Find Academic Supervisors

Vasileios MegalooikonomouView profile

Professor

• Database and Knowledge Systems
• Intelligent Information Systems
• Data Mining
• +4 more

Vasileios Megalooikonomou serves as a Professor at the Department of Computer Engineering and Informatics within the School of Engineering at the University of Patras. He directs the Multidimensional Data Analysis and Knowledge Management Laboratory and maintains active teaching responsibilities across undergraduate and postgraduate programs. His research spans several interconnected domains in computer science and information systems. Dr. Megalooikonomou's work focuses on foundational database technologies while extending into advanced applications through Intelligent Information Systems and Data Mining . His expertise encompasses both theoretical frameworks and practical implementations in Pattern Recognition and Data Compression , with specialized applications in Biomedical Informatics and Multimedia systems. This multidisciplinary approach bridges traditional database theory with contemporary computational challenges. Within the University of Patras infrastructure, he leads the Multidimensional Data Analysis and Knowledge Management Laboratory , which serves as the operational hub for his research activities. His teaching portfolio includes core database courses (Databases I, Databases II, Database Laboratory), advanced algorithmic instruction (Data Mining and Learning Algorithms), and specialized postgraduate topics focusing on Spatial and Temporal Databases and Knowledge Mining. Office hours are maintained on Monday 11:00-13:00 and Wednesday 13:00-14:00 for student consultation.

Agnieszka LasotaView profile

Assistant Professor

• Optimization of production processes
• Enterprise operations optimization
• Petri nets (P/T type and s-nets)
• +5 more

Dr. Eng. Agnieszka Lasota is an Assistant Professor at the Institute of Mathematics, Faculty of Mathematics, Informatics and Econometrics, University of Zielona Góra, Poland. She is actively engaged in both research and teaching, supervising diploma theses and coordinating EU-funded projects. Research Interests Her scientific work focuses on the optimization and modelling of production processes and enterprise operations . She employs Petri nets of the P/T type and s-nets to analyse discrete-event systems and has a strong interest in fiber-optic and wireless telecommunications networks . Additional areas include the economic and legal analysis of statutory changes in the telecommunications investment process . Teaching Contributions She delivers courses on software engineering, web-application programming, databases in web applications, data warehouses, and multimedia in business. She also teaches investment market analysis, business-plan preparation, and investment financing, and runs diploma seminars for first-cycle (bachelor) students in Computer Science and Econometrics, Data Engineering, and Mathematics. Outreach & Service Member of the National Organizing Committee for the International Mathematics Competition "Mathematics Without Borders" (Junior & Senior categories) Delivers popular-science lectures for primary and secondary school pupils Promotes study programmes of the Faculty of Mathematics, Informatics and Econometrics and the University of Zielona Góra Facilitates cooperation between schools, industry, and the faculty Grants & Projects Dr. Lasota serves as author and coordinator of both “hard” and “soft” projects. She is a member of project-evaluation committees and acts as an economic expert for the National Centre for Research and Development. She has coordinated the EU Social-Fund project “Modern teaching and practical cooperation with entrepreneurs – development programme of the University of Zielona Góra” (POWR.03.05.0-00-00-Z014/18).

Georgina MirchevaView profile

Professor

• Bioinformatics
• Computational Biology
• Artificial Intelligence
• +4 more

Georgina Mircheva is a Professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Republic of Macedonia. Since 2007, she has held roles as a demonstrator and teaching/research assistant across multiple faculties, including the Faculty of Electrical Engineering and Information Technologies and the Faculty of Computer Science and Engineering. She teaches courses such as Databases, Artificial Intelligence, Data Mining, and Machine Learning. Bachelor of Science: Electrical Engineering (Computer Techniques, Informatics, and Automatics) - Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje (2007) Master of Science: Electrical Engineering (Computer Techniques and Informatics) - Same faculty (2009) PhD: Computer Science and Engineering - Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje (2014) Her research interests span Bioinformatics , Computational Biology , Artificial Intelligence , Data Mining , Multimedia Systems , Machine Learning , and Databases . She has authored or coauthored over 30 research papers in international journals and conference proceedings. Georgina offers office hours for consultations, including in-person sessions at TMF 138 and online meetings via the "Courses - Konsultacii" platform.

