جست‌وجوی استاد و پژوهشگر

Antoon Bronselaerمشاهده پروفایل

استادیار

• Data Quality
• Data Fusion
• Uncertainty Management
• +۲ مورد دیگر

Antoon Bronselaer is an Assistant Professor at Ghent University's Department of Telecommunications and Information Processing within the Faculty of Engineering and Architecture. He is an active member of the Database, Document and Content Management (DDCM) research group, focusing on advancing database theory and applications. His research centers on data quality (particularly measurement models and improvement techniques), temporal databases , data fusion , and uncertainty management in databases . Key contributions include measure-theoretic foundations for data quality assessment, dynamical order construction in data fusion, and predicate enrichment techniques for wrapper induction. Analysis of his recent publications reveals consistent innovation in data cleaning algorithms (e.g., Swipe framework), temporal data modeling, and orthographic similarity measures for graph-based text representations. His work increasingly bridges database theory with healthcare applications (e.g., statin-gait relationship studies) and digital humanities (Byzantine epigrams project). No scientific awards are mentioned in the source materials. Dr. Bronselaer's academic guidance includes formal advisees at Ghent University, though specific student names aren't documented in the provided texts. His research is supported through university positions and likely Belgian/Flemish research grants given his publication venues. Current projects involve automated news update analysis, persistent identifier validation, and syntactic interoperability frameworks. He leads research within the DDCM group, specializing in data quality measurement systems and temporal data management solutions. Recent work demonstrates expansion into healthcare data analytics and cultural heritage informatics while maintaining core expertise in database theory.

• Bioinformatics
• Genomics
• Metabolomics
• +۷ مورد دیگر

Marnix Medema is a Personal Professor in Bioinformatics at Wageningen University & Research. His research focuses on computational approaches to understand microbial natural product biosynthesis, microbiome dynamics, and plant-microbe interactions. He leads projects on gene cluster discovery, AI-driven natural product science, and microbial synthetic communities. His work bridges bioinformatics, genomics, and metabolomics. Key contributions include the antiSMASH tool for gene cluster analysis and the BGC Atlas web resource. He collaborates globally on topics like lichen biochemistry and crop microbiome resilience. He supervises multiple PhD candidates exploring topics such as bacteriophage diversity modeling and natural product discovery via AI. His research has been featured in Cell, Nucleic Acids Research, and Nature Product Reports.

Semih Salihoğluمشاهده پروفایل

دانشیار

• Database Management Systems
• Graph Data Management Systems
• Distributed Data Processing Systems

Semih Salihoğlu is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. He holds a PhD from Stanford University (2015) and a BSc from Yale University (2006). His research focuses on database management systems, graph data management, and distributed data processing systems. Research interests include graph database optimization, large-scale data processing, and open government data integration. His recent publications focus on modern techniques for querying graph-structured databases, differential computation optimization, and transforming open data portals into interactive databases. Salihoğlu's work addresses challenges in handling complex graph databases beyond traditional relational models.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Jyrki Nummenmaa is a Professor at the Faculty of Information Technology and Communication Sciences, Department of Computing Sciences at Tampere University. He holds an ORCID ID (0000-0002-7476-7840) and can be contacted via jyrki.nummenmaa@tuni.fi. His research focuses on data mining, machine learning, natural language processing, and database systems. Key research interests include computational methods for analyzing political language, RDF graph processing, ethical AI security systems, and sequential pattern mining. He has collaborated extensively with researchers such as Peltonen J., Zimina E., and Duan L. Recent publications (2022–2024) emphasize interdisciplinary applications like parliamentary discourse analysis and transparent question-answering systems. He has edited conference proceedings and contributed to over 100 peer-reviewed works since 1992. His work spans theoretical advancements (e.g., fair neighbor embeddings, nonparametric graph embeddings) and applied solutions (e.g., bus delay analysis, health-e-living simulations). Notable projects include TraQuLA for RDF querying and PiHVI for forum posting analysis.

