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Avi SilberschatzView profile

Professor

• Database Systems
• Operating Systems
• Storage Systems
• +4 more

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
• +3 more

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
• +7 more

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 GandhiView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

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 MowryView profile

Professor

• Computer Architecture
• Systems
• Databases
• +3 more

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 PraphamontripongView profile

Associate Professor

• Software Engineering
• Computer Science Education
• Testing and Quality Assurance
• +3 more

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 OrlowskaView profile

Academic

• Workflow Management
• Database Systems
• Data Mining
• +3 more

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 CheneyView profile

Professor

• Databases
• Programming Languages
• Provenance
• +5 more

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 KhanView profile

Adjunct Professor

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +7 more

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 KiliasView profile

Researcher

• Database Systems
• Text Mining
• In-Database Analytics
• +3 more

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 SchneiderView profile

Researcher

• Deep Learning
• Information Extraction
• Information Retrieval
• +3 more

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.

• Cybersecurity
• Privacy
• Threat Modeling
• +5 more

Wouter Joosen is a Professor at KU Leuven (Catholic University of Leuven) in Belgium, with an extensive research career spanning from 1988 to the present. His primary research focuses on cybersecurity, privacy, and secure software engineering, with significant contributions to threat modeling frameworks, particularly LINDDUN. Dr. Joosen's research interests center around privacy-enhancing technologies , security threat modeling , and machine learning applications in cybersecurity . His work bridges theoretical security concepts with practical implementations, focusing on real-world security challenges in cloud computing, authentication systems, and data protection. He has pioneered approaches to automate threat modeling processes and develop robust security mechanisms for modern distributed systems. His publication record shows a clear evolution from traditional software security in the 1990s-2000s to contemporary research on privacy-preserving machine learning, secure infrastructure as code, and advanced authentication systems. A notable trend in his recent work (2021-2025) is the integration of machine learning techniques to enhance security mechanisms while maintaining privacy constraints, particularly in access control systems and threat modeling frameworks. Dr. Joosen has collaborated extensively with researchers across Europe, particularly with Dimitri Van Landuyt, Davy Preuveneers, Danny Hughes, and Bert Lagaisse, indicating strong research group leadership and international collaboration. His research has significant practical applications in GDPR compliance, secure authentication, privacy-preserving technologies, and robust security frameworks for cloud-native applications. His work on OAuth 2.0 security, biometric authentication, and infrastructure security directly addresses current industry challenges in securing modern web applications and services.

Babak SalimiView profile

Assistant Professor

• Data Management
• Causal Inference
• Algorithmic Fairness
• +5 more

Babak Salimi is an Assistant Professor at the Halıcıoğlu Data Science Institute (HDSI) at the University of California, San Diego (UCSD), with an affiliation to the Department of Computer Science and Engineering. His research focuses on trustworthy data analysis, algorithmic fairness, and causal inference, aiming to enhance transparency, fairness, and reliability in data-driven decision-making. Salimi completed his Ph.D. in 2016 at Carleton University under Prof. Leopoldo Bertossi and was a postdoctoral researcher at the University of Washington under Prof. Dan Suciu. Salimi has been honored with prestigious awards including the NSF CAREER Award (2024), the SIGMOD Best Paper Award (2019), and the VLDB Best Demonstration Paper Award (2018). His work emphasizes causal modeling for data quality and bias mitigation, addressing systemic biases through data-centric approaches. He actively engages in academic service roles, including General Chair of Guide-AI 2025 and Area Chair for FAccT 2023/2024. His research group develops tools to bridge the gap between complex data and human understanding, with a focus on data management for machine learning and responsible AI practices. Salimi teaches courses like DSC 261 (Responsible Data Science) and DSC 100 (Introduction to Data Management), emphasizing education in ethical data science principles. Key research thrusts include causal modeling for bias mitigation, data cleaning techniques, and holistic debiasing frameworks. His NSF CAREER project redefines algorithmic bias as a data quality issue, integrating causal modeling with traditional data management strategies to enhance decision-making trustworthiness. Salimi’s work impacts diverse sectors, including healthcare and criminal justice, by improving data quality and fairness. His educational initiatives aim to broaden participation in data science through enhanced data literacy programs.

• Artificial Intelligence
• Computational Intelligence
• Database Systems
• +4 more

Univ.-Prof. Wolfgang Faber is a Professor at the Alpen-Adria-Universität Klagenfurt (AAU), affiliated with the Institute for Artificial Intelligence and Cybersecurity. He holds academic positions in both computer science and engineering disciplines. His research focuses on artificial intelligence, computational intelligence, and theoretical computer science, with particular expertise in answer set programming (ASP), knowledge representation, and formal methods. His work integrates areas like ontology reasoning, action reversibility, and hybrid knowledge bases. Education details are not explicitly stated in the provided text, but his professional trajectory reflects advanced qualifications in engineering and computer science. Research priorities include database systems, semantic technologies, and formal languages, often addressing challenges in meta-reasoning and computational logic. His contributions span theoretical advancements and practical applications, such as ASP-based tools for planning and reasoning systems. Notable research trends include the development of efficient ASP solvers, evaluation of Datalog tools for OWL 2 QL meta-reasoning, and exploration of epistemic logic programs. His work on action reversibility in STRIPS planning and paracoherent answer set computation highlights his focus on bridging formal logic with real-world problem-solving. Collaborations and projects involve international conferences and workshops, including contributions to ICLP, JELIA, and Reasoning Web venues. Wolfgang Faber's professional engagement extends to academic service roles, including membership in the AAU Senate. His research is supported through grants and collaborations, though specific grant details are not provided here. He maintains an active presence in academic communities via ORCID and Google Scholar profiles, with a prolific publication record in top-tier conferences and journals.

• Database Systems
• Data Management
• Information Systems
• +3 more

Tobias Bleifuß is a researcher affiliated with the Hasso Plattner Institute at the University of Potsdam, Germany. His work focuses on database systems, data management, and schema evolution. He has collaborated extensively with researchers like Felix Naumann and Divesh Srivastava on projects exploring functional dependencies, temporal data analysis, and data quality. Notable contributions include methodologies for discovering functional dependencies via hitting set enumeration and analyzing schema changes in Wikipedia tables. His research interests span theoretical computer science aspects of databases, practical data management challenges, and leveraging temporal data for semantic insights. Recent work emphasizes efficient algorithms for data exploration and maintaining data integrity in evolving schemas.