Find Academic Supervisors

Eirina BourtsoulatzeView profile

Researcher

• Network Coding
• Multimedia Communications
• Information-Centric Networking
• +3 more

Eirina Bourtsoulatze is a Researcher at Imperial College London's Intelligent Systems and Networks Group since January 2017. Previously, she held a Senior Researcher position at the Institute of Computer Science, University of Bern, Switzerland. She earned her PhD in Electrical Engineering from EPFL (Switzerland) in 2013 and a Diploma in Electrical and Computer Engineering from Aristotle University of Thessaloniki (Greece) in 2008. Education highlights include her PhD thesis on 'Rate allocation and optimized decoding in inter-session network coding' advised by Prof. Pascal Frossard, and her diploma thesis on 'Selection combining diversity receivers in wireless fading channels' with Prof. Georgios Karagiannidis. Her research focuses on network coding, multimedia communications, IoT, signal processing, and machine learning applications in communication systems. She has pioneered adaptive video streaming techniques using network coding in named data networks, minimizing decoding delays, and optimizing rate allocation in P2P networks. Key contributions include the NetCodCCN protocol (presented at IEEE INFOCOM 2016) and a best student paper award-winning work on distributed rate allocation in P2P networks. Her publications span IEEE Transactions on Communications, Multimedia, and prestigious conferences like INFOCOM and NetCOD. Her work integrates theoretical network coding advancements with practical applications in scalable video delivery, IoT architectures, and machine learning-driven communication optimizations. Current research explores machine learning's role in addressing communication challenges in smart networks.

Olesia NikulinaView profile

Assistant Professor

• Visual Marketing
• Digital Marketing
• Unstructured Data Analysis
• +2 more

Olesia Nikulina is an Assistant Professor of Marketing at ESCP Business School in Madrid, specializing in visual and digital marketing, unstructured data analysis, and consumer behavior. She holds a joint PhD from Maastricht University and Hasselt University, and has held roles including doctoral researcher at Maastricht University, visiting scholar at Babson College, and strategy consultant at EY. Her research focuses on how visual content transports audiences into narratives, published in journals like the Journal of Advertising , and presented at leading conferences such as ACR and AMA. She was nominated for the AMA Sheth Doctoral Consortium Fellowship. Before academia, she worked as an overseas marketing manager for a major IT company and as a strategy consultant at EY.

Matthias ZeppelzauerView profile

Professor

• Human-Centered AI
• Computer Vision
• Multimodal Learning
• +5 more

FH-Prof. Priv.-Doz. Dr. Matthias Zeppelzauer is a Professor of Computer Vision and Machine Learning at the University of Applied Sciences St. Pölten, leading the Media Computing Research Group at the Institute of Creative Media Technologies. His work focuses on human-centered AI, explainable machine learning, and clinical gait analysis. He holds a habilitation in Computer Science from TU Wien and has authored/co-authored over 90 publications. Education: PhD in Computer Science (2011, TU Wien) Habilitation (2020, TU Wien) Bachelor/Master in Media Informatics (1999-2006, TU Wien) Research Interests: Human-centered AI, computer vision, multimodal learning, explainable AI, clinical gait analysis, and social media analysis. Cross-cutting themes include interactive machine learning and trustworthy AI. Key Contributions: Developed methods for gait analysis (GaitRec dataset), explainable AI frameworks (KAVAGait), and multimodal fake news detection. Coordinated the Center for Artificial Intelligence at FH St. Pölten since 2021. Recognition: 2024 EuroVA Best Paper Award 2021 MTD Award for ReMoCapLab 2020 CVPR Outstanding Reviewer 2018 Austrian Open Source Award Grants & Projects: Coordinator of FAIRAI, TrustAI, and ReMoCapLab projects. Involved in EU-funded initiatives like Visual Heritage and Center for Digital Health. Labs/Teams: Leads Media Computing Research Group and collaborates with the Center for Artificial Intelligence and Center for Digital Health.

• Signal Processing
• Information Theory
• Multimedia Security
• +5 more

Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.