Apostol SlavchevView profile

Professor

• Training in sprint and hurdle running
• Specialized diversity in athletics
• Sports science
• +5 more

Professor Apostol Slavchev serves as Vice-Rector for Academic Affairs at the Vasil Levski National Sports Academy, where he holds a professorship in the Department of Athletics within the Faculty of Pedagogy. With over four decades of academic service, he has held leadership roles including Dean of the Faculty of Education (2008-2016) and membership on the Academic Council since 2007, demonstrating sustained institutional commitment to advancing sports education and athletic training methodologies. His academic foundation includes doctoral studies completed in 1998 with a dissertation on 400m hurdle training optimization, complemented by medical education at Sofia's G. Dimitrov State Medical University (1978-1982). His scholarly journey reflects a unique integration of medical science with athletic specialization: Doctorate in Sports Training Methodology (1998) Medical University Studies (1978-1982) Polytechnic High School Diploma (1972-1976) Slavchev's research spans sprint and hurdle training mechanics , athletics officiating systems , and inclusive athletic programming for diverse populations including children and Special Olympics participants. His work bridges theoretical sports science with practical coaching applications, particularly in developing technical proficiency in short-distance events through biomechanically-informed training protocols. The integration of mathematical modeling in sports ranking systems demonstrates his interdisciplinary approach to athletic administration. His 150+ publications reveal consistent focus on athletics pedagogy and technical officiating , with recent works emphasizing digital educational tools and longitudinal performance analysis. The trajectory shows evolving specialization from foundational coaching manuals toward sophisticated biomechanical analysis and institutional management systems, reflecting both deepening expertise and expanding administrative responsibilities. As an academic advisor, Slavchev has supervised four doctoral candidates to successful dissertation completion, with research concentrated in athletic training methodology. His institutional roles include directing postgraduate training for coaches and serving on the editorial board of the "Athletics & Science" journal, demonstrating commitment to knowledge dissemination. Slavchev founded and chairs the KLASA athletics club at the National Sports Academy while leading the Bulgarian Athletics Federation's judges committee since 2004. His practical coaching legacy includes developing over 100 athletes, with multiple national champions in hurdle events, demonstrating effective translation of academic research into competitive success.

• Artificial Intelligence
• Machine Learning
• Probabilistic Inferencing
• +5 more

Barry Drake serves as an Adjunct Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he is also affiliated with the Faculty of Engineering and Information Technology and the Data Science Institute. Additionally, he holds the position of Co-Chief Scientist at the Digital Health Cooperative Research Centre and participates in government advisory committees. His career bridges academic research and industry application, with significant experience in translating research into practical technology solutions for business and government sectors. Drake earned his PhD in Computer Science from UNSW Australia (1998-2003) and a BSc with First Class Honors in Computer Science from UTS (1991-1996). His educational background is complemented by a Certificate of Proficiency for Radio Fitter/Mechanic Electronic Systems from the Royal Australian Navy. His research focuses on algorithms and software for practical smart systems, with particular expertise in applications of AI and machine learning, probabilistic inferencing and knowledge compilation, learning probabilistic models, and fast-efficient near-neighbor searching in ultra-high dimensional spaces. His work demonstrates a clear trajectory toward health informatics applications , especially in health systems and delivery of health services, where he has made significant contributions through projects like the Lumos statewide linkage programme. The analysis of his publication record reveals a consistent pattern of research bridging theoretical computer science with practical healthcare applications. His recent work shows increasing focus on health data integration, patient journey modeling, and privacy-preserving synthetic data generation for healthcare applications, while maintaining his foundational expertise in probabilistic models and efficient search algorithms. Inventor on 20 filed patents ORCID identifier: 0000-0003-0572-9936 Drake actively supervises Masters and PhD students according to his profile, and his funded research portfolio includes multiple grants from the Digital Health CRC and NSW Health. His industry experience, particularly his 13 years at Canon Information Systems Research Australia where he served as Senior Principal Engineer and lead researcher for machine learning, informs his approach to technology research methods from a commercial perspective. His current projects focus on the impact of integrated care in New South Wales, synthetic data generation, and patient journey modeling. As Co-Chief Scientist at the Digital Health CRC, Drake contributes to a major national initiative focused on digital health innovation. His work with the Lumos programme has created Australia's first statewide linked data asset across primary care and other health settings, providing unique insights about cross-setting healthcare utilization. This initiative represents a significant contribution to health data infrastructure in Australia.

