Find Academic Supervisors

• Edge Computing
• Internet of Things
• Vehicular Networks
• +5 more

Muhammad Bilal is an Associate Professor at Zhejiang University's School of Computer Science, Department of Computer Science and Technology. His research spans multiple cutting-edge domains in computer science and engineering, with a particular focus on edge computing, Internet of Things, and vehicular networks. His research interests include Edge Computing , Internet of Things , Vehicular Networks , Federated Learning , Digital Twins , and Cloud Computing . His work addresses critical challenges in resource allocation, latency optimization, privacy preservation, and intelligent decision-making in distributed computing environments. He has made significant contributions to developing novel frameworks for service caching, computation offloading, and secure data processing in next-generation networks. His recent publications reveal a strong trend toward integrating artificial intelligence with edge and cloud infrastructure, particularly focusing on privacy-preserving techniques and resource-efficient algorithms. His work spans applications from intelligent transportation systems to healthcare monitoring and meteorological services, demonstrating the versatility and real-world impact of his research. Dr. Bilal has established a prolific publication record with numerous high-impact papers in top-tier journals and conferences, reflecting his active engagement in the academic community. His collaborative approach is evident through extensive co-authorship networks, particularly with researchers in China, Pakistan, and internationally.

• Multimedia Quality of Experience
• Machine Learning
• Immersive Media
• +3 more

Dr. Helard Becerra is an Assistant Professor in the School of Computer Science at University College Dublin (UCD). He holds a PhD from the University of Brasília and has held postdoctoral roles at UCD's Insight Centre for Data Analytics and Samsung R&D Institute Brazil. His research focuses on multimedia quality assessment (audio/video/speech) and AI-driven healthcare solutions for stroke rehabilitation. Key contributions include developing NAViDAd (a deep learning-based quality metric) and predictive models for stroke recovery outcomes. Education: B.Sc. (UNSAAC, Peru, 2010); M.Sc. & Ph.D. (UnB, Brazil, 2013/2019). Professional experience includes roles at Samsung (2019), DIT (2017-2018), and leadership in EU projects like Precise4Q (Horizon 2020). Research interests span: 1) Perceived quality in multimedia systems, 2) Explainable AI for healthcare, 3) Predictive modeling in stroke rehabilitation. Notable work includes gradient boosting models for social risk prediction and Elo rating systems for personalized therapy. Teaching responsibilities include coordinating modules on Software Engineering, Parallel Computing, and Programming. He actively supervises graduate students and serves on UCD's Equality, Diversity & Inclusion Committee. Awards include the 2019 Best Student Paper Award at International Symposium on Electronic Imaging. He reviews for top conferences (ACM MMSys, IEEE ICIP) and journals (IEEE Access, IEEE Signal Processing Letters).

Elias AllayiotisView profile

Academic

• Mobile Computing
• Cloud Computing
• Data Analytics
• +3 more

Elias Allayiotis is an Associate Lecturer in the Department of Computing at the School of Sciences, University of Central Lancashire Cyprus. He holds a PhD in Computer Science (2017) and has professional certifications in Information Systems Auditing and Linux Administration. His research focuses on enhancing mobile user Quality of Experience (QoE) through context-aware systems and mobile-to-cloud computing architectures. Education: PhD (UCLan, 2017), MSc & BSc (Roosevelt University, 2004–2007) Certifications: ISACA, LPI, HEA His research interests include modeling mobile QoE using context-aware approaches, optimizing mobile-to-cloud data offloading, and developing adaptive systems for user-centric experiences. He has published in venues like IEEE PIMRC and IWCMC. Awards: AWS Research Grant (2015), UCLan PhD Scholarship (2013) Professionally, he has held roles as CTO/VP of Product Development at Competitive Analytics Professionals and Managing Director at UVIA Technologies. He teaches the Masters Project module (CO4804) and is a member of ACM, IEEE, and ICSC.