• Digital innovation live performance
• Programming in Australian performing arts centers
• Audience relevance
• +6 more

Dr. Elizabeth Gibbs is a Lecturer in Dance at Queensland University of Technology's Faculty of Creative Industries, Education & Social Justice, School of Creative Arts. She joined QUT in 2018 after over a decade as Associate Artistic Director of Australian Dance Theatre and a 14-year professional dance career with Australian Dance Theatre and Rambert London. She is an affiliate investigator in QUT's Design Lab and recently secured ARC Linkage Project funding for 2025. Her research focuses on digital innovation in live performance, particularly the intersection of AI technology and creative practice. She leads three interconnected interdisciplinary research projects investigating digital content creation and distribution in live performance. Her work bridges the gap between traditional dance practice and emerging technologies, examining how digital tools can enhance creative outcomes while maintaining artistic integrity. Gibbs' publications demonstrate a consistent trajectory from cognitive aspects of dance performance toward digital innovation. Her recent work explores augmented reality effects in live performance, virtual approaches to choreographic pedagogy, and holographic theatre. This evolution reflects the broader shift in performing arts toward digital integration while maintaining core artistic values. Her professional background as both a performer and arts administrator provides unique insight into the practical challenges of integrating technology into live performance contexts. With extensive international performance experience across 30+ countries at venues like The Paris Opera Le Garnier and Sadler's Wells, she brings real-world perspective to her academic work. As an industry leader, she has served on Creative Australia funding panels and contributed to shaping performing arts policy. Her work with the Smith Quartet as Manager demonstrates her broader engagement with interdisciplinary arts practice beyond dance.

Jonathon HareView profile

Professor

• Representation Learning
• Deep Learning
• Computer Vision
• +6 more

Jonathon Hare is a Professor in the School of Electronics & Computer Science at the University of Southampton. He holds a BEng degree in Aerospace Engineering and PhD in Computer Science, both from the University of Southampton. His research is centered around representation learning with the goal of developing techniques that allow machines to understand and utilize data to fulfill human information needs. His research spans three main areas: Novel representation techniques including differentiable neural architectures for counting and working with unordered sets; Understanding representations through biological inspiration, exploring how neural architectures relate to biological systems; and Applications of representation in geospatial intelligence and document analysis. He has made a strong commitment to open science, with many projects having open-source implementations, including the OpenIMAJ software that won the prestigious 2011 ACM Multimedia Open Source Software Competition. His recent publications (2024-2025) show a strong focus on hardware-efficient neural networks, representation learning, and explainable AI. His work addresses critical challenges in neural network memory configuration, FPGA acceleration, and stable algorithm learning using mathematical constraints. His research shows growing emphasis on practical implementation challenges and the intersection of theoretical neural network concepts with real-world hardware constraints. Vice Chancellors Teaching Award (2015) Faculty award for innovative teaching (2013-14) 2011 ACM Multimedia Open Source Software Competition winner Shortlisted for Blackboard and VLE Awards (2016, 2017, 2020) Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE (SMIEEE) As Doctoral Programme Director, Professor Hare supervises over 150 PhD students across multiple disciplines including Computer Science, Geography, Engineering, and the MINDS interdisciplinary program. He leads undergraduate Computer Vision and postgraduate Differentiable Programming/Deep Learning modules, and has been instrumental in developing new machine learning curriculum including modules on Deep Learning, Natural Language Processing, and Machine Learning Technologies. His teaching excellence has been widely recognized through multiple awards and nominations. He also serves on the editorial board of IET Image Processing journal since 2020. Professor Hare is actively involved in several research groups including Vision, Learning and Control; International Centre for Ecohydraulics Research; Centre for Machine Intelligence; and Southampton Marine and Maritime Institute. His current projects include SemanticNews, JASH – AYURDA (Fish for Life), International Centre for Spatial Computational Learning, and various marine and geospatial research initiatives.

• Deep Learning
• Pattern Recognition
• Energy Systems Modeling
• +3 more

Jenny Alexandra Cifuentes Quintero is an Assistant Professor at the Department of Quantitative Methods, School of Engineering (ICAI), Universidad Pontificia Comillas, Madrid. She holds a PhD in Automation and Mechanical and Mechatronic Engineering from a double degree program between National University of Colombia and INSA Lyon, France. Her research focuses on pattern recognition, deep learning, and machine learning applications in energy systems, biomedical engineering, and data science. Education: PhD in Automation and Mechanical/Mechatronic Engineering (double degree: National University of Colombia & INSA Lyon, France) Research: Energy Systems Modeling, Pattern Recognition, Medical Gesture Analysis, Data Science, Urban Mobility Her recent publications span deep learning , energy systems , biomedical signal processing , and interpretable AI . Key trends include surgical gesture classification , renewable energy forecasting , and neural network interpretability . She has received recognition for her work on wind power forecasting (Best Paper, IREC 2022) and contributes as a reviewer for journals like IEEE Access and IEEE Transactions on Biomedical and Health Informatics . Scientific Awards: Best paper on wind energy forecasting (IREC 2022) Mentorship: Directed Master thesis by Mora, E. (2021) Research Grants: Participated in projects for Endesa Medios y Sistemas S.L. (2022) and Enel Iberoamérica S.R.L. (2021)

