Find Academic Supervisors

Hermann StrackView profile

Professor

• Network Security
• E-Government
• IT Security
• +5 more

Prof. Hermann Strack is a Professor of Network Management and Practical Computer Science at Harz University of Applied Sciences since 2000. He leads the Computer Science/E-Administration program and chairs the Competence Center at the university. His research focuses on cybersecurity, e-government, and digital transformation in education, with projects funded by the EU, German federal/state governments, and industry. Education: Doctorate in Engineering (2000), University of Karlsruhe State Examinations in Mathematics/Physics (1983-1985), University of Münster Diploma in Mathematics (1986), University of Münster His teaching spans Bachelor/Master programs in Computer Science, E-Administration, and Mobile Systems. Key research areas include security for distributed systems, eIDAS compliance, and secure e-government infrastructure. He collaborates on EU projects like StudIES+ and SHIELD, and leads initiatives such as the CyberSecurity Association in Saxony-Anhalt. Grants & Projects: EU CEF projects (StudIES+, TREATS) German Federal Ministry (BMBF) Education Platform project KAT-InnoLab SecInfPro-Geo (Competence Center) He is actively involved in academic governance, serving on ICT commissions and state-level university bodies. His work bridges technical security solutions with policy-driven digital transformation in public administration and education.

• Human-Computer Interaction
• Computer-Supported Cooperative Work
• Social Media Analysis
• +7 more

Xianghua Ding is a researcher in Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW) , with a focus on social media analysis , personal informatics , and health technology . Their work spans digital platforms for stress management, equitable participation in short-form video sharing, and VR-based educational tools. Key Research Areas : Health and wellbeing: Investigating stress tracking, emotion recognition, and self-management support in chronic disease contexts. Social media systems: Developing machine translation models tailored to user-generated content and analyzing distributed collaboration dynamics. Cultural heritage and digital inclusion: Exploring how digital making revitalizes traditional crafts and supports marginalized groups. Notable Collaborations : Co-authored works with researchers from institutions like ACM, CSCW, and CHI, focusing on urban accessibility, peer-support mental health systems, and cross-platform social interaction. Methodological Approach : Combines empirical user studies, algorithmic modeling (e.g., Transformer, LDA), and design interventions for real-world problems.

David BernsteinView profile

Professor

• In-Vehicle Computing Visualization
• Graphics and Multimedia
• Transportation Modeling
• +3 more

David Bernstein is a Professor of Computer Science at James Madison University's College of Integrated Science & Engineering. He holds a Ph.D. from the University of Pennsylvania, an M.P.A. from Princeton University, and a B.A. from State University of New York at Binghamton. His research focuses on in-vehicle computing visualization, transportation modeling, and network optimization. He has contributed to projects such as web-based parking systems, traffic congestion pricing, and multimedia software design. Education: Ph.D., Computer Science, University of Pennsylvania, 1990 M.P.A., Princeton University, 1983 B.A., State University of New York at Binghamton, 1981 Experience: Professor at James Madison University, 2007–Present Associate Professor at James Madison University, 1999–2007 Assistant Professor at Princeton University, 1994–1999 Assistant Professor at MIT, 1990–1994 His research interests span transportation systems, including traffic flow modeling and dynamic pricing strategies, alongside computer science applications in multimedia and educational software. His work often integrates computational methods with real-world infrastructure challenges. Publicly accessible publications reflect a focus on optimizing network dynamics and user-centric systems design.

Aidong ZhangView profile

Professor

• Bioinformatics
• Data Mining
• Multimedia Database Systems
• +3 more

Aidong Zhang is a SUNY Distinguished Professor Emerita in the Department of Computer Science and Engineering at the University of Virginia. She served as Department Chair from 2009 to 2015. Her primary affiliations include the School of Engineering and Applied Sciences. Dr. Zhang's research focuses on Bioinformatics, Data Mining, and Multimedia Database Systems, with recent work emphasizing robust AI systems, healthcare applications, and interdisciplinary machine learning techniques. Education: PhD in Computer Science from Purdue University (1994). Her academic contributions have been recognized through prestigious awards including IEEE Fellow (202?), CSE Faculty Distinguished Teacher Award (2004), and NSF CAREER Award (1998). Research interests span causal inference, algorithmic fairness, biomedical data analysis, and federated learning. Notable projects include improving group robustness in AI models, developing explainable neural architectures for healthcare, and advancing multimodal data integration. Her work consistently bridges theoretical computer science with practical biomedical and clinical applications. Key awards and honors include: IEEE Fellow UB Exceptional Scholar-Sustained Achievement Award (2003) SUNY Chancellor's Research Recognition Award (2002) NSF CAREER Award (1998) Her recent publications (2023-2025) highlight advancements in spurious bias mitigation, interpretability of large language models, and federated learning frameworks. Collaborative work with medical researchers has produced innovations in Alzheimer’s risk prediction and clinical decision support systems.