David G. Mitchellمشاهده پروفایل

دانشیار

• Propositional Satisfiability
• Constraint Satisfaction Problems (CSP)
• Formal Verification
• +۳ مورد دیگر

David G. Mitchell is an Associate Professor in the Department of Computing Science at Simon Fraser University (SFU). He is part of the School of Computing Science and affiliated with the Computational Logic Laboratory. His research focuses on propositional satisfiability (SAT) and finite domain constraint satisfaction (CSP), exploring their complexity and practical algorithm design for applications in formal verification and related tasks. Education : Ph.D., Computer Science, University of Toronto, 2002 M.Sc., Computing Science, Simon Fraser University, 1993 B.Sc., Cognitive Science and Artificial Intelligence, University of Toronto, 1989 Research Interests : Mitchell's work bridges mathematical logic and computer science, emphasizing SAT-based problem solving, constraint modeling languages, and automated reasoning. His contributions include advancements in clause learning, resolution complexity, and declarative programming frameworks for solving NP problems. He has developed tools like MXC and Enfragmo, which apply SAT techniques to real-world applications. Students : Mitchell has advised numerous graduate students, including current PhD candidates Mona Mehdizadeh and Sima Jamali, and past advisees such as Sahba Etezad, Heng Liu, and Faraz Hach. His students' work spans algorithm optimization, constraint satisfaction, and formal verification. Labs/Teams : He is a member of the Computational Logic Laboratory at SFU, contributing to research in logic-based problem-solving and algorithm development.

• Location Theory
• Combinatorial Optimization
• Service Management
• +۳ مورد دیگر

Rongbing Huang is an Associate Professor in the School of Administrative Studies at York University's Faculty of Liberal Arts & Professional Studies. His research focuses on operations management, including location theory, combinatorial optimization, and service systems. Education: PhD in Operations Management from Rotman School of Management, University of Toronto M.Sc. in Management Information Systems from Fudan University B.Sc. from East China Normal University Dr. Huang's research examines optimization problems in logistics, transportation, and environmental policy design. Recent publications address green energy subsidies, environmental taxation schemes, and port-based supply chain decisions. He teaches Quantitative Methods, Decision Analysis, eCommerce, Physical Distribution, and Operations Management, drawing on his professional experience as a software engineer in China.

Jiasi Shenمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Program Analysis
• +۳ مورد دیگر

Jiasi Shen is an Assistant Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She leads the HKUST Automated Reasoning and Transformation of Software research group. PhD and Master's from Massachusetts Institute of Technology Bachelor's from Peking University Her research focuses on automating software development through program analysis , program transformation , and active learning . She explores how to systematically introduce safety checks, optimize performance, and enable cross-platform adaptation while maintaining core functionality. Recent work includes: Dynamic graph-based fingerprinting for cryptomining detection Benchmarking LLMs for operating system verification tasks Improving program comprehension via deimplicitization techniques She has received the Distinguished Artifact Award at SLE 2017 and serves on program committees for OOPSLA, Onward!, and SPLASH conferences. Her group supervises multiple PhD and MPhil students while developing systems like Konure (database application modeling) and KumQuat (parallel Unix command synthesis).

• Natural Language Processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Aditya Sharma is a researcher at the University of California Santa Barbara , focusing on Natural Language Processing , Computer Vision , and Artificial Intelligence . His work bridges vision-language models, medical AI evaluation, and knowledge graph reasoning. PhD : Advancing AI Understanding in Language & Vision (UCSB, 2024) Research Interests: • Vision-Language Models for geometric reasoning and mixed reality • Medical LLM evaluation and hallucination reduction • Temporal knowledge graph reasoning and multimodal systems Article Trends: Recent publications emphasize vision-language integration (e.g., GeoCoder, OCTO+), LLM reliability in medical contexts (MedHELM), and temporal reasoning (TwiRGCN). He explores applications in dark web security , healthcare diagnostics , and energy-efficient computing .