Vicenç Fernández AlarcónView profile

Professor

• Decision Analysis
• Data Analytics
• People Management
• +5 more

Vicenç Fernández Alarcón is a Professor at the Department of Business Organization , Universitat Politècnica de Catalunya , affiliated with the School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT) . He leads research in HR analytics and organizational behavior while contributing to digital transformation studies. Doctorate from UPC Master's in Statistical Learning and Data Mining Master in People Analytics Engineer in Electronics His research focuses on: Data-Driven HR: Talent management systems, analytics implementation, and organizational behavior modeling Decision Analysis: Methodological frameworks for business intelligence Organizational Change: Digitalization challenges and cultural adaptation Media Richness: Communication theory in digital environments Recent publications (2020-2025) demonstrate expertise in: HR analytics adoption patterns Web personalization evaluation Organizational change dynamics Talent retention in tech sectors Leadership roles include: Principal Investigator for TechTalent Research Group Member of GRO Research Group Project reviewer for European conferences Collaborations span multiple institutions through the Unite! University Network with emphasis on sustainable industrial practices.

Joseph MoscaView profile

Associate Professor

• Human Resource Management
• Organizational Behavior
• Education Technology
• +7 more

Dr. Joseph Mosca is an Associate Professor in the Department of Management and Leadership at Monmouth University's School of Business Administration. With over 25 years of corporate administrative experience and a doctorate in management, technology, and education from NYU, he specializes in organizational behavior and technology's impact on management. Ed.D., New York University M.A./B.A., Montclair State College His research examines technology-education synergies in workplace dynamics, Generation Z workforce adaptation, and organizational change management. Recent publications focus on hybrid learning, soft skills in HR, and performance appraisal evolution. Academy of Strategic and Organizational Leadership Distinguished Paper Award Beta Gamma Sigma Teaching Award (2006) Who’s Who Among America’s Teachers (2002-2005) Sigma Pi Fraternity Advisor Awards As faculty advisor to Sigma Pi since 1989, he mentors students in leadership and community engagement. His teaching portfolio spans human resource management, strategic leadership, and technology-impacted organizational development.

Jing LiView profile

Assistant Professor

• E-commerce
• User-generated Content
• Customer Relationship Management
• +1 more

Jing Li is an Assistant Professor in the Information Systems Management department at NEOMA Business School in France. Her work bridges the fields of E-commerce , User-generated Content , Customer Relationship Management , and Generative Artificial Intelligence . Research Focus: Consumer trust in online reviews and digital content Impact of visual elements in electronic word-of-mouth Privacy policy effectiveness and consumer behavior Generative AI applications in customer relationship management Academic Contributions: Her research appears in top-tier journals like Journal of Marketing , Information Systems Research , and Journal of Business Ethics . Recent work analyzes review helpfulness metrics, privacy policy strategies, and cart targeting dynamics. Professional Engagement: Jing Li serves as an ad hoc reviewer for prestigious journals including Journal of Marketing , Information & Management , and Internet Research .

Drago ŽagarView profile

Professor

• Communication Networks and Protocols
• Codes and Coding
• Quality of Service in Networks
• +7 more

