Find Academic Supervisors

• Database Systems
• Graph Databases
• Route Planning
• +4 more

Dr. Theodoros Chondrogiannis is a Post-doctoral Researcher at the University of Konstanz's Database and Information Systems group. He holds a PhD from the Free University of Bozen-Bolzano (2017) and previously worked as a postdoc there until joining Konstanz. His research focuses on route planning, graph database query processing, and spatial network analysis. He leads a DFG-funded project on multi-criteria path queries and teaches 'Graph Data Management and Analysis' annually. Education: BSc (Computer Science, University of Peloponnese, 2009), MSc (Advanced Information Systems, National Kapodistrian University of Athens, 2013), PhD (Free University of Bozen-Bolzano, 2017). Research interests include algorithm design for transportation networks, graph database systems, and geosocial network analysis. His work bridges theoretical foundations with practical applications in urban mobility and query optimization. He will assume an Associate Professor role at NTNU's Department of Computer Science (IDI) starting January 2025. Notable service roles include publicity chair for SSTD 2023 and co-chair for ACM SIGSPATIAL Student Research Competitions (2019-2020).

Farah ChancharyView profile

Lecturer

• Computer Science
• Cloud Computing
• Data Structures
• +7 more

Farah Chanchary is a Lecturer and Lab/Course Coordinator at the School of Computer Science, Carleton University. She specializes in areas such as data structures, cloud computing, and educational technologies. Her work spans algorithm design, privacy in advertising, and challenges in e-learning, particularly in Saudi Arabia. She coordinates laboratory and course activities, supporting academic infrastructure. Research Interests: - Data Structures and Algorithms - Cloud Computing and Migration - Privacy-Preserving Models in Advertising - E-Learning Readiness and Challenges - Machine Learning Applications (Neural Networks, Genetic Algorithms) - Geometric Query Processing Publications Trends: Her recent work focuses on efficient data structures for window queries, privacy concerns in online advertising, and cloud computing challenges. Earlier research includes studies on e-learning adoption in Saudi Arabia and neural network applications in rainfall forecasting. No awards or grants are listed. She has no documented advisees. She manages laboratory facilities and course coordination in the School of Computer Science.

• Graph Query
• Data Mining
• Pattern Recognition
• +2 more

Dr. Taotao Cai is a Senior Lecturer in Computing at the University of Southern Queensland (UniSQ) and Honorary Lecturer at Macquarie University. He holds a BEng from Jinggangshan University, MCmpScTch from Shenzhen University, and PhD from Deakin University. His research focuses on advancing graph query technology and developing novel computational methods. His research interests span graph-based data analysis, intelligent systems for knowledge discovery, and applications in remote sensing and IoT. Key areas include developing algorithms for efficient data processing and pattern recognition in complex datasets. Dr. Cai has received the School Research Encouragement Award and secured significant funding including an AEA Ignite Grant ($429,720) and ECR Seed Grant ($10,000) for projects on intelligent energy systems and pattern recognition. He mentors graduate students in projects spanning computer vision, road extraction from remote sensing, and knowledge-driven traffic safety models. His team won the 2024 UniSQ Learning and Teaching Award for curriculum innovation.

Essam MansourView profile

Associate Professor

• Federated Data Science
• Data Systems
• Distributed Systems
• +6 more

Essam Mansour is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, and the head of the Cognitive Data Science (CoDS) Lab. His research focuses on Federated Data Science, knowledge graphs, graph neural networks, large language models, and distributed systems. He has received an NSERC Discovery Grant (2020) and leads projects like the Data Civilizer system for big data management and Lusail for scalable RDF querying. He advises students across PhD, Master’s, and undergraduate levels, with notable alumni working at companies like Huawei, Amazon, and as PhD candidates at McGill. Mansour has contributed to systems such as E-Store, Accordion, and StarDB, and holds a pending patent for data processing. He actively reviews for top journals like ACM TODS and IEEE TKDE, and serves on program committees for SIGMOD, VLDB, and ICDE. His work emphasizes scalable systems for data integration, query optimization, and cybersecurity applications using graph-based techniques. The CoDS lab develops platforms like KGNet and KGLac, advancing federated learning and knowledge graph technologies.

