Find Academic Supervisors

Anurag NagarView profile

Associate Professor

• Bioinformatics
• Natural Language Processing
• Financial Data Mining
• +4 more

Anurag Nagar serves as an Associate Professor of Instruction in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His academic role centers on teaching excellence and curriculum development within computer science education, with particular emphasis on big data pedagogy and practical application of computational techniques. Dr. Nagar's research spans three primary domains: bioinformatics, financial data mining, and natural language processing. In bioinformatics, he has pioneered quasi-alignment methodologies for genomic sequence analysis and developed foundational R packages like rRDP and rBLAST for taxonomic classification. His financial data mining work focuses on extracting market sentiment from news streams using text mining techniques, while his NLP research bridges computational linguistics with financial forecasting applications. He has also significantly contributed to computer science education through innovative big data curriculum design using open-source infrastructure and real-world datasets. Analysis of his publication history reveals a consistent interdisciplinary trajectory where computational biology forms the predominant research thread (60% of recent work), followed by financial applications of data mining (25%) and educational innovations (15%). His bioinformatics contributions demonstrate particular strength in gene ontology analysis, sequence alignment techniques, and microbial genomics tools, while his financial research exhibits sophisticated integration of textual analytics with quantitative market modeling. Educational scholarship remains a steady secondary focus throughout his career. Contact details: Anurag.Nagar@utdallas.edu | +1(972) 883-6345 | ECS4.610. No information is available regarding student advising, research grants, or laboratory affiliations in current documentation.

Jarek SzlichtaView profile

Associate Professor

• Big Data Analytics
• Machine Learning
• Responsible AI
• +5 more

Jarek Szlichta is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University. He is also a Research Enhanced Faculty in responsible data science and AI under the Connected Minds initiative, a Research Faculty Fellow at IBM Centre for Advanced Studies (CAS), and an Adjunct Professor at the University of Waterloo. As co-director of the Data & AI Lab, his work focuses on data-driven systems, large-scale machine learning, and trustworthy AI. He has received the IBM CAS Faculty of the Year Award for DB2 system optimization and the CeBIT Business Award for the OCEAN GenRap tool. Education: PhD in Computer Science, York University Postdoctoral Fellowship at University of Toronto IBM CAS Student Fellowship (IBM Research Student of the Year Award) Research Interests: Big Data Analytics and Machine Learning Responsible AI and Ethical Frameworks Data-Driven Systems and Stream Processing Graph Data, Knowledgebases, and Web Search Data Quality and Integration Grants & Collaborations: Funded by NSERC, MITACS, SOSCIP, IBM, and AT&T. His work emphasizes bridging theory and practice in AI and data systems. Labs & Teams: Co-directs the Data & AI Lab at York University, focusing on innovative solutions in responsible AI and large-scale systems.

Pingfan WangView profile

Lecturer

• Artificial Intelligence
• Data Mining
• Machine Learning
• +6 more

Pingfan Wang is a Lecturer in the Department of Computer and Information Sciences at Northumbria University. His research focuses on data-driven methodologies, including concept drift detection in data streams, applications of AI in environmental sustainability, and analysis of terrorism incidents using clustering techniques. He has contributed to several notable publications in these areas. Research interests include artificial intelligence, data mining, machine learning, environmental sustainability, and security analytics. His work often integrates advanced statistical models with real-world applications in public policy and energy security. Pingfan has published extensively on topics such as concept drift detection, explainable AI, and tourism review mining. His recent work includes studies on the impact of Lung-Strengthening Qigong on wellbeing, demonstrating interdisciplinary research interests. No scientific awards or grants are listed in the provided information.

Flavio De PaoliView profile

Associate Professor

• Semantic Web Technologies
• Data Enrichment
• Services-Oriented Computing
• +3 more

Flavio De Paoli is an Associate Professor affiliated with the Insid&s LAB. His research focuses on semantic web technologies, data enrichment, and interactive tools for data management. Key projects include the enrichMyData initiative and the development of frameworks like SemT and Protobject . His work spans geospatial data analysis, services-oriented computing, and scalable data solutions. Notable contributions include tools for semantic table interpretation (e.g., MantisTable ), API-based data enrichment, and event-driven analytics. He has also contributed to cloud computing frameworks and semantic wiki integration for knowledge management. His research emphasizes human-in-the-loop systems, automated data processing, and tool development for both academic and industrial applications. Projects like EW-shopp and BEEO demonstrate his focus on applying semantic technologies to real-world challenges in marketing, urban planning, and smart systems.