Drago Žagar is a Full Professor at the Faculty of Electrical Engineering, Computer Science and Information Technologies (FERIT), University of Osijek, where he also serves as Head of Department. He has been permanently appointed since 2015 and has a long-standing affiliation with the institution since 1990, with brief periods at other units within the same university. His educational background includes a B.Eng. (1990), M.Sc. (1995), and Ph.D. (2002), all from the Faculty of Electrical Engineering and Computing in Zagreb. His research spans key areas in modern networking and information systems, including communication networks and protocols , quality of service , cybersecurity , and IoT applications in agriculture and logistics . His recent work emphasizes energy-efficient monitoring, anomaly detection using machine learning, smart labels for supply chains, and adaptive video streaming. The 15 most recent publications reflect a strong trend in applying sensor networks and data analytics to real-world problems in agriculture, network security, and multimedia systems. The interdisciplinary nature of his work bridges computer networks, industrial informatics, and smart technologies. He has led significant projects such as Smart Label for Measuring and Monitoring Product Storage , IoT Ecosystems for Agriculture , and Boosting Telecommunications Engineering Education under Erasmus+. He also managed national and EU-funded initiatives focused on broadband access in rural areas and IPv6 security. He has mentored numerous students and authored key educational materials, including laboratory manuals in computer networks and electronics. His teaching includes courses on Computer and Mobile Networks and Computer Systems Security . He is actively engaged in research, with publications in journals such as Sensors , Electronics , Journal of Industrial Information Integration , and Telecommunications Systems . His work is indexed in Google Scholar, Scopus, Web of Science, and CRORIS.

Sos S. AgaianView profile

Professor

• Big and Small Data Analytics
• Computational Vision and Sensing
• Machine Learning
• +14 more

Sos S. Agaian is a Distinguished Professor of Computer Science at the College of Staten Island and the Graduate Center, City University of New York (CUNY). Previously, he was the Peter T. Flawn Professor of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he also served in the Graduate School of Biomedical Sciences and led the Multimedia and Mobile Signal Processing Laboratory. He has held visiting positions at Tufts University and Tampere Institute of Technology and was a Leading Scientist at AWARE, Inc., Massachusetts. Dr. Agaian holds a PhD in Mathematics and Physics from the Steklov Institute of Mathematics, Russian Academy of Sciences, a Doctor of Engineering Sciences from the Institute of Control Systems, RAS, and an MS in Mathematics and Mechanics (summa cum laude) from Yerevan State University, Armenia. His research spans Big and Small Data Analytics, Computational Vision, Machine Learning, Digital Forensics, Information Fusion, and Fast Algorithms . His work emphasizes extracting meaning from visual content and developing intelligent systems for applications in healthcare, biomedical data mining, multimedia security, and urban computing. He has contributed foundational theories such as Agaian's Theorem , Agaian's Family , Agaian's Method , and Agaian’s Matrix , which are widely recognized in signal processing and combinatorics. His publications include over 650 peer-reviewed papers, ten books, and nineteen edited proceedings. His research has been cited extensively, reflecting broad impact across engineering and computer science. Trends in his recent work focus on real-time data analytics, neurocomputing, and secure multimedia systems, particularly in defense and medical imaging. SPIE Fellow (2005) AAAS Fellow (2010) IS&T Fellow (2013) IEEE Fellow (2017) Entrepreneurship Award (UTSA, 2016, 2013) Innovator of the Year (UTSA, 2014) Tech Flash Titans - Top Researcher (2014) Distinguished Research Award (UTSA, 2006) Dr. Agaian has mentored over 30 PhD and 60 Master’s students, many of whom now work at top institutions and companies including Intel, Raytheon, MIT-LL, Siemens, and Cisco. He has secured over $7 million in research funding from NSF, DARPA, U.S. Army, DOE, and AFOSR. He co-founded BA Logix, Inc. and the Center for Simulation, Visualization, and Real Time Prediction at UTSA, which received a $5M NSF grant. He holds over 44 U.S. and international patents, several of which are licensed commercially, including to Latakoo for use in media transmission (e.g., NBC’s Sochi Olympics coverage). He leads the Computational Vision, Machine Learning, and Data Analytics (CSMAD) Laboratory , which focuses on real-time intelligent systems, multimodal biometrics, cancer imaging, and urban computing. The lab trains students in cutting-edge research and collaborates with industry and government sponsors.