• Computer Architecture
• Parallel Programming
• Compilers
• +6 more

Martha A. Kim is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science. She serves as a Member of the Data Science Institute, Center Co-Chair of the Center for Computing Systems for Data-Driven Science, and Chair of the Computer Engineering Program. Professor Kim's research focuses on computer architecture with particular emphasis on the hardware-software interface. Her work develops tools and designs that help computer systems operate efficiently and intuitively, including database accelerators for energy-efficient queries, techniques for optimizing parallel software to better utilize hardware resources, and improving the versatility of specialized hardware accelerators. She leads the ARCADE Lab where she conducts research in computer architecture, parallel programming, compilers, and low-power computing. Her publication record shows consistent output at top-tier conferences including ASPLOS, MICRO, MEMOCODE, ISLPED, DAC, DaMoN, CC, and CGO, with recent work spanning video transcoding in the cloud, pipelined dataflow circuits, thermal monitoring, and database acceleration. Professor Kim has received several prestigious awards: 2013 Rodriguez Family Award 2015 Edward and Carole Kim Faculty Involvement Award 2013 NSF CAREER award 2016 Anita Borg Early Career Award She currently advises three doctoral students (Martha Barker, Thomas Repetti, and Andrea Lottarini) and has previously graduated PhD students Melanie Kambadur (2016) and Lisa Wu (2014). Her research is supported by C-FAR, DARPA, Google, Intel, and NSF. Professor Kim teaches Fundamentals of Computer Systems, Computer Architecture, and Principles and Practice of Parallel Programming courses at Columbia University. The ARCADE Lab provides opportunities for students to work on cutting-edge research in hardware-software interface optimization, with applications in energy efficiency, database acceleration, and parallel computing systems.

Jun MuraiView profile

Professor

• Information network
• Multimedia database
• Information security

Jun Murai is a Professor with research interests in Information networks, Multimedia databases, and Information security. His work focuses on information technology, network systems, database optimization, and cybersecurity challenges.

Steffen StaabView profile

Professor

• Business Information Systems
• Databases
• Human-Computer Interaction
• +14 more

Steffen Staab is a full professor at the University of Koblenz-Landau (Germany) for Databases and Information Systems and at the University of Southampton (UK) for Web and Computer Science. Education: Studied Computer Science and Computational Linguistics at Friedrich-Alexander University Erlangen-Nuremberg (Germany), University of Pennsylvania (USA), and University of Freiburg (Germany). Research Interests: Span a wide range of fields, including Ontologies , Semantic Web , Social Web , Data Engineering , Text Mining , Human-Computer Interaction (HCI) , Software Modelling , and Programming . His work focuses on improving how text and data are understood and utilized across domains. Keynote Engagements: Delivered keynotes at conferences such as WEBIST 2019 , ICEIS 2012 , and ENASE 2012 .

• Information Communication Systems
• Multimedia Information Processing
• Content Distribution Systems
• +4 more

Wataru Kameyama is a Professor in the Department of Communications and Computer Engineering at Waseda University’s School of Fundamental Science and Engineering. Since 2014 he has held a full-time faculty position at Waseda; he previously served as Professor at GITS (2002–2014) and Associate Professor at GITI (1999–2002), both within Waseda University. He received his M.E. and Ph.D. in Electronics Engineering from Waseda University in 1987 and 1990 respectively. Education: Ph.D. in Engineering, Waseda University, 1990 M.E. in Electronics Engineering, Waseda University, 1987 B.E. in Electronics Communication Engineering, Waseda University, 1985 Research Interests: Prof. Kameyama’s research spans information communication systems, multimedia information processing, content distribution architectures, Named Data Networking, digital rights management, and high-dimensional data mining. His recent work emphasizes proactive content-caching for mobile video, producer mobility in NDN, and outlier-detection algorithms for large-scale datasets. Publications Trend: Across 85 peer-reviewed papers (h-index 9) his recent articles focus on next-generation networking (NDN, ICN), mobile multimedia delivery using transportation infrastructure, and data-mining techniques for high-dimensional outliers, often validated through large-scale field experiments. Awards & Honors: International Cooperation Award, ITU Association of Japan (2012) IEEE/IEICE Distinguished & Outstanding Contribution Awards (2009, 2007) Best Author/Best Paper Awards, Institute of Image Information and Television Engineers (2009, 2006) Professional Memberships: ACM, IEEE, Institute of Image Electronics Engineers of Japan, Information Processing Society of Japan, Institute of Electronics, Information and Communication Engineers, Institute of Image Information and Television Engineers.