Alessio ZamboniView profile

Researcher

• Knowledge Graphs
• Natural Language Processing
• Data Integration
• +3 more

Alessio Zamboni is an academic researcher and senior software engineer at the University of Trento, where he earned a MSc in Computer Science. He holds roles in the Department of Information Engineering and Computer Science, contributing to the KnowDive research group focusing on Knowledge Management, DevOps, and NLP. His career began at 17 as a full-stack developer, evolving into research roles at Fondazione Bruno Kessler (geographical information systems) and exchange studies in China (Zhejiang and Jilin Universities). Research interests span knowledge graphs, data integration, and software engineering. Notable projects include interoperable electronic health records and university system integration architectures. He has advised EU-funded initiatives in technical education and contributed to multilingual data disambiguation systems. Labs: Active member of the KnowDive Group at University of Trento, collaborating on interdisciplinary projects blending AI, data systems, and educational technology.

Sven HertlingView profile

Professor

• Knowledge Graph Generation
• Ontology Matching
• Knowledge Graph Integration
• +3 more

Dr. Sven Hertling is a Substitute Professor for Data Science at the University of Mannheim, affiliated with the Data and Web Science Group. His research focuses on Knowledge Graph Generation, Ontology Matching, and Knowledge Graph Fusion. He teaches courses such as Knowledge Graphs, Data Mining, and Database Technologies. Notable awards include the Best Demo at ESWC 2021 and Best Paper at the Knowledge Graphs and Semantic Web Conference (KGSWC) 2022. Education: PhD in Computer Science (implied by role). His research interests span ontology alignment, large language model integration, and scalable knowledge graph systems. Recent work includes frameworks like KGrEaT for knowledge graph evaluation and tools like MELT for ontology matching analysis. He has contributed to over 50 publications, with a focus on ontology alignment evaluation (OAEI) and semantic web technologies. Key projects include the Software Campus initiative's GUI search engine and DBkWik, integrating knowledge from thousands of Wikis. Grants: €100,000 from Software Campus for the SuGraBo project. Advising: Coordinates OAEI challenges and chairs ESWC 2023. He actively serves on program committees for ISWC, ESWC, and other conferences, emphasizing community engagement in semantic web research.

Nodari SitchinavaView profile

Professor

• Parallel Algorithms
• Cache-efficient Computing
• I/O-efficient Algorithms
• +4 more

Nodari Sitchinava is a Professor in the Department of Information and Computer Sciences at the University of Hawaii at Manoa, where he leads the Algorithms and Parallel Computing Group (AlgoPARC). His research focuses on designing efficient algorithms for modern parallel systems, particularly cache-efficient and I/O-optimized solutions for multicore and GPU architectures. His educational background includes advanced training in computer science with specialization in parallel algorithms. Research interests span: Parallel algorithm design for multicores and GPUs Cache-oblivious and I/O-efficient algorithms Communication-efficient distributed computing models Computational geometry and graph algorithms Lower bounds in computational complexity Publication analysis reveals consistent focus on parallel computing foundations, with recent work emphasizing GPU algorithm efficiency, external memory models, and geometric computations. Theoretical contributions frequently include novel lower bound proofs and practical implementations optimized for modern hardware constraints. Awards and honors include: Best Paper Award at the European Symposium on Algorithms (2019) for contributions to comparison-based algorithm complexity Teaching encompasses graduate and undergraduate courses in algorithms, parallel computing, and data structures. His instructional approach emphasizes fundamental principles of algorithm design and analysis across sequential and parallel computational models.

Haopeng ZhangView profile

Assistant Professor

• Natural Language Processing
• Generative AI
• AI4Science (Healthcare, Linguistics, Sensing)
• +3 more

