Find Academic Supervisors

Nikolaus AugstenView profile

Professor

• Database Systems
• Data-Centric Applications
• Approximate Matching
• +4 more

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

Peiyuan PanView profile

Senior Lecturer

• Software System Development
• Web-based Applications
• Artificial Intelligence Technologies
• +3 more

Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.

• Computer Science
• Game Design
• Database Systems
• +3 more

Walker M. White is the Stephen H. Weiss Provost's Teaching Fellow and Director of the Game Design Initiative (GDIAC) at Cornell University's Department of Computer Science. He leads the undergraduate game design minor and teaches core courses like CS/INFO 3152 (Introduction to Game Design) and CS/INFO 4152 (Advanced Topics in Game Design). His research focuses on data-driven games, database systems (e.g., Cayuga), and inquiry-based learning. He previously served as an assistant professor at the University of Dallas, specializing in mathematics and computer science education. His work bridges game design, database efficiency, and pedagogical innovation. Education & Teaching: PhD in Mathematical Logic (implied by research background) Assistant Professor of Mathematics and Computer Science at University of Dallas Developed inquiry-based learning methods (Moore Method) for proof-based courses Research Interests: Data-driven game design, declarative processing for games, scalable database systems, and educational technology. His projects include the SGL language for game logic and Cayuga for data stream processing. Teaching Contributions: Transitioned CS 1110 to Python, lowering barriers for beginners Independent study projects featured at indie game festivals Liaison for game studio recruitment (EA, Valve, etc.) Awards & Recognition: None explicitly listed, but contributions to game design education and database research are highlighted. Advising & Labs: Oversees GDIAC, which connects multiple departments. Manages competitive independent study programs and advises projects on mobile game development and NPC behavior systems.

• Big Data Analytics
• Distributed Machine Learning
• Wireless Communication
• +3 more

Shadi Khalifa is an Adjunct Assistant Professor with expertise in Big Data Analytics , Distributed Machine Learning , and Wireless Communication . His work bridges Cloud Computing and Data Mining with practical applications in MapReduce , Analytics-as-a-Service , and LTE Systems . Research Trends : Analysis of Shadi Khalifa’s publications reveals a focus on distributed computing and big data optimization . Key themes include inter-cell interference coordination in wireless networks, smart big data frameworks , and cloud-based analytics . His work emphasizes scalability, efficiency, and integration of machine learning with infrastructure.

• Biomedical Informatics
• Machine Learning
• Data Mining
• +5 more

Dr. Yanqing Ji is a Professor in the Department of Electrical and Computer Engineering at Gonzaga University, specializing in biomedical informatics and high-performance computing. He serves as an editor for the Journal of Digital Signal Processing and a reviewer for 23 international journals. Education: Ph.D. in Computer Engineering, Wayne State University M.Sc. in Physical Electronics, University of Science & Technology of China B.E. in Industrial Automation, Qingdao University Dr. Ji's research focuses on biomedical association mining, adverse drug reaction detection, and parallel/cloud computing applications in healthcare. His work integrates machine learning, fuzzy logic, and multi-agent systems to enhance drug safety surveillance and biomedical literature search. His publications emphasize scalable MapReduce solutions for biomedical text mining, temporal association mining for drug interactions, and intelligent agent systems for pharmacovigilance. These span prestigious conferences like IEEE BIBM, ITNG, and NAFIPS, alongside journals such as BMC Bioinformatics and IEEE Transactions on Knowledge and Data Engineering . Scientific awards include NIH R21, AHRQ R18 grants, and NSF-funded projects (IGERT 9987598, MRI 9977815, ITR 0081696). He has led system design for distributed healthcare applications and optimized decision models using genetic algorithms.