• Neuroimaging
• Reproducible Research
• Computational Neuroscience
• +۳ مورد دیگر

Dorota Jarecka is a Research Scientist at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology (MIT). Her work focuses on developing open-source software tools and frameworks to enhance reproducibility and scalability in neuroimaging research. She is a core contributor to initiatives like BIDS Apps, NiMARE, and Pydra, which aim to standardize and streamline neuroimaging data analysis workflows. Her research interests span neuroinformatics, reproducible research practices, computational neuroscience, and the development of scalable data management solutions. She actively contributes to consortia such as the NMIND consortium and the BRAIN Initiative Cell Census Network, promoting collaborative approaches to neuroimaging challenges. Jarecka’s publications highlight her expertise in large-scale neuroimaging analysis, meta-analysis techniques, and the integration of open science tools like DataLad and Datalad. She emphasizes the importance of human-in-the-loop systems and agentic frameworks (e.g., STRUCTSENSE) to improve structured information extraction in scientific workflows. Her work has advanced reproducibility through ontologies and provenance tracking (e.g., NIDM Experiment) and has addressed technical challenges such as software variability across operating systems. She is also involved in educational efforts, including Software Carpentry workshops on version control with Git. Jarecka’s contributions bridge computational methods with neuroscience, fostering collaboration between researchers, developers, and institutions to tackle complex neuroimaging and atmospheric science problems.

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +۶ مورد دیگر

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

Viswa Viswanathanمشاهده پروفایل

استاد

• Data analytics
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Viswa Viswanathan is a Professor of Computing and Decision Sciences at the Stillman School of Business, Seton Hall University. He specializes in data analytics, machine learning, artificial intelligence, and software engineering. His research focuses on leveraging IT and analytics to enhance online learning environments, particularly through intelligent tutoring systems and educational technology innovation. He has authored numerous books on R programming, SAP certification, and business analytics course materials. Research Contributions: Dr. Viswanathan has published extensively in operations research, algorithm design, and educational technology. Notable work includes stochastic greedy algorithms, constraint-based intelligent tutoring architectures, and optimization models for pooled testing during pandemics. His work bridges theoretical computing with practical applications in business education. Teaching: Teaches advanced courses like Business Applications of Machine Learning, Business Intelligence, and Big Data Analytics. Known for integrating emerging technologies like Ruby on Rails and GPT-3 into pedagogical frameworks. Professional Impact: Co-developed SAP certification guides (TS410 and TERP10) widely used in enterprise education. His books on R programming (e.g., R Data Analysis Cookbook) are standard references in data science education.

• Approximation Algorithms
• Network Design Problems
• Operations Research
• +۳ مورد دیگر

Barbara M. Anthony is the Lord Chair and Professor of Computer Science at Southwestern University. She holds a PhD from Carnegie Mellon University (2008) and a BA from Rice University (2001). Her expertise spans Approximation Algorithms, Network Design, Operations Research, Graph Theory, and Algorithmic Game Theory. She teaches courses ranging from introductory programming to advanced topics like Theory of Computation and supervises academic internships. Dr. Anthony is actively involved in student research mentorship, leading projects such as algorithm development for course scheduling, transportation optimization, and educational tools like the Surad.io streaming platform. She has secured grants including an NSF S-STEM Award ($614k) and a Google CS Engagement Award ($5k). Notable awards include recognition as an ACM Senior Member and contributions to diversity initiatives like NCWIT Extension Services. She advises UPE (Computer Science Honor Society) and collaborates with nonprofits like Faith in Action Georgetown. Research highlights include work on Doodle Poll voting equilibria (AAMAS 2018), Dial-a-Ride optimization (ATMOS 2019), and bottleneck matching algorithms (COCOA 2022). Over 30 students have conducted research under her guidance, many presenting at conferences like Texas Academy of Sciences and SIGCSE. Her pedagogical innovations include integrating real-world datasets (e.g., UCSC Genome Browser) into coursework and promoting parallel computing concepts in first-year seminars. Affiliations: Department of Computer Science, Southwestern University Grants: NSF S-STEM, Google CS Engagement, NCWIT Labs/Teams: Collaborations with local nonprofits (Faith in Action), tech startups (Surad.io), and interdisciplinary student projects Future Work: Expanding AI ethics education, optimizing university timetabling systems, and fostering diversity in computing

Kent Jonesمشاهده پروفایل

استاد

• Computer Science
• Electrical Engineering
• Software Engineering
• +۳ مورد دیگر

Kent Jones is a Professor in the Department of Math/Computer Science at Whitworth University since 1995. His academic career includes roles as site director for the ACM Intercollegiate Pacific Northwest Programming Competition and judge/problem writer since 2000. He has collaborated on medical technology grants and taught over 15 courses ranging from introductory programming to advanced topics in computer architecture and graph theory. Education: Ph.D./M.S. from Washington State University, B.S. from Walla Walla College Research focuses on machine learning applications in medical technology, genetic algorithms, and computational networks. His work includes developing an endoscopic guidance system for neurosurgery and exploring fuzzy retrieval systems in spatial databases. Jones emphasizes interdisciplinary collaboration, demonstrated through international presentations in India and Australia. Grants include $15K for neurosurgery technology (2000) and $4.4K for hardware/software (2001). He co-organized computing outreach seminars for high-school students (2007) and maintains industry ties through prior roles at Boeing and McDonald's.