Olusegun Opeyemi AyejuyoView profile

Professor

• Analytical Chemistry
• Environmental Chemistry
• Trace Analysis
• +3 more

Professor Olusegun Opeyemi Ayejuyo serves as Professor of Analytical Chemistry in the Department of Chemistry, Faculty of Science, University of Lagos, holding concurrent leadership roles as Head of Department (since August 2022) and former Acting Dean of Faculty (December 2024-May 2025). With 26 years of academic service, he maintains active research programs and institutional governance responsibilities including Senate representation and editorial leadership. His academic credentials include: B.Sc. in Chemistry, University of Benin (July 1985) M.Sc. in Analytical Chemistry, University of Ibadan (September 1987) Ph.D. in Analytical Chemistry, University of Ibadan (May 1995) Diploma in Environmental Management, Gallille College, Mizra Israel (September 2008) Prof. Ayejuyo pioneers advanced analytical methodologies for environmental contaminant detection, specializing in HPLC, GC-ECD, AAS, and UV-Visible spectrophotometry applications. His work targets trace-level quantification of Persistent Organic Pollutants, Polycyclic Aromatic Hydrocarbons, and Potentially Toxic Metals across multimedia environmental matrices. Current projects address air quality monitoring, microplastic pollution dynamics in the Gulf of Guinea, and biotransformation of agro-industrial waste streams. His 2022-2024 publications reveal concentrated focus on African environmental challenges, particularly microplastic contamination pathways in West African coastal systems and policy implementation gaps for global pollution conventions. Research integrates field sampling with multivariate statistical analysis to establish contaminant source fingerprints and ecological risk profiles. Major recognitions include: 5th Nigerian Merit Gold Award for Productivity (2013) Israeli Scholarship Award for Environmental Management Training (2008) Aldrich Chemical Company Recognition for phytochemical research (1994) Mentoring over 100 postgraduate students, he directs significant research funding through: TETFund 2022 Grant: Legacy Air Pollutants monitoring TETFund 2022 Grant: Indoor dust air pollution studies TETFund 2020 Grant: Biconversion of agro/textile waste to industrial chemicals University Central Research Grant (2011): Endocrine disruptors in Lagos Lagoon He co-leads the World Bank Center of Excellence for Environmental Standards Air Quality in Africa and maintains active collaboration with the University of Birmingham Pollution Laboratory. As Editor-in-Chief of the Faculty of Science Journal and Chairman of SON's Technical Committee on Natural Waters, he shapes national environmental standards while contributing to the African Regional Standard Organisation and Chemical Society of Nigeria.

• Cybersecurity
• Information Security
• Computer Science
• +4 more

Gerald Quirchmayr is a Professor at the Faculty of Computer Science , leading the Research Group Multimedia Information Systems . His work focuses on Cybersecurity , Information Security , and Privacy Requirements Engineering , with applications in logistics, e-health, and cybersecurity education. Recent research trends include the integration of graph databases for ICT risk models, situational awareness in cybersecurity systems, and privacy-aware software development methodologies. Key sub-fields span Logistics Security Contextual Integrity Cybersecurity Education Data Flow Diagrams Knowledge Graphs Risk Management Scientific Awards Best Paper Award (2016) for 'Implementation of a Generic ICT Risk Model using Graph Databases' Quirchmayr actively contributes to educational initiatives , including the Sec Tech project (2016-2018) for cybersecurity teaching and the ongoing Dynamic Cybersecurity Management project (2022-2025). His lab develops tools like PrivacyStory to enhance privacy awareness in user stories.

Roy VillafaneView profile

Associate Professor

• Data Mining
• High Performance Computing
• Distributed Computing
• +3 more

Roy Villafane serves as an Associate Professor in the Department of Computing at Andrews University, bringing industry experience from Symantec Corporation and the University of Central Florida's IT departments to his academic role. His foundational training includes BS, MS, and PhD degrees in Computer Science from the University of Central Florida. His educational qualifications include: PhD in Computer Science, University of Central Florida MS in Computer Science, University of Central Florida BS in Computer Science, University of Central Florida Professor Villafane's research centers on data mining, high performance computing, and distributed systems, with notable publications addressing transaction recovery in federated databases, broadcast techniques for vehicular networks, and algebraic data mining for performance analysis. He conceptualizes computing as a modular creative process where basic building blocks enable infinitely complex constructions, a perspective he actively shares with students across various computing courses. His work consistently bridges theoretical computer science with practical system implementation challenges. No scientific awards are documented in the available information, though his publication record spans over a decade with contributions to IEEE proceedings and journals. Regarding student mentorship, while specific current advisees aren't listed, his doctoral thesis and collaborative publications indicate research supervision experience. His industry background at Symantec suggests practical applications of his research, though active grant funding isn't specified in the provided materials. There is no indication of a dedicated research laboratory or named research group in the available documentation, though his collaborative publications suggest interdisciplinary project work.