A. Murat TekalpView profile

Professor

• Signal Processing
• Image and Video Processing
• Computer Vision
• +3 more

A. Murat Tekalp is a Professor at Koç University, Faculty of Engineering, where he has been a faculty member since 2001. He previously served on the faculty at the University of Rochester from 1987 to 2005. He was the Dean of the Faculty of Engineering at Koç University from 2010 to 2013. His academic career began with a research position at Eastman Kodak after completing his PhD. His research interests lie in the fields of signal processing, image and video processing, computer vision, and multimedia systems. He has made significant contributions to digital image restoration, video enhancement, and visual content analysis. His work bridges theoretical signal processing with practical multimedia applications. Dr. Tekalp has received numerous scientific honors, including: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Full Member of the Turkish Academy of Sciences (TÜBA) Member of Academia Europaea Recipient of the TÜBİTAK Science Award IEEE Turkey Lifetime Achievement Award IEEE Signal Processing Society Distinguished Lecturer He is actively involved in the academic community through editorial leadership, serving as Editor-in-Chief of EURASIP Signal Processing: Image Communication (Elsevier) and on the Editorial Board of Proceedings of the IEEE (Wiley-IEEE Press). He has advised numerous graduate students and led research groups in multimedia and signal processing, though specific names are not listed in the source text. His work has been supported by various national and international research grants, contributing to advancements in visual media technologies.

• Data Management
• Data Analytics
• Spatio-temporal Databases
• +9 more

Mohammed Eunus Ali is a Senior Lecturer in the Department of Software Systems & Cybersecurity within the Faculty of Information Technology at Monash University, Australia. He holds a PhD in Computer Science and Software Engineering from the University of Melbourne and has previously served as a Professor at the Bangladesh University of Engineering and Technology (BUET), where he led a research group in Data Science and Engineering for over a decade. He has also held research positions at Monash University, Swinburne University, the University of Melbourne, and RMIT University. PhD : Computer Science and Software Engineering, University of Melbourne (2010) M.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (2002) B.Sc. Engg. : Computer Science and Engineering, Bangladesh University of Engineering and Technology (1999) Dr. Ali’s research spans data management, analytics, and learning , with a strong focus on spatio-temporal data, geo-social networks, and multimodal high-dimensional data . His work enables applications in urban computing, intelligent transportation systems, and smart, sustainable cities . In recent years, he has expanded into Generative AI and large language models (LLMs) , exploring their role in enhancing geo-spatial query processing, SQL generation, and data engineering tasks. His publications appear in top-tier venues such as ACL, TKDE, VLDB, ICDE, SIGSPATIAL, and IEEE Access . His recent publications reflect a strong trend toward AI-driven solutions for real-world spatial and health problems , including blood glucose prediction for diabetics, seismic intensity forecasting, eco-friendly route planning, and LLM-based code generation. These works demonstrate a convergence of deep learning, spatio-temporal analytics, and real-world system design . Scientific Awards: Bangladesh University Grants Commission Award (2012) ADC Best Poster Award (2016) SSTD Best Demo Award (2017) ADC Best Paper Award (2022) Dr. Ali actively contributes to the research community as a Program Committee Member for premier conferences including SIGMOD, VLDB, ICDE, and SIGSPATIAL . He is a Senior Member of the ACM and currently supervises PhD students, focusing on cutting-edge topics in data science and AI. His collaborative research network spans institutions in Australia and Bangladesh, contributing to advancements in both academic and applied domains. His work aligns with the UN Sustainable Development Goals , particularly in the areas of sustainable cities, innovation, and quality education.

• Multimedia Data Management
• Collaborative Filtering
• Similarity Search
• +5 more

Ilaria Bartolini is a Full Professor at the University of Bologna's Department of Computer Science and Engineering, specializing in multimedia data management and preference learning. She coordinates the MultiMedia Database Group and leads the PhD Programme in Computer Science and Engineering. Her research focuses on similarity search, collaborative filtering, and applications in cultural heritage and health domains. Education: Laurea in Computer Science (1997), University of Bologna PhD in Electronic and Computer Science Engineering (2002), University of Bologna Research Interests: Her work spans multimedia systems, data curation, and AI-driven solutions for healthcare and cultural heritage. She collaborates with institutions like HKUST, NJIT, and the European Processor Initiative, leading projects such as DETECt (EU H2020) and S-PIC4CHU (PRIN 2022). Grants & Projects: EU H2020 DETECt (2018–present): Transcultural crime narrative analysis PRIN 2022 S-PIC4CHU (2024–present): Data science integrity GenData 2020, WISDOM, COOPERARE (Italian national projects) Teaching: Leads courses on web applications, multimedia data management, and digital humanities at the undergraduate and graduate levels. Her educational framework emphasizes active e-learning through tools like ALDO. Labs & Teams: Directs the MultiMedia Database Group, fostering interdisciplinary research in data science and AI applications.