Knowledge Representation and Reasoning (KR). Notable projects include the VLog rule engine for knowledge graphs and the Tab2Know platform for extracting structured data from scientific tables. He supervises PhD theses in areas like stream reasoning and knowledge graph embeddings. His research also involves collaborative efforts with institutions worldwide, addressing topics such as data compression, distributed computing architectures, and hybrid reasoning systems.

• Learning Analytics
• Artificial Intelligence in Education
• Educational Technology
• +۳ مورد دیگر

Xinyu Li is a Research Fellow at the Department of Human Centred Computing, Monash University. Their work focuses on multimodal learning analytics, AI in education, and collaborative learning dynamics, contributing to UN Sustainable Development Goals through educational technology advancements. Key research areas include generative AI's impact on learning processes, self-regulated learning strategies, and spatial data analysis for educational systems. Over 27 peer-reviewed publications since 2021 demonstrate expertise in learning analytics, educational psychology, and computer science intersections. Recent studies examine metacognitive effects of AI tools, comparative analysis between ChatGPT and human experts in learning support, and temporal dynamics of collaborative learning through sensor data. Work also extends to spatial computing with road network query optimization and ethical challenges of large language models in education. Collaborations span global institutions, reflecting interdisciplinary reach in education technology. Publications have been widely disseminated via news outlets, social platforms, and academic networks, achieving significant readership on Mendeley. Research bridges technical innovations (e.g., FLoRA Engine, VizChat systems) with educational outcomes, emphasizing practical deployments and real-world impact through field trials and randomized controlled trials.

Avi Silberschatzمشاهده پروفایل

استاد

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

Avi Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University, where he previously chaired the Department of Computer Science (2005–2011). He holds a chaired professorship and has held prior roles at Bell Laboratories and the University of Texas at Austin. His research focuses on database systems, operating systems, storage systems, and network management. He is a Fellow of ACM, IEEE, and AAAS, and has received prestigious awards including the ACM Karl V. Karlstrom Educator Award (1998) and the IEEE Taylor L. Booth Education Award (2002). Silberschatz has advised over a dozen PhD students, many now leading roles in academia and industry. He authored influential textbooks like Operating System Concepts and Database System Concepts , and holds over 40 patents. Current research explores non-volatile memory technologies and their integration into systems. Contact: avi@yale.edu. Research & Awards Key areas include database systems, operating systems, and cybersecurity. Awards include Bell Labs President’s Award (1998, 1999, 2004) and VLDB Test of Time Award (2019). His work bridges theoretical foundations and practical applications, with contributions to transaction management, storage systems, and distributed computing. Education & Grants Over two dozen grants supported his work, and he has pioneered educational materials in computer science. Collaborations include projects with Prof. Robert Soulé on next-gen memory systems.

• Data Science
• Machine Learning
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Valentin Hartmann is a Researcher at the Data Science Laboratory (DLAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on advanced topics in data science, including differential privacy, optimal transport theory, and privacy-preserving machine learning. He holds a postdoctoral position and contributes to cutting-edge research in computational statistics and secure distributed learning. His research interests span multiple domains: (1) Development of privacy-preserving techniques for machine learning and data analysis, (2) Optimization of transport problems with applications in statistics and geometry, (3) Design of secure algorithms for distributed systems, and (4) Implementation of robust statistical methods through R packages like 'transport'. Valentin actively publishes in top-tier venues, with recent contributions addressing neural network behavior analysis, privacy risks in data distribution inference, and novel cryptographic approaches for collaborative learning. His work bridges theoretical foundations with practical software solutions, as evidenced by his development of the 'transport' R package for optimal transport computations. He is affiliated with the DLAB research group at EPFL's INN building (Office INN 315), contributing to interdisciplinary projects at the intersection of computer science, mathematics, and privacy engineering.

• Pattern Recognition
• Artificial Neural Networks
• Expert Systems
• +۷ مورد دیگر

Maher Ahmed is an Associate Professor at the Faculty of Science, Wilfrid Laurier University. His research focuses on pattern recognition, artificial neural networks, and expert systems. He has contributed to diverse fields including robotics, medical informatics, network security, and computer vision. Key research interests include developing algorithms for shape representation, improving network anomaly detection, and applying machine learning to financial fraud prevention. His work bridges theoretical computer science with practical applications in healthcare and autonomous systems. Publications span robotics localization, medical literature reviews, encryption techniques, and sign language recognition, reflecting a cross-disciplinary approach. He currently holds no listed awards but maintains an active research agenda with a focus on applied artificial intelligence.