Fadila BentayebView profile

Associate Professor

• NoSQL Data Warehouses
• Text Warehousing
• Personalization & Recommendation Systems
• +5 more

Fadila Bentayeb is an Associate Professor (HDR) at the University of Lyon 2, affiliated with the ERIC Laboratory and the Department of Informatics and Statistics (DIS). She leads the Complex Data Warehousing and OLAP research team (SID) and has served as Head of the first year of the Master's degree in Computer Science at the Institute of Communication (ICOM) since 2014. Her work bridges academic research and practical applications in data-intensive systems. HDR (Habilitation to Direct Research), University of Lyon 2 (2011) Ph.D. in Computer Science, University of Orléans (1998) Her research focuses on NoSQL data warehouses, text warehousing, and personalization systems, with emphasis on scalable architectures and context-aware analytics. Recent work explores AI-driven security in IoT networks and temporal modeling for evolving data structures. Key trends in her publications include: (1) Graph-based multidimensional modeling (GAMM framework), (2) Optimization of distributed big data warehouses using machine learning, (3) Medical text classification via hybrid CNN-LSTM models, and (4) Temporal data warehouse evolution. As part of the ERIC Laboratory’s leadership team, she contributes to strategic research directions and mentors students in advanced data systems. Her technical expertise extends to Hadoop/Spark optimization, OLAP cube design, and semantic integration of heterogeneous data sources.

LI RongView profile

Professor

• Cellular Dynamics
• Mechanobiology
• Biomechanics
• +6 more

LI Rong is a Professor (courtesy appointment) in the Department of Mechanical Engineering at the National University of Singapore (NUS), and Director of the Mechanobiology Institute (MBI), a Research Centre of Excellence at NUS. She concurrently holds a Distinguished Professorship in the Department of Biological Sciences at NUS. Previously, she served as Bloomberg Distinguished Professor of Cell Biology at Johns Hopkins School of Medicine and Department of Chemical and Biomolecular Engineering at Johns Hopkins University. Her research focuses on cellular dynamics and mechanics, integrating genetics, quantitative imaging, biophysical measurements, mathematical modeling, genomics, and proteomics. This interdisciplinary work aims to understand how eukaryotic cells transmit genomes, adapt to environments, and organize for specialized functions. Her recent publications span entity profiling, temporal data management, and big data analytics systems. Key contributions include the MAROON+ system for temporal entity profiling, HC-Store for hybrid MapReduce-RDBMS systems, and frameworks for optimizing algorithmic trading strategies using MapReduce. LI Rong's leadership roles include directing the Mechanobiology Institute and holding distinguished appointments across multiple institutions. Her work bridges engineering, biology, and computational sciences, reflecting her expertise in both mechanobiology and big data systems.

Joseph LetourneauView profile

Lecturer

• Incident Response
• Digital Forensics
• Computer Networking
• +1 more

Joseph Letourneau is an Instructor in the Division of Information Technology & Sciences at Champlain College. He holds expertise in Incident Response, Digital Forensics, and Cybersecurity, with a focus on enhancing student engagement through structured and interactive teaching methodologies. His professional background includes work in Managed Security Services, covering areas like Vulnerability Assessments, PCI Investigations, and Malware Analysis. Research Interests: His research spans cybersecurity practices, digital forensics techniques, and the application of clustering algorithms in big data analysis. He emphasizes practical, hands-on learning to prepare students for real-world challenges in IT security and networking. Articles Overview: His publications explore density-based clustering methods for big data, sentiment analysis frameworks, and cybersecurity threat detection. Notably, he has contributed to optimizing clustering algorithms using MapReduce and enhancing digital forensic methodologies. Advising & Grants: No specific advising or grant information is highlighted in the provided materials.