Saad Ali AminView profile

Senior Lecturer

• Health Informatics
• Multimedia and e-learning
• E-health
• +9 more

Dr. Saad Ali Amin serves as the Post-graduate Programme Manager and Principal Lecturer in Network Computing within Coventry University's Faculty of Engineering and Computing. With extensive experience in academic leadership and research, he directs postgraduate programs while maintaining an active research portfolio focused on health informatics and computing technologies. His work bridges theoretical computer science with practical healthcare applications, particularly in biomedical image processing and distributed computing systems. Education: Pg.Cert., Learning and Teaching in Higher Education, Coventry University Ph.D., Computer Science, Loughborough University M.Phil., Computer and Control Systems, Brunel University Dr. Amin's research spans multiple interconnected domains within health informatics and computing. His primary focus centers on the design of biomedical image processing algorithms and multimedia applications for parallel and distributed computing environments, with implementation on Clusters of Workstations (CoW). He has made significant contributions to context-aware multimedia information systems for e-health decision support, creating technological solutions that adapt to user and environmental contexts. His work in parallel processing, particularly with systolic arrays and VLSI implementations, demonstrates a strong foundation in hardware-aware algorithm design. The integration of neural networks with biomedical applications represents another key thread in his research portfolio, showing how advanced computational models can enhance healthcare delivery systems. Analysis of Dr. Amin's publication record reveals a consistent trajectory from foundational work in parallel computing and image processing toward increasingly healthcare-focused applications. His early work (2004-2006) established technical capabilities in medical visualization and knowledge management, while his middle period (2010) demonstrated the application of context-aware systems to e-health decision support. The most recent publications (2011-2013) show expansion into performance evaluation systems, steganography applications, and comprehensive surveys of pervasive healthcare systems. This progression illustrates a researcher who has successfully adapted core computing expertise to address evolving healthcare technology challenges, with particular emphasis on culturally sensitive implementations as evidenced by his work in UAE and Arab contexts. Dr. Amin has secured multiple competitive research grants that reflect the practical impact of his work: Breast cancer detection research funded by BUiD Research Funding (UAE) Parallel computing and medical imaging collaboration with Midlands region hospitals (UK) European Community funding for collaboration with European institutes UNESCO funding as Project Manager for Computerised Documentation Bank Prime Minister Initiative 2 (UK) for Context-Aware Multimedia Information System for e-Health Decision Support Knowledge Exchange and Enterprise Network (KEEN UK) for Reliable and Energy Cognitive Radio Multichannel MAC Protocol development As an academic leader, Dr. Amin has supervised 4 completed PhD students with 3 currently under supervision. His professional service includes membership on the IEEE Committee for Signal Processing Chapter for the UK and Ireland, Vice-Chairmanship of the British Computer Society/Middle East section, and roles on the Executive Council of Dubai Government Excellence Projects. He has organized major international conferences including the 4th International Conference on Developments in e-Systems Engineering (DeSE'11) and served on program committees for numerous biomedical engineering and signal processing conferences.

Tasneem DarwishView profile

Assistant Professor

• Intelligent Network Management
• Satellite Networks
• Software Defined Networks (SDNs)
• +6 more

Tasneem Darwish is an Assistant Professor in the Department of Computer Science at Saint Francis Xavier University. Their research focuses on intelligent and autonomous network management, including software-defined networks (SDNs), network function virtualization (NFV), satellite networks, 5G/6G technologies, vehicular ad hoc networks (VANETs), IoT, blockchain, and network security. Their work emphasizes future network architectures and protocols for emerging technologies like LEO satellites and edge computing. Research interests include optimizing network performance through graph-based frameworks, latency reduction in distributed systems, and secure communication protocols. They have contributed to interdisciplinary areas such as smart transportation systems via vehicular networks and high-altitude platform stations (HAPS) for enhanced connectivity. Publications highlight trends in satellite-based communication infrastructure, self-evolving network management systems, and challenges in integrating emerging technologies into existing frameworks. Their work bridges theoretical advancements with practical implementations in next-generation networks. No scientific awards or grants are explicitly mentioned in the provided texts. Advising responsibilities and lab affiliations remain unspecified.