Apurva Gandhiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Apurva Gandhi is a Researcher at Carnegie Mellon University , affiliated with the School of Computer Science and the Computer Science Department. Their research focuses on Artificial Intelligence with applications in program synthesis , agentic tasks , and handwritten content analysis . Research Interests : Artificial Intelligence Machine Learning Natural Language Processing Program Synthesis Publications highlight expertise in web agents , AI-driven database systems , deepfake detection , and sequence modeling for cybersecurity . Contact : apurvag@andrew.cmu.edu

Todd Mowryمشاهده پروفایل

استاد

• Computer Architecture
• Systems
• Databases
• +۳ مورد دیگر

Todd Mowry is a Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on enhancing microprocessor-based system performance through parallelism exploitation, including single-chip multiprocessing and latency mitigation strategies. He leads initiatives like the STAMPede project, addressing challenges in compiler-driven parallelization and hardware-software co-design. His work spans computer architecture, systems, and databases, with a strong emphasis on compiler optimization, memory management, and scalable computing. Mowry has advised students including Sam Arch, Patrick Coppock, Hongyi Jin, Ruihang Lai, and Eliot Solomon. He teaches advanced courses such as 15745 (Fall 2025) and 15418 (Spring 2025), contributing to graduate-level education in computer systems. Recent research highlights include innovations in machine learning optimization (e.g., Relax framework), efficient LLM microservices, and architectural solutions for thread-safe metadata management. His publications address topics like UDF optimization, auto-batching in neural networks, and energy-minimal dataflow systems. Mowry's projects often involve collaboration across academic and industrial partners, driving advancements in both theoretical and applied computer systems. He has contributed to frameworks like CORA for tensor compilation and NVOverlay for non-volatile memory systems, emphasizing practical scalability and efficiency.

Upsorn Praphamontripongمشاهده پروفایل

دانشیار

• Software Engineering
• Computer Science Education
• Testing and Quality Assurance
• +۳ مورد دیگر

Upsorn Praphamontripong is an Associate Professor in the Department of Computer Science at the University of Virginia's School of Engineering and Applied Science. His primary focus is on computer science education and software engineering, particularly in testing methodologies and web application development. He teaches courses such as Software Testing, Database Systems, and Programming fundamentals, emphasizing hands-on learning and adaptive educational models. His research involves innovative approaches to software testing, including mutation analysis for web applications and the transition to virtual learning environments. He maintains a public repository of CS-Education resources to support teaching and learning, covering topics like software deployment, testing artifacts, and programming practice materials. Professor Praphamontripong has organized international workshops such as EASEAI (Education through Advanced Software Engineering and Artificial Intelligence), highlighting his commitment to advancing educational technology and interdisciplinary collaboration. His work bridges practical software development with pedagogical innovation, addressing challenges like self-paced learning models and virtual classroom adaptations. He actively contributes to conferences and publishes on topics ranging from web application security to performance analysis of distributed systems. His academic contributions span over two decades, with notable research in mutation testing techniques, web application quality assurance, and the integration of creativity-based skills in programming education. While no formal awards are listed, his extensive teaching portfolio and resource development underscore his dedication to improving both technical and educational practices in computer science.

Maria Orlowskaمشاهده پروفایل

عضو هیئت علمی

• Workflow Management
• Database Systems
• Data Mining
• +۳ مورد دیگر

Honorary Professor Maria Orlowska is affiliated with the School of Electrical Engineering & Computer Science at the University of Queensland. Her research focuses on workflow systems, database integration, data mining, and wireless sensor networks. She has contributed extensively to collaborative business process technologies, flexible workflow modeling, and RFID data management. Her work spans theoretical foundations in process constraints, distributed systems, and practical applications in enterprise integration and real-time data analytics. Key research areas include workflow exception handling, multidatabase integration methodologies, and optimization of dynamic processes. Her studies on sensor networks address routing algorithms and telemetry systems. She has collaborated on projects involving smart shop floors, e-learning platforms, and spatial data management. Notable contributions include methodologies for business contract compliance and service-oriented architecture advancements. Publications emphasize interdisciplinary applications of computer science principles, with a focus on real-world system implementations. Her work bridges theoretical computer science with practical engineering challenges in distributed environments.