Haopeng Zhang is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaii at Mānoa, leading the ALOHA Lab. He holds a Ph.D. from the University of California, Davis (advised by Dr. Jiawei Zhang), dual M.S. degrees from Georgia Tech (Electrical Engineering and Computational Science), and a B.S. from UIUC (Electrical Engineering). His research focuses on NLP, Generative AI, AI4Science, and Graph Mining, emphasizing biomedical applications, multimodal reasoning, and workflow generation. Recent achievements include organizing workshops at EMNLP and ACL, receiving grants from NSF and OpenAI, and developing tools like StrucSum and MermaidFlow. Education: Ph.D., Computer Science, UC Davis (2024) M.S., Electrical Engineering & Computational Science, Georgia Tech B.S., Electrical Engineering, UIUC Research interests span structural summarization, LLM applications in healthcare and linguistics, and graph-based NLP systems. His work bridges theoretical advancements with practical tools, including the ALOHA Lab's contributions to reliable summarization systems and multimodal benchmarks. Notable contributions include the DomainSum benchmark and Wi-Chat wireless sensing framework. Key awards include UH travel grants, OpenAI credits, and NSF computing allocations. He actively serves on conference committees (ACL, KDD) and reviews for top journals (ACM TKDD, IEEE transactions). Teaching responsibilities include Advanced AI and Machine Learning courses at UH Mānoa. Lab activities focus on AI-driven solutions for scientific domains, with ongoing projects in biomedical event extraction and low-resource language support.

Michael McGuireView profile

Assistant Professor

• Computer Science
• Data Mining
• Environmental Informatics
• +3 more

Michael McGuire is an Assistant Professor in the Department of Computer and Information Sciences at Towson University's College of Science and Mathematics. He holds a Ph.D. in Information Systems from the University of Maryland, Baltimore County (2010), with supporting degrees in Information Systems (MS, 2005) and Geography & Environmental Planning (BS, 1996). Education: Ph.D. & MS in Information Systems (UMBC), BS in Geography (Towson) Department: Computer and Information Sciences School: College of Science and Mathematics Email: mmcguire@towson.edu His research focuses on spatio-temporal data mining , sensor database systems , and environmental informatics applications. Current work examines deep learning approaches for climate prediction , including convolutional neural networks for tornado day forecasting and transformer models in hurricane intensity analysis. Earlier publications explore information visualization through eye-tracking studies and multi-criteria decision systems for route planning. Recent publications highlight machine learning applications in environmental contexts, with 2024 studies on knowledge graphs and 2023 work on federated learning . The 2022 tornado prediction papers demonstrate CNN capabilities in meteorological modeling, while 2021 eye-tracking research shows feature engineering approaches for visual processing tasks. Dr. McGuire's technical expertise spans distributed database systems , cloud computing architectures , and human-computer interaction through eye gaze analysis. His 2018-2020 publications compare SQL/NoSQL storage for climate data and develop safe route planning algorithms using Fuzzy TOPSIS and other decision frameworks.

David C. AnastasiuView profile

Assistant Professor

• Data Mining
• Machine Learning
• High Performance Computing
• +4 more

Dr. David C. Anastasiu is an academic with a focus on data mining, machine learning, and high performance computing. He served as an Assistant Professor in the Department of Computer Engineering at San José State University from 2016 to 2019 before moving to Santa Clara University. He holds a Ph.D. in Computer Science from the University of Minnesota (2016), and earlier degrees from Texas State University. His research emphasizes scalable methods for sparse data analysis, including nearest neighbor algorithms, time series forecasting, and applications in computational genomics and urban computing. He has contributed to the AI City Challenge, advancing computer vision and traffic analysis systems. His work spans theoretical advancements and practical implementations, such as hardware acceleration for ML inference (ML-NIC) and graph-based clustering for single-cell data (CosTaL). Key projects include CityFlow, a city-scale benchmark for multi-camera vehicle tracking, and the development of efficient cosine similarity search algorithms (L2AP). He also explored user-centric search result organization and collaborative learning platforms to enhance educational engagement. His lab, DMLab, focuses on interdisciplinary applications of machine learning and data science. Anastasiu’s work bridges algorithmic innovation with real-world challenges in healthcare, transportation, and environmental monitoring.

