Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.
Professor Rachel Harrison is a Professor in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. Her research focuses on software metrics, machine learning, and requirements engineering with emphasis on empirical and automated software engineering solutions. She has over 160 publications and extensive industry collaborations with organizations like IBM and Philips Research Labs. Her work has been recognized through roles as Editor-in-Chief of the Software Quality Journal and leadership in conferences such as ICSE and ESEM. She leads the Dependable System Engineering Centre (DSERC) and is part of the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Applied Software Engineering and Data Analytics (ASEDA) Group. Her research projects include AI applications for big data analysis (AIMi), automated review classification (ReClass), and software quality improvement (SEQUIN). Professor Harrison has served on over 50 international program committees and initiated workshops like RAISE and AIRE. Her teaching includes advanced computer science modules and leadership in courses like Essential Maths for University Study and Advanced Software Development . Her work bridges academic research and practical applications, particularly in healthcare technology (e.g., diabetes management systems) and mobile application usability. She advocates for rigorous software quality practices and has contributed to frameworks for requirements validation and risk assessment in software projects.
Hans Tompits is an Associate Professor in the Department of Knowledge-Based Systems at Technische Universität Wien (Vienna University of Technology). His research focuses on computational logic, declarative logic programming, and formal methods, with a particular emphasis on Answer-Set Programming (ASP). He coordinates the Master's program in Logic and Computation and leads projects in areas such as formal methods for optimization, fault-tolerant autonomous systems, and algorithmic composition. His work bridges theoretical advancements with practical applications, including tools like SeaLion (an ASP IDE with debugging support) and dlvhex (an ASP-based semantic web reasoner). He has contributed to foundational topics like program equivalence, debugging techniques, and integration of ASP with external systems. His recent projects address challenges in autonomous vehicle architectures, music composition algorithms, and safety-critical system design. Tompits has published extensively on topics ranging from nonmonotonic reasoning and modal logics to the development of declarative programming tools. His interdisciplinary approach spans computer science, mathematics, and AI, with applications in both academic and industrial contexts.
Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.
Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.
Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).
Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.
Kristen A. Carpenter serves as Council Tree Professor of Law and Director of the American Indian Law Program at the University of Colorado Law School. She is a leading scholar in American Indian Law, Indigenous rights, cultural property, and human rights with extensive international engagement. Professor Carpenter's research focuses on Indigenous peoples' rights in domestic and international contexts, with particular expertise in cultural property, religious freedom, language rights, and tribal sovereignty. Her work bridges legal theory with practical applications for Indigenous communities, examining how legal frameworks can better serve Indigenous self-determination and cultural preservation. Her scholarship reveals a consistent trajectory toward strengthening Indigenous rights through multiple legal frameworks. Early work established foundational concepts in cultural property and religious freedom, while more recent publications address emerging challenges in language preservation, international diplomacy, and migration. A significant portion of her research examines the practical implementation of the United Nations Declaration on the Rights of Indigenous Peoples. North American member of the UN Expert Mechanism on the Rights of Indigenous Peoples (EMRIP), appointed in 2017 Justice of the Shawnee Tribe's inaugural Supreme Court Regular speaker at UN forums including the International Year of Indigenous Languages kickoff Author of influential casebook Case and Materials on Federal Indian Law Professor Carpenter actively engages with Indigenous communities through teaching, scholarship, and service. She directs the American Indian Law Program at Colorado Law, teaches courses in American Indian Law, Cultural Property Law, and Indigenous Peoples in International Law, and mentors students through the Native American Law Students Association. Her scholarship informs both academic discourse and practical legal strategies for Indigenous rights advocacy worldwide.
Professor Jordan Taylor is affiliated with Princeton University as a faculty member in the Department of Biomedical Engineering within the School of Engineering and Applied Science. His research focuses on unraveling computational processes in motor control and learning, with particular emphasis on interactions between explicit cognitive strategies and implicit motor adaptation during skill acquisition. Taylor leads the Intelligent Performance and Adaptation Laboratory , aiming to develop optimal training protocols for motor rehabilitation post-stroke or disease. Research Interests : Taylor investigates how humans learn motor skills through dual mechanisms of declarative strategy formation and implicit neural adaptation. His work explores the neural systems underlying these processes and their functional consequences, especially in pathological conditions like cerebellar degeneration. Current studies examine working memory constraints, reward modulation of implicit adaptation, and plan-based generalization of motor learning. Publication Trends : Recent articles analyze dual mechanisms in sensorimotor learning, reward-driven adaptation, and contextual influences on motor memory. His computational neuroscience approach combines behavioral experiments, neural imaging, and theoretical modeling to study cognitive-motor interactions across various tasks.