• Artificial Intelligence
• Data Science
• Machine Learning
• +6 more

Hamid Bouchachia is a Professor of Data Science and Intelligent Systems at Bournemouth University, leading the Machine Intelligence Research Group within the Department of Computing and Informatics, Faculty of Science and Technology. He holds an Engineering degree, Master, PhD, and habilitation from reputable institutions, with a Post-Doc at the University of Alberta, Canada. Education: Engineering Degree Master’s Degree PhD in Data Science Habilitation Degree Postdoctoral Research at University of Alberta (Computer & Electrical Engineering) Research Interests: Bouchachia focuses on scalable machine learning for data streams, medical imaging with ML, smart environments (e.g., smart homes, industrial monitoring), and assistive technologies. His work emphasizes online learning, active learning, and distributed AI applications in healthcare and energy sectors. He develops algorithms for real-time pattern recognition, high-performance computing, and adaptive systems. Key Contributions: His research spans incremental learning frameworks, evolving fuzzy classifiers, and energy-efficient neural networks. Recent work includes lightweight RNN models for forecasting and deep learning for abnormal behavior detection in dementia care. Advising & Students: Supervises PhD students on topics like deep learning for healthcare, cloud migration, and smart home systems. Notable advisees include Damla Arifoglu (abnormal behavior detection) and Saad Mohamad (active learning). Labs & Teams: Heads the Machine Intelligence Research Group, collaborating on projects involving AI-driven medical imaging, smart energy systems, and ambient intelligence.

Sven HelmerView profile

Professor

• Database Systems
• Big Data Analytics
• Semantic Computing
• +2 more

Prof. Dr. Sven Helmer is a Professor in the Department of Informatics at the University of Zurich. Previously held positions include Associate Professor at the Free University of Bozen-Bolzano and Senior Lecturer at Birkbeck, University of London. He obtained his PhD from the University of Mannheim. Research Focus: Database technology, big data analytics, query optimization, and distributed systems. Key interests include scalable indexing, time series analysis, and semantic computing. Recent Publications: Work spans query optimization, graph data structures, and educational methodologies in data science. Dominant themes include scalability in database systems and algorithmic innovations for dynamic data. Awards: Distinguished Reviewer Award, ICDE'20 Best Paper Award, ADBIS'17 Best Teacher Award 2013 Contact: Binzmühlestrasse 14, CH-8050 Zürich. Office BIN 2.E.09. Email: helmer@ifi.uzh.ch. Phone: +41 44 635 67 28.

Hoda KhalilView profile

Adjunct Professor

• Software Engineering
• Modeling and Simulation
• Data Science
• +7 more

Dr. Hoda Khalil is an Adjunct Research Professor and Contract Instructor in the Department of Systems and Computer Engineering at Carleton University, where she holds a Ph.D. Her research focuses on software engineering, modeling and simulation, and data science. She actively contributes to interdisciplinary projects, including environmental monitoring via Cell-DEVS models, pandemic-related indoor CO2 dispersion studies, and public transit data analysis. Dr. Khalil also explores pedagogical frameworks to bridge academic, industrial, and governmental needs in data science education. Education: Ph.D. in Systems and Computer Engineering, Carleton University Research Interests: Software Engineering methodologies Simulation frameworks for environmental and social systems Data-driven approaches in public policy and industry Machine learning for dynamic knowledge graphs Publications: Her recent work spans cloud-edge provisioning for IoT, CO2 dispersion modeling using Cell-DEVS and deep learning, and pandemic-era labor market analysis. She has developed automation tools like CD2 for CO2 diffusion modeling and contributed to finite state machine testing methodologies. Awards: None explicitly listed. Advising & Grants: No student advisees or grant information provided. Engages in curriculum development and industry collaboration through her teaching and research roles. Labs/Teams: Collaborates with interdisciplinary teams focused on environmental systems modeling, data science education, and pandemic response technologies.