Emanuele Della ValleView profile

Associate Professor

• Big Data
• Stream Processing
• Semantic Technologies
• +3 more

Emanuele Della Valle is an Associate Professor at the Department of Electronics, Information and Bioengineering of Politecnico di Milano. He holds a PhD in Computer Science from Vrije Universiteit Amsterdam and a Master's in Computer Science and Engineering from Politecnico di Milano. His research focuses on Stream Reasoning, Big Data, Semantic Technologies, and their industrial applications. He has led initiatives to establish Stream Reasoning as a recognized field, including organizing over 15 workshops/tutorials and developing the C-SPARQL Engine (downloaded 9,000+ times). He is a member of the editorial board of the Journal of Web Semantics and has authored multiple books and over 70 publications. Education: PhD in Computer Science, Vrije Universiteit Amsterdam Master in Computer Science and Engineering, Politecnico di Milano Research Interests: Big Data and Stream Processing Semantic Web and Knowledge Representation Data Science and Real-Time Analytics Applications in IoT, Telecom, and Social Media Awards include the CAiSE’19 Best Paper Award, IBM Faculty Awards (2006, 2013), and multiple best paper recognitions. His work has been applied with industry partners like Telecom Italia, IBM, and Siemens. Teaching focuses on Big Data, Data Science, and AI, with courses such as 'Streaming Data Analytics' and 'Artificial Intelligence and Big Data Science'. He has coordinated EU projects (FP7 Service Finder, EIT Digital activities) and founded Quantia Consulting and motus ml.

Dana YangView profile

Assistant Professor

• Statistical Inference
• Machine Learning
• Computational Complexity
• +4 more

Dana Yang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. She joined Cornell in Spring 2022 after completing a Simons-Berkeley fellowship focusing on computational complexity of statistical inference at UC Berkeley. Previously, she was a postdoctoral associate at Duke University’s Fuqua School of Business. Her education includes a B.S. in Mathematics from Tsinghua University and M.A./Ph.D. in Statistics from Yale University. Yang’s research interests span statistical inference, machine learning, and computational complexity. She focuses on problems involving planted structures (e.g., matching recovery), privacy-preserving algorithms, high-dimensional statistics, and graph theory. Her work bridges theoretical foundations with practical applications in data science. Her recent articles explore phase transitions in statistical recovery, private convex optimization, and algorithmic fairness. Notable contributions include studies on planted matching problems, community detection efficiency, and secure sequential learning protocols. No scientific awards were explicitly mentioned in the provided materials. Her advising history and grant involvement remain unspecified in the current data. Dana Yang is affiliated with Cornell’s Comstock Hall facility, though specific lab or team affiliations were not detailed.

• Computational Geometry
• Data Structures
• Approximation Algorithms
• +1 more

Joachim Gudmundsson is Professor and Head of School of Computer Science at the University of Sydney, holding SOAR and ARC Future Fellowships. His research develops algorithms for computational geometry problems including spatio-temporal analysis, graph spanners, geometric approximation, and trajectory mining. Key research domains: Spatio-temporal pattern analysis Graph spanners and approximation algorithms Trajectory clustering and map matching Geometric network optimization Multi-robot coordination algorithms Recent publications demonstrate strong focus on trajectory analysis algorithms under Fréchet distance, geometric graph decompositions, and pattern formation in multi-robot systems. Work increasingly addresses dynamic and real-world graph structures. Recognized with prestigious fellowships: SOAR Fellow (Sydney Research Accelerator) ARC Future Fellow (Australian Research Council)

Melinda HodkiewiczView profile

Professor

• Maintenance Engineering
• Asset Management
• Data Science
• +6 more

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

• Computer Vision
• Machine Learning
• Object Detection
• +7 more

Du Huynh is an Associate Professor at the School of Physics, Mathematics and Computing, Department of Computer Science and Software Engineering, The University of Western Australia (UWA). She holds a 0.8 FTE appointment and has served as Graduate Research Coordinator since 2013. Her research focuses on Computer Vision, Machine Learning, Object Detection, and Video Analytics with applications in mineral processing, intelligent transportation systems, and medical imaging. She has secured over $3M in ARC grants and is a Chief Investigator in the Australian Centre for Quantum Growth ($18M funding). Education: BSc (Hons) and PhD in Computer Science from UWA. Prior to UWA, she lectured at Murdoch University for five years. Research Highlights Developed algorithms for automated mineral ore analysis, pedestrian trajectory prediction, and surgical instrument detection. Received three Best Paper Awards (ICPR2014, Kenneth Clarke Journal 2019, AusDM2022) and multiple award nominations. Editorial roles include PLOS One, Journal on Artificial Intelligence, and guest editorships for special issues in CVIU and Electronics. Grants & Projects Lead investigator in quantum computing algorithms for real-time optimization ($3M ARC funding). Collaborations with industry (e.g., Main Roads WA) on traffic video analytics and drone-based prediction systems. Academic Contributions Developed courses in Computer Vision, Machine Learning, and Java Programming. Supervised numerous HDR students and served on technical committees for WACV, ICCV, and DICTA conferences.