• Next-Generation Wireless Networks
• Wireless Ad Hoc and Sensor Networks
• Cognitive Radio Networks
• +5 more

Prof. Dr. V. Çağrı GÜNGÖR is a Professor and Department Chair of Computer Engineering at Abdullah Gül University, Kayseri, Turkey. He holds a Ph.D. from Georgia Institute of Technology and has been actively involved in research and academia since 2001. His primary roles include leading the Computer Engineering department and contributing to editorial boards of prestigious journals. Education: B.S. and M.S. in Electrical and Electronics Engineering (Middle East Technical University, 2001/2003); Ph.D. in Electrical and Computer Engineering from Georgia Tech (2007). Research focuses on next-generation wireless networks, smart grid communications, cognitive radio networks, underwater acoustic networks, and AI-driven data mining. His work bridges theoretical advancements with practical applications in smart grids and vehicular networks, emphasizing reliability and efficiency. Notable awards include TUBITAK Young Scientist Award (2017), BAGEP (2017), and IEEE Best Paper Awards (2012, 2006). He leads multiple research projects funded by TÜBİTAK, EU Marie Curie, and industry partners like Alcatel-Lucent and Eaton Corporation. Grants and projects include developing low-power WSNs for structural health monitoring, underwater acoustic networks, and smart grid communication frameworks. He has co-authored over 100 peer-reviewed publications and edited books on industrial wireless networks. Teaching courses include Computer Networks, Wireless Sensor Networks, and Capstone Design Projects. His lab explores cutting-edge solutions in IoT, vehicular networks, and AI-driven network optimization.

Sun Tae KimView profile

Academic

• Business Analytics
• Statistical Learning Methods
• Digital Data Streams
• +3 more

Sun Tae Kim is a Teaching Assistant Professor in the Decision and Technology Analytics (DATA) department at Lehigh University. He holds a Ph.D. in Business Administration from Louisiana State University, a master’s in Statistics from Tulane University, and a bachelor’s in Statistics from the University of Illinois at Urbana-Champaign. His research focuses on organizational use of uncontrolled systems like online review platforms, applications of business analytics to digital data streams, and the development of statistical learning methodologies for quantitative research. He is also actively involved in creating innovative business analytics curricula. Prior to Lehigh, he developed and taught courses in information systems and business analytics. No grants or lab affiliations are explicitly mentioned in the provided text.

Kashinath BasuView profile

Senior Lecturer

• Network Virtualization
• Cloud Computing
• AI/ML in Networking
• +7 more

Dr. Kashinath Basu is a Senior Lecturer in Computer Science at Oxford Brookes University, affiliated with the School of Engineering, Computing and Mathematics. He holds a PhD in Computer Science and specializes in network virtualization, cloud computing, AI/ML for infrastructure management, and smart grid communication. His research explores SDN/NFV, cybersecurity frameworks, and quantum-resistant cryptography. Education: PhD in Computer Science, with academic and professional experience spanning over two decades. Research focuses on next-gen networks, cloud orchestration, and AI-driven security solutions. Current projects include softwarization of networks, smart grid communication protocols, and QoS-aware cloud services. Research interests span from foundational network architecture design to applied cybersecurity in smart cities. He has authored influential papers on topics like SD-WAN traffic prediction using ML, QoS-aware service selection in cloud environments, and control frameworks for smart grids. His work bridges theoretical network research with real-world infrastructure challenges. Professional contributions include editorial roles for journals like IEEE Transactions on Smart Grid and active participation in IEEE Working Groups on SDN/Quantum Communication. He has organized major conferences such as IEEE MobileCloud and ITNAC, reflecting his leadership in advancing network technologies. Labs/Teams: Member of the Cloud Computing and Cybersecurity Research Group, Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, and the Dependable Systems Engineering Research Centre. Collaborates with industry partners on projects related to autonomous cyber defense and blockchain for smart city infrastructure.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +4 more

Jyrki Nummenmaa is a Professor at the Faculty of Information Technology and Communication Sciences, Department of Computing Sciences at Tampere University. He holds an ORCID ID (0000-0002-7476-7840) and can be contacted via jyrki.nummenmaa@tuni.fi. His research focuses on data mining, machine learning, natural language processing, and database systems. Key research interests include computational methods for analyzing political language, RDF graph processing, ethical AI security systems, and sequential pattern mining. He has collaborated extensively with researchers such as Peltonen J., Zimina E., and Duan L. Recent publications (2022–2024) emphasize interdisciplinary applications like parliamentary discourse analysis and transparent question-answering systems. He has edited conference proceedings and contributed to over 100 peer-reviewed works since 1992. His work spans theoretical advancements (e.g., fair neighbor embeddings, nonparametric graph embeddings) and applied solutions (e.g., bus delay analysis, health-e-living simulations). Notable projects include TraQuLA for RDF querying and PiHVI for forum posting analysis.