Theodoridis IoannisView profile

Professor

• Data Science
• Databases
• Big Data Management
• +3 more

Professor Theodoridis Ioannis is a distinguished faculty member in the Department of Informatics at the University of Piraeus, where he serves as Director of the Data Science Laboratory within the School of Information and Communication Technologies. With a career spanning over two decades, he has established himself as a leading expert in data management and analysis. His research interests focus on Data Science, particularly in databases, big data management, data mining, and geoinformatics. Professor Theodoridis has made significant contributions to spatial database systems, time series analysis, and distributed data processing. His work bridges theoretical foundations with practical applications in areas such as smart cities, mobility analytics, and scientific data management. His publication record demonstrates consistent research productivity with over 100 peer-reviewed articles in top-tier venues, accumulating more than 10,000 citations. His research output shows a clear evolution from foundational database techniques toward contemporary challenges in big data analytics, machine learning integration, and privacy-preserving methods. Member of editorial board of ACM Computing Surveys (since 2016) Reviewer for numerous international journals and conferences Active participant in data management conference committees Professor Theodoridis has secured significant research funding through Horizon 2020 projects, serving as project coordinator and research team leader since 2001. His work demonstrates strong industry and academic collaboration, with applications spanning multiple domains. He has also co-authored three influential monographs in his field. He leads the Data Science Laboratory, which serves as a hub for interdisciplinary research at the intersection of database systems, machine learning, and domain-specific applications. The laboratory fosters collaboration between computer scientists, domain experts, and industry partners to address real-world data challenges.

• Database Systems
• Big Data Management
• XQuery Processing
• +3 more

Vinayak R. Borkar is a researcher and software engineer affiliated with the University of California, Irvine, where he completed his PhD in 2016. His work focuses on big data platforms, database systems, and scalable query processing frameworks. PhD in Big Data Processing (UC Irvine, 2016) Contributions to Apache AsterixDB, Hyracks, and Pregelix Industry experience at BEA Systems (2000s) Research interests include database systems , big data management , XQuery optimization , and dataflow engines . His publications analyze scalable similarity queries, memory management, and declarative approaches to machine learning. Recent articles explore Apache AsterixDB , dataflow compilation , and graph analytics . Collaborators include Michael J. Carey and Alexander Behm.

Raj BhatnagarView profile

Professor

• Data Mining
• Pattern Recognition
• Artificial Intelligence
• +4 more

Raj Bhatnagar is a Professor in the Department of Computer Science at the University of Cincinnati, affiliated with the College of Engineering, Architecture and Art. His research focuses on data mining, pattern recognition, AI, and algorithms for bioinformatics. He has secured over 25 grants from federal agencies (NSF, Air Force Research Laboratory), industry (Charles Schwab, Edaptive Computing), and state/private entities, totaling millions in funding. Education: PhD in Computer Science from University of Maryland College Park (1989) Research Interests: His work spans distributed computing, graph theory, machine learning for industrial applications, and cybersecurity. Notable contributions include algorithms for clustering, anomaly detection, and formal concept analysis. He collaborates with Cincinnati Children’s Hospital and the Air Force on projects like predictive analytics for spindle health and verification of microelectronics designs. Recent Grants: Includes a 2023 industry grant for Machine Learning educational seminars (MME I45I), and multiple ongoing Air Force contracts (2018-2022) for ML-based verification test processes. Labs/Teams: Involved with Edaptive Computing’s Data Analytics & Cybersecurity Simulation Center and the Air Force-led STAMP/TAME research initiatives.

• Image Analysis
• Pattern Recognition
• Document Analysis
• +3 more

Dr Christian Clausner is a Research Fellow at the University of Salford, affiliated with the School of Science, Engineering & Environment and the Informatics research center. He holds a part-time PhD in Computer Science, completed in parallel with his academic role. His research focuses on image analysis, pattern recognition, and document analysis, with a particular emphasis on OCR technology, historical document digitization, and semantic metadata frameworks. Education: Studied Computer Science and Artificial Intelligence in Germany. Professional Experience: Joined academia in 2009 after private sector work, specializing in technical workflows for large-scale document digitization projects. Research Highlights: Developed NAME XML for entity-relation tagging, contributed to OCR evaluation tools, and led initiatives like the 1961 UK Census digitization and crowdsourcing for tabular data extraction. He co-chaired HIP workshops in 2021 and 2023, advancing historical document processing standards. Key Projects: WaterAGRI (2020–2024), Europeana Newspapers OCR workflow, and the Medical Officer of Health Reports scoping study. His work bridges computer science and cultural heritage, addressing challenges in text recognition, layout analysis, and semantic labeling.