• Big data analytics
• Scalable intelligent information systems
• Cloud data analytics
• +۶ مورد دیگر

Yanlei Diao is a Professor of Computer Science at Ecole Polytechnique in France and also holds a professorship at the University of Massachusetts Amherst. She joined Ecole Polytechnique in September 2015 and leads the CEDAR research team focusing on Rich Data Exploration at Cloud Scale. Her work bridges theoretical computer science with practical big data systems that address real-world challenges in data analytics. Professor Diao's research spans big data analytics, scalable intelligent information systems, and cloud data processing infrastructure. Her work emphasizes practical solutions for explainable anomaly detection, interactive data exploration, and uncertain data management. She has pioneered systems like UDAO (a next-generation optimizer for cloud analytics), EXAD (explainable anomaly detection), AIDEme (interactive data exploration), and GESALL (genomic scalable analysis) that have influenced both academia and industry. Her recent publications reveal a strong focus on making big data analytics more explainable, efficient, and accessible. She has developed frameworks for unsupervised anomaly detection across heterogeneous domains, created benchmarks like Exathlon for evaluating explainable anomaly detection systems, and advanced human-in-the-loop approaches for interactive database exploration. Her work consistently bridges theoretical foundations with practical implementations in distributed systems. Selected Awards: ERC Consolidator Award (2017-2023) for "Charting a New Horizon of Big and Fast Data Analysis through Integrated Algorithm Design" CRA-W Borg Early Career Award (2013) NSF CAREER Award (2008) IBM Innovation Award on Scalable Data Analytics (2010) Professor Diao actively mentors PhD and Master's students, with former students now holding positions at top technology companies including Google, Facebook, Amazon, Netflix, and Huawei. Her research is supported by diverse funding sources including the European Research Council, National Science Foundation, ANR, and industry partners like Google, IBM, and Alibaba. She serves as PC Co-Chair of PVLDB 2025-2026 and has delivered keynotes at major industry events including Amazon Machine Learning Workshop (2024), SWIFT AI Forum (2023), and Berlin Institute for the Foundations of Learning and Data (2022). Her CEDAR research team at Inria/LIX develops cutting-edge technologies for big data analytics, with current projects focusing on foundation models for big data, explainable AI for anomaly detection, and genomic data analysis at scale. The team maintains strong collaborations with industry partners including Alibaba Cloud, where joint work has led to significant publications at top database conferences.

Sotirios Batsakisمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Representation
• Semantic Web
• Spatio-Temporal Reasoning
• +۴ مورد دیگر

Dr. Sotirios Batsakis is a Senior Research Fellow at the University of Huddersfield, School of Computing and Engineering. He holds a diploma in Computer Engineering and Informatics from the University of Patras (Greece) with highest distinction, and a Master’s/Ph.D. in Electronic & Computer Engineering from the Technical University of Crete. His work contributes to UN Sustainable Development Goals through research in knowledge representation, semantic web, and spatio-temporal reasoning. Centre for Planning, Autonomy and Representation of Knowledge Centre of Artificial Intelligence for Mental Health Centre for Autonomous and Intelligent Systems His research spans knowledge representation, semantic technologies, spatio-temporal reasoning, and AI applications in mental health. Recent work focuses on large language models for formal verification, defeasible reasoning, and compliance checking in legal contexts. He has contributed to qualitative spatial reasoning frameworks, temporal representation in OWL, and semantic IoT systems. Active in academic service as a programme committee member for IEEE conferences, he has published extensively on semantic web technologies, ontologies, and AI applications. His research fingerprint includes ontology engineering (100%), semantic web (66%), and temporal reasoning (15%) across 63 publications since 2009.