Antti Ylä-JääskiView profile

Professor

• Mobile Cloud Computing
• Distributed Systems
• Pervasive Computing
• +5 more

Antti Ylä-Jääski is a tenured Professor at Aalto University's Department of Computer Science within the School of Science. He holds roles such as former Vice Head of Department (teaching) and leads the Distributed and Mobile Computing Research Group. His research focuses on distributed mobile computing systems, including mobile cloud computing, edge/fog computing, and energy-efficient communication. He has authored over 135 publications and secured funding from institutions like the Academy of Finland and Business Finland. Education: Doctoral degree in Engineering from Hochschule für Technik Zürich (1993), Master's from Helsinki University of Technology (1986) Research interests span mobile cloud computing, distributed systems, and IoT. Recent work includes vehicular fog computing for 5G, real-time gaze prediction in VR, and edge-assisted video streaming. Awards include multiple Best Paper Awards and teaching accolades, such as the 2009 award for best teaching in the School of Science and Technology. He supervises graduate studies and has advised notable students like Teemu Kämäräinen (2019 Dissertation Award) and Ilya Nikolaevskiy (2018 Award). His projects include 5G@II (2017-2019) and PraNA (2016-2018), focusing on network analytics and programmable systems. Labs/Teams: Leader of the Distributed and Mobile Computing Group, member of Helsinki Institute for Information Technology (HIIT), and Nokia Center for Advanced Research Deputy Director.

• Machine Learning
• Evolutionary Computation
• Multiobjective Optimization
• +10 more

Professor Xiaodong Li is a faculty member at RMIT University's School of Computing Technologies, serving as Assistant Associate Dean for Data Science & Artificial Intelligence. He holds a Ph.D. in Artificial Intelligence from the University of Otago, New Zealand. His research focuses on machine learning, evolutionary computation, swarm intelligence, and optimization techniques with applications in blockchain security, renewable energy, and logistics. He has received prestigious awards including the 2013 ACM SIGEVO Impact Award and the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and is an IEEE Fellow. His academic contributions include editorial roles at IEEE Transactions on Evolutionary Computation and leadership in IEEE Task Forces on Swarm Intelligence and Multi-modal Optimization. Current research interests span automated code generation, quantum AI-driven logistics, and anomaly detection. Supervision projects highlight interdisciplinary applications in AI ethics, solar energy monitoring, and fraud detection. Education: Ph.D. in Artificial Intelligence, University of Otago, New Zealand Key Roles: IEEE Fellow, ARC College of Experts (2023–2025) Publications: Over 280 peer-reviewed articles, including works on niching methods and evolutionary algorithms. Research trends show strong emphasis on hybrid optimization techniques, blockchain security, and AI-driven solutions for sustainability challenges. Recent articles explore dynamic environments, quantum rerouting strategies, and explainable machine learning systems. Awards: ACM SIGEVO Impact Award, IEEE Fellow, ARC College Membership Grants/Projects: Multiple industry-collaborative grants in smart logistics and energy systems. He leads the Data Science & AI team at RMIT, fostering innovation in large-scale optimization and metaheuristics. Active in international conferences like GECCO and IEEE CEC, he promotes open-source benchmark datasets for algorithm testing.

Hossein FaniView profile

Professor

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +4 more

Hossein Fani is a Professor of Computer Science at the University of Windsor, affiliated with the Faculty of Science. He leads the Graph Neural Network Lab, focusing on AI-driven solutions for team recommendation, graph-structured data analysis, and online grooming detection. His work integrates machine learning, natural language processing, and social network analysis to address challenges in collaborative systems and cybersecurity. Research Interests: Graph Neural Networks Expert Team Formation Algorithms Query Refinement and Backtranslation Techniques Online Safety and Grooming Detection Latent Aspect Detection in Text Key Projects include the Graph Neural Network Lab, where his team applies neural networks to graph-structured data, and the development of tools like RePair and LADy for query refinement and aspect detection. He has advised students such as Christine Wong, Farinam Hemmatizadeh, and Soroush Ziaeinejad on projects involving customer feedback analytics and team recommendation systems. His work has contributed to NSERC-funded grants for research infrastructure in the Faculty of Science. Labs/Teams: Graph Neural Network Lab RePair Toolkit Development OpeNTF Benchmark Library

