Find Academic Supervisors

Gregory GayView profile

Associate Professor

• Software Testing
• Search-Based Software Engineering
• AI for Software Engineering
• +4 more

Gregory Gay is an Associate Professor in the Interaction Design and Software Engineering division within the Department of Computer Science and Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden. His academic profile spans numerous software engineering conferences where he has served as committee member, program chair, and active researcher since at least 2018. Dr. Gay's research focuses on the intersection of software engineering and artificial intelligence, with particular emphasis on: Software Testing and Analysis Search-Based Software Engineering AI for Software Engineering (AI4SE) AI Engineering Automation of development tasks Software Carbon Footprint and sustainability His recent publications demonstrate a strong trend toward applying AI and optimization techniques to software testing challenges, with increasing focus on sustainability aspects of software development. Many studies take an industrial perspective, examining real-world applications in automotive software systems. His work blends theoretical foundations with practical applications, making significant contributions to both academic research and industrial practice in software engineering. Dr. Gay has been actively involved in numerous top software engineering conferences including ASE, ICSE, ESEC/FSE, ISSTA, and ICST, serving on program committees and organizing tracks. His research methodology typically combines optimization, artificial intelligence, and machine learning to help developers deliver complex systems in a safe, secure, and efficient manner.

• Cybersecurity
• Privacy
• Networking for Big Data
• +4 more

Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.

Eduard GröllerView profile

Professor

• Visual Computing
• Scientific Visualization
• Volume Visualization
• +5 more

Eduard Gröller is a Full Professor of Visualization at the Vienna University of Technology (TU Wien), leading the Research Unit of Computer Graphics and the Visualization Group within the Institute of Visual Computing & Human-Centered Technology (VC&HCT). He holds adjunct professorships at the University of Bergen, Norway, and chairs several academic committees. His academic journey began with a PhD in 1993 from TU Wien, followed by extensive contributions to visualization research. His research focuses on visual computing, scientific visualization, and medical imaging, with applications in energy modeling, climate analysis, and molecular biology. Gröller has pioneered methods like BEMTrace for BIM-based energy models and HORA 3D for flood risk visualization. He co-authored over 300 publications, including foundational work in IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum . Gröller's awards include the IEEE VGTC Technical Achievement Award (2019), Eurographics 2015 Outstanding Technical Contributions Award, and Fellow of the Eurographics Association (2009). He actively contributes to conferences like EuroVis and IEEE Visualization as program chair and reviewer. His educational efforts span courses in visual computing, graphics, and data analysis, with a focus on immersive tools like ImNDT for material data exploration. Current projects include Climate-Sensitive Adaptive Planning for Resilient Cities, Visual Analytics in Radiation Therapy, and scalable web-based visualization techniques. He leads teams in VRVis, a research center for applied visualization, and collaborates internationally on medical imaging and environmental data challenges.

Vatsal MaruView profile

Assistant Professor

• Applied Machine Learning
• Optimization Algorithms
• Scheduling Algorithms
• +5 more

Vatsal Maru is an Assistant Professor of Instruction in the Department of Information Systems at the Jindal School of Management (JSOM), University of Texas at Dallas. His expertise lies at the intersection of applied machine learning, optimization algorithms, and industrial operations. He holds a PhD in Information Systems (2022), MS (2017), and BE (2013) from Wichita State University and Gujarat Technological University, respectively. His research focuses on leveraging AI and optimization techniques to address complex challenges in supply chain networks, scheduling systems, and cyber-physical frameworks. Key areas include real-time industrial operations, decentralized decision-making, and crisis-resilient logistics. Courses taught include Object-Oriented Programming in Python, Web Analytics, and MIS-related curricula. Maru’s recent work explores deep reinforcement learning applications for aircraft scheduling and vehicle routing optimization, alongside IoT-driven cyber-physical systems for smart manufacturing. His publications emphasize real-world problem-solving, such as improving pandemic-era supply chains and enhancing student outcomes via assessment frameworks. Active in professional organizations like the Society of Manufacturing Engineers and IISE, he bridges academic research with industrial practice. His contributions span over a decade of work in computational intelligence, optimization modeling, and systems design.

Guido TironeView profile

Associate Professor

• Web Analytics
• Machine Learning
• Customer Insights

Guido Tirone is an Associate Professor of Practice in Marketing at the Jindal School of Management (JSOM), University of Texas at Dallas. He teaches courses such as MKTG 6352 (Marketing Web Analytics and Insights) and MKTG 6V99 (Understanding the Marketing Data Ecosystem). His research focuses on Web Analytics, Machine Learning applications in marketing, and Customer Insights. Education: MBA from SMU Cox School of Business (2018) MSc from Bocconi University, Milan, Italy (2012) BA from Università Cattolica del Sacro Cuore, Rome, Italy (2018) His work integrates quantitative methods with marketing strategy, emphasizing data-driven decision-making. No specific grants, awards, or lab affiliations are mentioned in the provided text.