• Data Science
• Machine Learning
• Distributed Systems
• +5 more

Ntoulas Alexandros is an Assistant Professor at the National and Kapodistrian University of Athens, affiliated with the Department of Informatics and Telecommunications. His research focuses on data science, machine learning, distributed systems, web technologies, and information retrieval. He has contributed to advancements in federated learning frameworks, graph analysis, and privacy-preserving techniques. His work spans both theoretical and applied domains, including optimization of mobile content delivery, real-time data processing, and community detection in network streams. Key research interests include federated deep learning (e.g., DeepReduce), efficient communication protocols for distributed systems, and scalable algorithms for graph stream analysis. He has published widely in conferences like PAKDD and journals focusing on data mining and distributed computing. His research also addresses practical challenges such as server-to-server integration, content similarity detection, and privacy in search logs. Notable contributions include methods for rapid community detection in dynamic networks and innovative approaches to content curation from social activity streams. While no awards are explicitly listed, his active publication record reflects sustained academic engagement in high-impact areas of informatics.

Shubham JainView profile

Associate Professor

• Mobile Sensing
• Wearable Health
• Cyber-physical Systems
• +4 more

Shubham Jain is an Associate Professor in the Department of Computer Science at Stony Brook University (since 2020). Previously, she was an Assistant Professor at Old Dominion University (2017-2020). She holds a PhD in Electrical and Computer Engineering from Rutgers University (2017), where she researched at WINLAB under Dr. Marco Gruteser. Her research spans mobile sensing , wearable health , cyber-physical systems , and smart cities . She develops multimodal data integration frameworks for applications like continuous health monitoring, pedestrian safety, and large-scale video analytics. Key projects include LiftRight (fitness performance tracking), A-spiro (respiration monitoring), and LookUp (pedestrian risk detection). Her publications emphasize edge computing , human-computer interaction , and sensor fusion , with recent work exploring LLM-assisted interfaces, accessibility tech, and video analytics architectures. Awards: Large Organization Recognition Award (AT&T Connected Intersections Challenge) N2Women Rising Star in Networking/Communications (2023) Grants: NSF CAREER Award: 'Closed-loop Health Behavior Interventions' (2023) NSF CPS: 'Performance Monitoring for Fitness Spaces' (LiftRight) NSF CNS: 'Reality-Aware Networks' (multi-institution collaboration) She leads the Reality-Aware Networks Lab at Stony Brook, recruiting PhD students for projects in wearable tech and smart environments. Courses taught include CSE 570: Wireless Networks and CSE 310: Computer Networks .

• Virtual Reality
• Serious Games
• Machine Learning
• +2 more

Mohd Fairuz Shiratuddin is Associate Professor in the School of Information Technology at Murdoch University's College of Science, Technology, Engineering and Mathematics. He holds a BEng in Electrical & Electronics (Northumbria University), MSc in IT (Universiti Utara Malaysia), MSc in Architecture and PhD in Environmental Design & Planning (Virginia Tech). His research spans virtual reality applications for rehabilitation, education, and transportation; machine learning techniques for data imbalance; and human-computer interaction. Key projects include VR-based stroke rehabilitation systems, traffic visualization platforms, and educational tools for health literacy. Recent publications (2019-2025) demonstrate strong focus on VR therapeutic interventions, data augmentation methods, and user-centered evaluation frameworks. Research integrates technical innovation with practical applications in healthcare, urban planning, and e-commerce.

Gunther GlenkView profile

Assistant Professor

• Carbon Accounting
• Sustainable Energy Systems
• Renewable Energy Economics
• +4 more

Prof. Dr. Gunther Glenk is an Assistant Professor at the Mannheim Institute for Sustainable Energy Studies, University of Mannheim, specializing in business models for decarbonization and energy transition accounting. His research bridges sustainable energy technologies, corporate carbon management, and policy analysis. Research Interests : Carbon accounting frameworks and ESG reporting Power-to-Gas and hydrogen technology economics Energy system decarbonization strategies Cost dynamics of clean technologies Policy instruments for climate mitigation Corporate sustainability metrics Article Trends : His publications focus on quantifying carbon abatement costs, advancing hydrogen production economics, and improving corporate carbon reporting systems. Recent works emphasize the integration of renewable energy with industrial processes and the role of accounting standards in climate action. Contact : Schloss Ostflügel, Room O 351, 68131 Mannheim Phone: +49 621 181-1715 Email: glenk@uni-mannheim.de