• Internet of Things
• 6G Software Architecture
• Machine Learning Systems
• +5 more

Abhishek Kumar is a Researcher at the Center for Ubiquitous Computing, part of the Faculty of Information Technology and Electrical Engineering at the University of Oulu. He holds a PhD from the Department of Computer Science at the University of Helsinki (2018–2022). His research focuses on software-hardware co-integration in areas such as IoT, 6G, machine learning systems, energy efficiency, and environmental sensing, with a strong emphasis on trustworthy computing and UN Sustainable Development Goals (SDGs) like clean energy and sustainable cities. Teaching responsibilities include courses such as 521290S Distributed Systems (5 ECTS, 2025), High Performance Computing: Programming Parallel Supercomputers (3 ECTS, 2025), and Network Analysis and Graph Machine Learning (2 ECTS, 2025). Past courses include IoT and distributed systems modules from 2022 onward. He actively supervises 1 PhD student, 11 Master’s thesis students, and 4 independent studies, with prior supervision of 3 Bachelor’s, 4 Master’s, and 10 Advanced Practical Training students. Research collaborations span VTT, Ericsson Research, Alif Semiconductor, Aalto University, and University of Helsinki. He advises Soundxvision Oy, a Helsinki XR Center startup developing input modalities for XR headsets. Funding sources include the Finnish-American Research & Innovation Accelerator (FARIA), Indian-Finnish Consortia (FICORE), Nokia Foundation, and Walter Ahlström Foundation. His work bridges IoT, 6G, and AI to address environmental and societal challenges, leveraging software-defined systems and distributed computing paradigms. Recent publications explore metaverse integration, edge AI, and cybersecurity in emerging networks.

• Temporal Information Extraction
• Computational Linguistics
• Natural Language Processing (NLP)
• +3 more

Marc Verhagen is a computational linguist and programmer affiliated with Brandeis University's Computer Science Department. He holds a PhD in Computer Science (2004) and MA degrees in Computational Linguistics (University of Utrecht, 1991) and Social Geography (1985). His roles include Senior Research Scientist (2007-2008) and Postdoctoral Fellow (2004-2006). He co-founded LingoMotors Inc. (1997-2002), leading software development for natural language processing (NLP) tools. Research Focus: Temporal information extraction, TimeML standards, and computational linguistics infrastructure. Key Projects: TARSQI toolkit for temporal processing, LAPPS Grid, ISO-Space spatial annotation, and Medstract biomedical NLP. He has expertise in programming languages (Python, Perl), database systems (Oracle), and tools like Dreamweaver. His work bridges academia and industry, emphasizing NLP tool development and semantic interoperability. Beyond technical roles, he coordinated large-scale events (300–800 attendees) and served as a free-lance DJ and theater participant. Research interests span temporal semantics, event modeling, and spatial NLP, with contributions to TimeML, annotation frameworks, and semantic visualization. Over 30 publications since 1990, including his 2004 PhD thesis Times Between the Lines .

• Deep Learning
• Multimedia Data Analysis
• Artificial Intelligence
• +3 more

Daniel Schneider is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Distributed Systems within the Faculty of Mathematics and Computer Science. He is part of Prof. Bernd Freisleben's research group, where he began his PhD in 2020. Education : Daniel holds a Master of Science (M.Sc.) and is currently pursuing his PhD under Prof. Freisleben's supervision. Research Interests : His work focuses on deep neural networks applied to multimedia data analysis, spanning both wildlife monitoring and music information retrieval . In wildlife research, he develops AI-driven systems for analyzing camera trap images to identify European mammals and birds, addressing ecological data challenges. For musicology, he specializes in automating the transcription of historical organ tablature notation into modern formats, preserving cultural heritage through computational methods. Publications : His recent papers highlight trends in leveraging deep learning for interdisciplinary applications: from real-time species recognition at the edge (Bird@ Edge) to systems like DeepTab for music notation digitization. These contributions bridge computer science innovations with ecological and cultural preservation domains. Advising & Grants : As a PhD student, Daniel is advised by Prof. Freisleben. No grants are explicitly mentioned in the provided texts. Labs & Teams : He is a core member of the Distributed Systems and Intelligent Computing research group at Philipps-Universität Marburg.

• Deep Learning
• Multimedia Data Analysis
• Artificial Intelligence
• +2 more

Hicham Bellafkir is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Mathematics and Computer Science (Fb12) and the Distributed Systems group (AG Freisleben). He initiated his PhD in 2021 under Prof. Bernd Freisleben's supervision, focusing on deep neural networks and the analysis of multimedia data. His work integrates advanced machine learning techniques with applications in biodiversity monitoring, wildlife data analysis, and sensor networks. Current research interests include leveraging neural networks for image and audio recognition in ecological contexts. Contact: Office 04C02 (H|04 Institutsgebäude), reachable at hicham.bellafkir@uni-marburg.de . Research contributions span bioacoustics, camera trap image processing, and disruption-tolerant networking, with publications emphasizing interdisciplinary applications of AI in environmental science. Education: M.Sc. in Computer Science (exact degree not specified) Key Projects: Nature 4.0 sensor systems, QUICL networking protocols, bird species recognition frameworks Labs/Teams: AG Freisleben's Distributed Systems Group