Maryam SafariView profile

Senior Lecturer

• Accounting
• Accountability
• Corporate Governance
• +4 more

Dr. Maryam Safari is a Senior Lecturer at RMIT University in the School of Accounting , Department of Accounting, Info Sys & Supply Chain. Specializes in accounting, corporate governance, ethics, and qualitative research Focuses on social capital, technology impact, and accountability frameworks Her research spans: Corporate Governance : Board effectiveness, ASX compliance, remuneration structures Accountability : Multinational corporate responsibility, post-disaster reforms Education : ICT integration in curricula, learner engagement dynamics Sustainability : Environmental disclosures, GRI standards Recent publications examine: Gender equality implementation challenges Digital transformation in accounting education Voluntary disclosure practices during crises Scientific awards include: 2025 Wiley Top Cited Article Award for work in Accounting & Finance Professional roles: Chair, Accounting History Special Interest Group (AFAANZ) Chair, RMIT Women Researchers’ Network Editor, Issues in Accounting Education journal Open to Masters Research and PhD student supervision .

• Blockchain and FinTech
• Deep Learning
• Text Mining
• +3 more

Rong Liu is an Associate Professor at the School of Business, Stevens Institute of Technology, specializing in Information Systems and FinTech. His research focuses on Blockchain, Deep Learning, Text Mining, and Business Process Management. Prior to Stevens, he was a Research Staff Member at IBM T.J. Watson Research Center (2006–2017). Liu holds a PhD in Information Systems from Penn State University (2006). Research Interests Liu’s work integrates AI and business analytics to address challenges in finance, healthcare, and operations. Key areas include blockchain applications in supply chains, ethical AI in hiring, misinformation detection, and predictive modeling for fraud and litigation. His methodologies combine deep learning with theory-driven approaches, emphasizing interpretability and real-world impact. Recent Trends in Publications His recent work explores large language model (LLM) applications in healthcare (e.g., drug shortage prediction), financial decision-making systems, and ethical compliance in job advertisements. He also investigates blockchain’s role in supply chain transparency and open-source development’s impact on ICOs. Awards & Recognition Liu has received awards including the Bright Idea Award (New Jersey Business Faculty, 2020) and multiple best paper awards at ICIS, WITS, and INFOCOM conferences. His work on supply chain event management (2007) was recognized with a Best Paper Award at the Business Process Management Conference. Service & Teaching He serves on academic committees at Stevens and reviews for top journals like MIS Quarterly and Production and Operations Management. Courses taught include Web Mining, Deep Learning for Business Analytics, and Large Language Models in Finance.

Enayat RajabiView profile

Associate Professor

• Machine Learning
• Knowledge Graphs
• Data Analytics
• +3 more

Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications

Pascal HitzlerView profile

Professor

• Neuro-Symbolic Artificial Intelligence
• Semantic Web
• Knowledge Graphs
• +4 more

Pascal Hitzler is the University Distinguished Professor and endowed Lloyd T. Smith Creativity in Engineering Chair at Kansas State University's Department of Computer Science within the Carl R. Ice College of Engineering. He serves as Director of the Center for Artificial Intelligence and Data Science (CAIDS) and Director (Research) of the Institute for Digital Agriculture and Advanced Analytics (ID3A). Previously, he held roles at Wright State University, including NCR Distinguished Professor and Director of Data Science. His academic journey includes a PhD in Mathematics from University College Cork (2001) and a Diplom in Mathematics from the University of Tübingen (1998). Affiliations: Kansas State University (2020–present), Wright State University (2009–2020), University of Karlsruhe (2004–2009), TU Dresden (2001–2004). Education: PhD in Mathematics (2001, UCC Cork), Diplom in Mathematics (1998, Tübingen). His research focuses on neuro-symbolic AI, semantic web technologies, knowledge graphs, and ontology modeling. Key contributions include founding editorships of the Neurosymbolic Artificial Intelligence and Semantic Web journals, co-authoring the Foundations of Semantic Web Technologies textbook, and pioneering ontology design patterns. His work emphasizes semantic web standards like OWL and integrates explainable AI with deep learning. Recent articles explore neuro-symbolic integration, geospatial knowledge graphs (KnowWhereGraph), and explainable AI through concept induction. He has received awards such as the SWSA Ten-Year Award (2020) and is a highly cited researcher in artificial intelligence. Grants and collaborations span digital agriculture, cybersecurity (K-CaTS), and interdisciplinary initiatives like the NSF-funded Global Food Systems Datahub. His DaSe Lab develops advanced semantic technologies, with projects including the Enslaved.org hub ontology and crisis management frameworks (HIP Ontology). Labs/Teams: Director of the DaSe Lab for Data Semantics, leading projects on knowledge graphs, neuro-symbolic AI, and geospatial data integration. Collaborates with institutions like the University of Notre Dame, Jönköping University, and IOS Press.