Panagiotis StamatopoulosView profile

Assistant Professor

• Artificial Intelligence
• Constraint Programming
• Logic Programming
• +9 more

Panagiotis Stamatopoulos is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been employed since 1993. He holds a PhD in Computer Science (1988) and a Diploma in Physics (1982) from the University of Athens. His research spans artificial intelligence, constraint programming, natural language processing, machine learning, and optimization. Specific interests include: Hybrid approaches combining constraint programming with operations research Natural language understanding for database access Parallel processing and distributed constraint solving Multi-agent systems and web intelligence applications His publications show consistent focus on constraint satisfaction algorithms, text summarization techniques, educational timetabling systems, and AI applications in diverse domains like sports analytics and robotics. Recent works demonstrate increased attention to NLP evaluation metrics and multimodal learning. He has supervised numerous diploma theses and led projects funded by the European Union (EDS, APPLAUSE, PARACHUTE, PARROT), University of Athens, and Olympic Airways. Stamatopoulos teaches undergraduate courses in Introduction to Programming and Logic Programming, plus postgraduate courses in Advanced Artificial Intelligence. He previously taught Artificial Intelligence, System Programming, and Expert Systems.

Gheorghi GuzunView profile

Associate Professor

• Data Management
• Machine Learning
• Data Compression
• +4 more

Dr. Gheorghi Guzun is an Associate Professor in the Department of Computer Engineering at San José State University (SJSU), where he has served since 2017. He holds a Ph.D. in Electrical and Computer Engineering from the University of Iowa (2016) and a BSc in Electrical Engineering from the Technical University of Moldova (2010). His research focuses on scalable data management systems, machine learning, and hardware-driven software design, with applications in energy-efficient data centers, infrastructure monitoring, and predictive analytics. He is a recipient of the NSF CAREER Award for his work on efficient AI systems. Education background: Ph.D. in Electrical and Computer Engineering, University of Iowa (2016) BSc in Electrical Engineering, Technical University of Moldova (2010) Research interests span data compression, large-scale indexing, and algorithm optimization for machine learning. His projects emphasize scalability and performance in distributed systems, with recent work addressing energy efficiency and sparsity-driven methods for AI. He also develops educational technologies, such as competitive learning platforms to enhance student engagement. Notable achievements include the NSF CAREER Award and contributions to infrastructure monitoring systems, flood event modeling, and predictive university admission yield estimation. His lab's work is documented at https://g-guzun.github.io/ . Grants and advising: His NSF-funded CAREER project explores scalable sparsity-driven methods for AI systems. He collaborates on projects involving distributed data analytics and maintains an active research lab at SJSU.

Ashwathy AshokanView profile

Academic

• Computer Science
• Artificial Intelligence
• Machine Learning
• +4 more

Ashwathy Ashokan is an Instructor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. With a Master's in Computer Science from UNO and a PhD in Artificial Intelligence and Machine Learning underway, she bridges industry experience (Union Pacific, Microsoft, Wipro) with academia. Her research focuses on algorithmic fairness, recommender systems, and text mining. Research Interests span Text Mining , Information Retrieval , Recommender Systems , Search Engines , Algorithmic Fairness , and Machine Learning . Her recent work in Information Processing & Management (2021) highlights fairness metrics in rating predictions, while earlier publications (2013-2014) explore MapReduce-based topic extraction from timestamped documents. Scientific Awards include the Best Graduate Student for Service Award UNO Advantage Scholarship Special Faculty Development Fellowship Volunteer Activities include involvement with Omaha-based organizations like Girls Who Code, Open Door Mission, and Nebraska AIDS Project. She has served as a Committee Member for UNO's Women in IT Initiative.