Prof. Dr. Zeki Bayram is a full Professor and current Chairman of the Computer Engineering Department at Eastern Mediterranean University (EMU). He has served as the founding chairman of the Internet Technologies Research Center (2006) and chaired the departmental ABET committee from 2010 to 2023. His academic contributions span semantic web services, mobile payment systems, and XML-based technologies. Founded Cybersoft Bilişim Teknolojileri Limited, a dormant software company in North Cyprus Active in academic service, including thesis supervision and editorial roles Teaches courses on programming languages, automata theory, and software tools Research focuses on semantic web service composition, secure payment schemes, and declarative programming paradigms. His work integrates logic programming, constraint solving, and ontology engineering. Prof. Bayram has published extensively in journals and conferences since the 1990s, with notable contributions to formal methods in service-oriented architectures.
Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.
Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.
Heather Miller is a tenure-track Assistant Professor in the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. Her academic journey includes prior roles as an Assistant Clinical Professor at Northeastern University's College of Computer and Information Science and as Executive Director of the Scala Center at EPFL. Miller's research centers on distributed and concurrent computation through the lens of programming languages, with particular emphasis on data-centric systems, big data processing, and edge computing. A defining theme throughout her work is composability - enabling construction of complex distributed systems through composition of components that are correct by construction. Her projects span distributable closures, flexible serialization techniques, futures and promises for asynchronous programming, and deterministic concurrent dataflow models. Her recent publications demonstrate strong trends in applying programming language theory to practical distributed systems challenges, with increasing focus on WebAssembly instrumentation, microservice resilience, and language model pipelines. This evolution reflects her commitment to bridging theoretical foundations with real-world system requirements. Dahl-Nygaard Junior Prize (2023) Mentorship forms a significant component of Miller's academic work. She actively supervises multiple PhD, MS, and undergraduate researchers at CMU, including Christopher Meiklejohn, Matthew Weidner, Huairui Qui, Ria Pradeep, and Luke Dramko. Her service contributions span numerous top-tier conferences including PLDI, SPLASH, ECOOP, and ICSE where she has served as committee member, chair, and keynote speaker. Miller co-founded the Curry On conference to foster industry-academia dialogue, hosting successful editions in Prague, Rome, Barcelona, Amsterdam, and London. She leads research groups focused on distributed programming models and maintains strong industry connections through Two Sigma, where she holds an affiliation. Her work consistently emphasizes practical open-source implementations, primarily within the Scala ecosystem where she's been a core contributor since 2011.
Professor Kara Morgan-Short holds a joint appointment at the University of Illinois at Chicago in the Department of Hispanic and Italian Studies and the Department of Psychology. She directs the Cognition of Second Language Acquisition Laboratory and is affiliated with the Laboratory of Integrative Neuroscience. Her research focuses on the cognitive and neural mechanisms underlying second language acquisition, integrating linguistics, cognitive psychology, and neuroscience. She has held editorial roles for Language Learning and contributed to advancing open science practices in applied linguistics. Education: PhD in Spanish Linguistics (Georgetown University, 2007), MATL in Spanish (University of Southern Mississippi, 1998), BA in Humanities (UT Austin, 1991). Research interests include the role of declarative/procedural memory, attention, and context in SLA. She employs behavioral and electrophysiological methods (e.g., ERP) to study linguistic and cognitive processes. Key grants include NSF funding for doctoral research (2018–2022) and Language Learning grants (2014–2016). Awards include the 2018 Excellence in Teaching Award and the 2009 Harold N. Glassman Dissertation Award. Her work emphasizes interdisciplinary approaches to understanding bilingualism and SLA at both behavioral and neurocognitive levels.