Gabriele KotsisView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +6 more

Gabriele Kotsis is a Full Professor at the Institute of Telecooperation, Johannes Kepler University Linz, with extensive contributions to Artificial Intelligence research. Her institutional presence spans multiple departments through interdisciplinary projects while maintaining her primary affiliation with JKU's engineering-focused research units. Her research expertise encompasses: Natural Language Processing and Neural Machine Translation systems Reinforcement Learning applications for Smart Grid optimization Mobile Computing and Multimedia Intelligence frameworks Big Data Analytics and Database Systems innovation Human-centered AI development methodologies Recent publications (2024-2025) reveal a strategic focus on practical AI implementations addressing multilingual communication barriers and energy efficiency challenges. Her work consistently bridges theoretical AI advances with real-world applications across multiple domains. Professor Kotsis maintains an active supervision record with thesis guidance and leads multiple funded research initiatives. Her current portfolio includes: 'Enhancing Neural Machine Translation' project (2025-2026) for Southeast Asian languages 'INTES' regulatory compliance ecosystem (2023-2026) 'Human-centered Artificial Intelligence' initiative (2022-2026) Through her leadership in international conferences (iiWAS, MoMM, DEXA) and editorial roles for major proceedings, she maintains significant influence in the global computer science research community while advancing JKU's research profile.

Lee Sue HanView profile

Senior Lecturer

• Computer vision
• Artificial intelligence
• Machine Learning
• +2 more

Dr. Lee Sue Han serves as Senior Lecturer and Head of Department for Information and Communication Technologies at Swinburne University of Technology Sarawak's Faculty of Engineering, Computing and Science. She joined Swinburne in April 2020 following post-doctoral research with Pl@ntNet (CIRAD and INRIA) in France. Her academic credentials include a BEng (Hons) in Electronic Engineering from Multimedia University (2011), MSc in Electrical and Electronic Engineering from Shinshu University, Japan (2014), and PhD in Computer Science from University of Malaya (2018). Dr. Lee's research bridges artificial intelligence and plant biology, focusing on computer vision, machine learning, and deep learning applications for plant disease identification, cross-domain classification, and environmental monitoring. Her work explores how deep learning models interpret botanical features and align with human perception in disease diagnosis, with significant contributions to automated plant health systems. Recent publications (2021-2022) demonstrate advanced work in multi-task learning for disease identification, global-scale plant recognition, and interpretable AI models. Her research consistently appears in top venues including Frontiers in Plant Science, IEEE Transactions, and Pattern Recognition. Her accolades include the 2022 Outstanding Student Feedback on Teaching award, 2022 Swinburne Research Success Award, and Best Paper Awards in 2020 and 2018. She actively secures research funding as PI for FRGS 2021 and co-PI for FRGS 2022 and SDEC 2021 projects. Dr. Lee supervises research in generative models, sign language production, drug discovery, animal re-identification, and plant disease modeling. She maintains international collaborations initiated during her France-based post-doctoral work and participates in global challenges like PlantCLEF.

• Web Technology
• Relational Databases
• Ajax Applications
• +4 more

Samir Hijazi is a tenured Professor at Texas Lutheran University with affiliations including his role as President-elect of the Association of Computer Educators in Texas (ACET). He holds multiple degrees including a D.B.A. in Information Systems (University of Sarasota), M.A. in Economics (Morgan State University), B.S. in Computer Science (Towson State), and A.A. in Computer Science (Montgomery College). His research spans several key technology domains: Web Technologies: AJAX applications, WAMP servers, and JavaScript frameworks Educational Systems: Virtual learning communities, portable teaching tools Information Management: Relational databases, knowledge management models Instructional Innovation: Web programming pedagogy, technology-enhanced learning Publications predominantly explore practical applications of technology in education, with recent works focusing on distance learning solutions and web development tools. Awards and recognitions: Servant Leader of the Year (2001) Leadership position in ACET He actively collaborates with students on technology projects and maintains conference participation through ACET and ASCUE, presenting on educational technology innovations.