James Cheneyمشاهده پروفایل

استاد

• Databases
• Programming Languages
• Provenance
• +۵ مورد دیگر

James Cheney is a Professor and Personal Chair of Programming Languages and Systems at the University of Edinburgh's School of Informatics, within the Laboratory for Foundations of Computer Science (LFCS). His roles include leading the Principles of Provenance research group and supervising doctoral students. He holds a PhD from Cornell University and has held postdoctoral and fellowship positions, including a Royal Society University Research Fellowship (2008–2020) and a Turing Fellowship (2018–2023). His research focuses on databases, programming languages, and provenance, with contributions to language-integrated provenance, type systems, and security. Notable projects include the Skye programming language (ERC-funded) and work on bidirectional transformations. He has supervised over a dozen PhD students, including Arthur Chan, Frank Emrich, and Jan Stolarek. His work has been supported by grants from the ERC, EPSRC, and industry partners like Huawei and Meta. Cheney's awards include the Royal Society Fellowship and ERC Consolidator Grant. He serves on program committees for top venues like POPL, ICFP, and ESOP. His research bridges theory and practice, with applications in data curation, cybersecurity, and formal methods.

Emdad Khanمشاهده پروفایل

استاد مدعو

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +۷ مورد دیگر

Dr. Emdad Khan is an Adjunct Professor of Computer Science at Maharishi University of Management. He holds a PhD in Computer Science from the University of California, Santa Cruz, along with MS degrees in Electrical Engineering (University of New Orleans) and Engineering Management (Stanford University), and a BS in Electrical Engineering from Bangladesh University of Engineering & Technology. His research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), Big Data, Machine Learning, and their applications in Intelligent Internet systems, Biological Systems, and multi-disciplinary education. He has pioneered technologies like netECHO (voice-based Internet access) and the Semantic Engine using Brain-Like Approach (SEBLA), emphasizing brain-inspired algorithms for solving NLU challenges. With 23 patents and over 75 publications, Dr. Khan has authored books including Internet for Everyone: Reshaping the Global Economy (2011) and contributed to academic works on neural networks and fuzzy systems. His work bridges technical innovation with social impact, aiming to drive economic development and global peace through technology. Dr. Khan has presented plenary talks at major conferences such as the 17th International Conference on Artificial Intelligence (AIKED ’17) and the 15th International Conference on Applied Computer Science (2016). His research explores brain-like algorithms for cognitive computing and biological systems analysis, integrating principles from Vedic Science and the Science of Creative Intelligence.

Torsten Kiliasمشاهده پروفایل

پژوهشگر

• Database Systems
• Text Mining
• In-Database Analytics
• +۳ مورد دیگر

Torsten Kilias is a Researcher at the Beuth University of Applied Sciences Berlin and the DATEXIS team, affiliated with the Data Science +X Graduate School. He holds a degree from Ilmenau University (2012) with prior internships at IBM and TU-Berlin. His research focuses on text mining in databases, particularly in-database relation extraction (INDREX) and span data models for shared-nothing architectures. Current work involves managing knowledge graphs in relational databases under the BMWi-funded Smart Data Web project. Key projects include APPL-FM (DFG), KIP-SDM (BMBF), and contributions to interdisciplinary initiatives like AnorexiaVR (BMG) and Vessel Segmentation. His technical expertise spans entity linking with neural embeddings, distributed systems for web analytics, and database acceleration using coprocessors. Publications highlight advancements in in-database analytics, with notable work in Information Systems journal (2015) and conference contributions on marketplace architectures for text annotation services (2014).

Rudolf Schneiderمشاهده پروفایل

پژوهشگر

• Deep Learning
• Information Extraction
• Information Retrieval
• +۳ مورد دیگر

Rudolf Schneider is a researcher in Data Science and Artificial Intelligence at Beuth University of Applied Sciences Berlin. He holds a PhD in Computer Science (2023) with focus on medical text mining and neural information retrieval. His academic roles included Research Assistant at DATEXIS and doctoral studies under Prof. Alexander Löser's supervision. Key research interests span Deep Learning applications in healthcare, information extraction from clinical notes, and neural retrieval systems. Current work involves BMWi-funded MACSS project for medical service solutions. Notable contributions include SECTOR (topic segmentation) and Smart-MD (medical paragraph retrieval) models. Publications highlight advancements in Open Information Extraction (OIE) benchmarking, relational database interactions, and NLP system evaluations. Active in conferences like ISWC and WWW. Organizational involvement includes heavy metal festival coordination and Chaos Computer Club participation.