• Wireless Communications
• Network Optimization
• Quality of Service (QoS)
• +3 more

Francisco M. Delicado Martínez is an academic researcher specializing in telecommunications, wireless networks, and IoT applications. His work focuses on Quality of Service (QoS) mechanisms, Software Defined Networks (SDN), and blockchain integration in fog computing environments. He has contributed to optimizing resource allocation in OFDMA and IEEE 802.16 networks, and developed IoT-based systems for environmental monitoring (e.g., glyphosate detection, agrochemical spray drifts, and Aedes aegypti surveillance). His recent research emphasizes low-cost IoT ecosystems and blockchain-driven e-government services for public administration and construction management. Research interests span across wireless communications, network optimization, and distributed computing architectures. His publications address challenges in contention resolution, bandwidth request mechanisms, and machine learning integration for disease vector monitoring. Collaborations include projects on fog computing orchestration, SDN-based network enhancements, and multimedia transmission over TDMA/TDD wireless networks. Key contributions include the S-HIDRA architecture (blockchain & SDN for fog computing), DriftGLY and SpectroGLY IoT systems, and the MosquIoT framework for mosquito population monitoring. His work bridges theoretical network protocols with practical applications in agriculture, public health, and smart infrastructure.

• Entity Resolution
• Knowledge Graphs
• Anomaly Detection
• +4 more

Vassilis Christophides is a Researcher at INRIA Paris, France, specializing in data management and knowledge systems. His research spans entity resolution, knowledge graphs, anomaly detection, and IoT analytics, with recent focus on fairness-aware algorithms and explainable AI. He maintains active collaborations with institutions across Europe and has published extensively in top-tier venues including VLDB, ICDE, and KDD. His core research investigates: Scalable entity resolution techniques for web-scale data Knowledge graph construction and alignment methodologies Real-time anomaly detection in streaming environments Fairness and bias mitigation in data integration pipelines Edge computing optimizations for IoT analytics Recent publications demonstrate a strong trend toward responsible data science, combining foundational data management with emerging concerns in AI ethics. His work frequently develops novel methods for: (1) improving transparency in automated systems through explainable anomaly detection, (2) ensuring fairness in entity resolution workflows, and (3) optimizing resource-constrained edge environments for continuous analytics. Dr. Christophides leads research initiatives at INRIA and collaborates on European projects involving streaming data processing, knowledge representation, and distributed computing infrastructures. His team focuses on bridging theoretical frameworks with practical implementations for web-scale data challenges.

• High Performance Computing
• Agent Based Modeling
• Analysis of Large Datasets
• +4 more

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence. Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume. His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling. Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic. Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.

• Digital Libraries
• Web Archiving
• Semantic Web
• +3 more

Thomas Risse is a Professor affiliated with Goethe University Frankfurt, with former roles at institutions such as L3S Research Center and Fraunhofer Institute. His work focuses on digital libraries, web archiving, semantic technologies, and information retrieval. He has led and participated in numerous international conferences and workshops, including TPDL, JCDL, and ECIR. Key contributions include methods for event-centric web archive analysis, named entity evolution recognition, and semantic ambient media experiences. His research spans topics like focused crawling, language evolution in digital archives, and QoS-aware service composition. Risse has collaborated extensively with researchers across Europe and contributed to projects like ARCOMEM, enhancing social and semantic web archiving.

Vijay AnandView profile

Associate Professor

• Cybersecurity
• Blockchain Technology
• Information Systems Management
• +3 more

Dr. Vijay Anand is an Associate Professor of Information Systems and Technology at the University of Missouri-St. Louis (UMSL), where he joined in 2019. He holds a Ph.D. from the Illinois Institute of Technology. His research focuses on cybersecurity, blockchain technology, and educational strategies in computing curricula. Research interests include SIM card security, cybersecurity program accreditation, blockchain applications in accounting, and enhancing computing education through competitive events. Recent work explores Zero Trust architectures in tactical edge networks and discrete event simulation for healthcare system optimization. He has contributed to over 15 publications, including work on cryptomining detection, knowledge graph-as-a-service for cyber defense, and transforming lab courses for distance learning. No scientific awards are explicitly listed. His advising and grants section remains unspecified, though his research aligns with cybersecurity policy development and curriculum innovation. He is affiliated with UMSL’s media contact department, contributing to academic outreach and media relations.