Georg GrohView profile

Adjunct Professor

• Applied Computer Science
• Social Signal Processing
• Social Network Analysis
• +4 more

Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.

Miloš MravikView profile

Researcher

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +3 more

Miloš Mravik is an academic affiliated with Singidunum University's Faculty of Informatics and Computing, specializing in research involving artificial intelligence, cybersecurity, and data science. He holds a doctoral degree in 'Advanced Protection Systems' from Singidunum University (2020–2023), preceded by a Master’s in 'Contemporary Information Technologies' (2018–2019) and a Bachelor’s in 'Informatics and Computing' (2014–2018). His work emphasizes applying machine learning techniques to real-world challenges like healthcare diagnostics, cybersecurity frameworks, and pandemic-era education systems. Research Interests: AI-driven cybersecurity solutions for IoT and blockchain Machine learning applications in health informatics Optimization of predictive algorithms using metaheuristics E-learning strategies during crises Recent articles focus on explainable AI for metaverse security, sentiment analysis using BERT models, and blockchain node detection via XGBoost. His work bridges theoretical computer science with practical applications in emergency education and pandemic response. No scientific awards listed. Advised no formally registered students. Active in conference organizing and software prototyping, as seen in projects like a scheduling web application and network management systems.

• Data Visualization
• Information Visualization
• Interactive Systems
• +4 more

Benjamin Bach is a Lecturer in the School of Computer Science at the University of St Andrews, specializing in data visualization and visual analytics. His research focuses on creating interactive systems that enhance how people understand and engage with complex data through visualization. Dr. Bach's research interests span data visualization, information visualization, interactive systems, human-computer interaction, visual analytics, responsive design, and collaborative visualization. His work bridges theoretical foundations with practical applications, developing novel techniques for data exploration and communication that have implications across various domains including scientific research, education, and decision-making processes. His recent publications demonstrate a strong focus on responsive visualization design, collaborative storytelling through data, and the integration of narrative techniques with visualization. Notably, his work on 'Visualization Atlases' and 'Discursive Patinas' explores how to create more engaging and meaningful visualization experiences that support both individual exploration and group discussion. Dr. Bach's research has appeared in top-tier visualization venues including IEEE Transactions on Visualization and Computer Graphics (TVCG), demonstrating both the quality and impact of his contributions to the field. His work often involves interdisciplinary collaborations, as evidenced by his participation in projects spanning from immunology to cultural heritage. Within the visualization research community, Dr. Bach is recognized for his methodological rigor and innovative approaches to visualization design challenges, particularly in the areas of responsive design for diverse devices and facilitating collaborative engagement with visualized data.

• Semantic Web
• Internet of Things
• Stream Reasoning
• +5 more

Pieter Bonte is a FWO Senior postdoctoral fellow and IMEC Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research focuses on Semantic Web technologies, stream reasoning, and Internet of Things applications. His research interests span Semantic Web, Internet of Things, Stream Reasoning, Knowledge Graphs, Context-aware Systems, RDF Processing, Linked Data, and Healthcare Informatics. His work bridges theoretical semantic technologies with practical applications, particularly in healthcare and IoT domains. Bonte's publication record shows a strong focus on streaming data processing, with numerous papers on Streaming Linked Data, context-aware query derivation, and semantic reasoning frameworks. His research demonstrates a progression from foundational semantic web technologies toward practical implementations in healthcare and IoT applications, with an increasing emphasis on privacy considerations and efficient processing techniques. His work frequently involves collaborations with Femke Ongenae, Filip De Turck, and other researchers at Ghent University and IMEC, indicating strong institutional research networks. His publications appear in respected venues including the Journal of Web Semantics, Semantic Web Journal, and various conference proceedings in the semantic technologies field.

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +9 more

Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications

Soon Lay KiView profile

Associate Professor

• Applied Natural Language Processing
• Data Management
• Aspect-Based Sentiment Analysis
• +9 more

Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.