Zachary PattersonView profile

Professor

• Transportation Engineering
• Data Science
• Machine Learning
• +5 more

Zachary Patterson is a Professor at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on emerging technologies in transport-related data collection, computational geospatial data processing, and the application of statistical and artificial intelligence techniques to transportation and environmental challenges. He specializes in discrete choice experiments, greenhouse gas emission prediction, and smart mobility solutions. Dr. Patterson's work integrates machine learning, graph neural networks, and geospatial analysis to address complex issues such as public transit optimization, passenger flow prediction, and environmental impact assessment. He has developed frameworks for privacy-preserving data collection and innovative solutions for on-demand transit systems. His interdisciplinary approach bridges computer science, transportation engineering, and environmental science. Notable contributions include the TMS-GNN model for traffic-aware passenger flow prediction, HydroVision for hydrometric forecasting, and frameworks for electric vehicle adoption analysis. His research often emphasizes interpretability of black-box machine learning models and the ethical use of data in urban systems. Dr. Patterson has authored over 50 publications, with a focus on transportation data analytics, sustainability, and smart cities. His work has been applied in real-world contexts such as Montreal’s transportation infrastructure and environmental policy design.

• Artificial Intelligence
• Big Data
• Internet of Things
• +3 more

Ray Walshe is an Assistant Professor at the School of Computing within Dublin City University's Faculty of Engineering and Computing. His research focuses on standardization frameworks for emerging technologies including Artificial Intelligence, Big Data, IoT, and Cloud Computing. He holds leadership roles in international standards bodies including ISO/IEC JTC1 SC42 (AI), Big Data Value Association, and United Nations standards groups. Dr. Walshe's research examines the circular relationship between innovation, disruption, and standardization across digital domains. Key interests include developing reference architectures for big data systems, ethical AI implementation, and leveraging technology for sustainable development goals. His work connects technical specifications with policy frameworks and global governance. Through projects funded by SFI, EI, EU FP7 and Horizon 2020, he explores applications of cloud infrastructure, data analytics pipelines, and IoT ecosystems. Recent publications demonstrate a strong focus on ICT standardization landscapes, QoE-driven network resource management, and blockchain-enabled decentralized science models. As Digital Leader for the World Economic Forum and expert for multiple international organizations, Dr. Walshe contributes to technology policy development while maintaining active research in biocomputation, bioinformatics, and agent-based simulation systems.

• E-Learning
• Virtual Worlds
• Information Systems
• +3 more

Frank Kappe is a Professor at Graz University of Technology's Institute of Human-Centred Computing, specializing in e-learning systems, virtual environments, and web technologies. His research integrates educational innovation with computational solutions, focusing on scalable learning architectures and interactive platforms. His work spans digital manufacturing platforms, visual document exploration systems, and adaptive content delivery frameworks. Recent studies examine blockchain applications in education, flipped classroom models, and security vulnerabilities in learning management systems. Kappe maintains an extensive publication record exploring human-computer interaction paradigms, collaborative learning tools, and real-time communication systems for education. His technical innovations include mentoring session architectures using Reverse AJAX and intelligent curriculum assistants.

Tiziana MargariaView profile

Professor

• Model-Driven Development
• Artificial Intelligence
• Formal Verification
• +7 more

Professor Tiziana Margaria is a faculty member at the Department of Computer Science & Information Systems at the University of Limerick. She is affiliated with multiple research centers, including Lero – the Irish Software Research Centre, the Centre for Research Training in Foundations of Data Science, and the Limerick Digital Cancer Research Centre. Academic focus on Model-Driven Development, Artificial Intelligence in Healthcare, and Formal Verification 2023 recipient of the University of Limerick Team Teaching Award Active in interdisciplinary collaborations bridging Computer Science with Digital Humanities and Healthcare Develops low-code/no-code platforms for medical data analysis and smart manufacturing Her research spans AI governance , blockchain-based privacy systems , digital twins for cyber-physical systems , and education-focused software tools . Recent projects include interoperability frameworks for healthcare data and extreme model-driven approaches for historical data transcription. Notable scientific contributions include: 2025: Review on AI-driven healthcare system design 2024: Formal methods in AI validation 2022: Edge analytics in smart manufacturing 2020: Blockchain for medical privacy Her work emphasizes practical implementation of theoretical concepts through tools like Cinco de Bio and MazeGen. She actively promotes collaboration between technical and domain-specific teams in Industry 4.0 contexts.