Evan ElkinsView profile

Associate Professor

• Media Industries
• Digital Media
• Globalization
• +5 more

Evan Elkins is an Associate Professor in the Department of Communication Studies at Colorado State University. His research focuses on the cultural politics of digital platforms, media industries, streaming media, and globalization. He holds a Ph.D. from the University of Wisconsin, Madison. His work examines geoblocking practices in digital entertainment, as exemplified in his 2019 book Locked Out: Regional Restrictions in Digital Entertainment Culture . Current research explores Spotify and Netflix's global success through cosmopolitan engagements with cultural difference. Elkins teaches courses such as Digital Media Cultures, Global Media Cultures, and Media Theory at the undergraduate and graduate levels. His research spans streaming diplomacy, datafied managerialism in sports, and the history of media distribution technologies like the DVD region code system. He has published in journals like American Quarterly , Media, Culture & Society , and International Journal of Cultural Studies . His work bridges media studies, cultural geography, and political economy, with a focus on how digital platforms mediate global cultural flows. Recent projects include analyzing Netflix’s domestic and foreign policy impacts and exploring Silicon Valley’s influence on sports management through data-driven strategies. Elkins is also active in media historiography, examining the evolution of TV comedy and cultural retail practices.

Christian GrimmeView profile

Associate Professor

• Multiobjective Evolutionary Computation
• Social Media Analysis
• Disinformation Detection
• +4 more

Prof. Dr.-Ing. Christian Grimme is an Associate Professor and Extraordinary Professor in the Department of Information Systems at the University of Münster. He leads the Computational Social Science and Systems Analysis research group. His roles include acting professorships, research group leadership, and academic co-direction of the ERCIS Competence Center for Social Media Analytics. He holds a Dr.-Ing. in Computer Science and has extensive postdoctoral and habilitation experience. Education Timeline: 2015–2018: Habilitation and venia legendi in Information Systems 2006–2012: PhD in Computer Science (Dr.-Ing.) 1999–2006: Diploma in Computer Science Research Interests focus on Multiobjective Evolutionary Computation, Social Media Analysis, Disinformation Detection, and AI Ethics. His work bridges algorithmic innovation (e.g., optimization algorithms) with societal challenges (e.g., automated propaganda detection). Recent projects include analyzing Large Language Models' role in disinformation mitigation and real-time social media content analysis using human attention mechanisms. Awards include the Best Teaching Award (2024), PPSN XIV Best Paper Award (2016), and multiple travel grants from ACM and DAAD. He actively participates in conferences like GECCO and EMO, contributing to both theoretical and applied research. Advising and grants highlight his role in guiding over 20 theses, spanning Master's and Bachelor's projects in IS/WI. Notable grants include DAAD-funded collaborations and internal university funding for projects like MODERAT! (moderation tools) and ERCIS SMA Competence Center. Labs/Teams: Leads the Computational Social Science & Systems Analysis group, collaborating with global partners via ERCIS. Engages in initiatives like CLAIRE and the Integrity & Security Initiative to address AI ethics and information security challenges.

Hajo ReijersView profile

Professor

• Process Mining
• Data Science
• Robotic Process Automation
• +4 more

Prof. Hajo Reijers is a full professor at Utrecht University's Department of Information and Computing Sciences, leading the Process Science group. He also holds a part-time professorship at Eindhoven University of Technology's Department of Mathematics and Computer Science. His expertise spans Process Mining, Data Science, Robotic Process Automation (RPA), and Business Process Redesign. His research addresses challenges in healthcare, services, and collaboration with international scholars. Research Interests: Process Mining: Focusing on discovering, analyzing, and improving business processes through event log analysis. Data Science: Leveraging data-driven approaches for organizational decision-making and automation. Robotic Process Automation: Developing smart RPA systems and understanding human-robot collaboration. Future of Work: Investigating how technology impacts work practices and employee well-being. Key Contributions: Prof. Reijers' work bridges academic rigor and industry application, with notable contributions to process discovery algorithms, concept drift detection, and RPA frameworks. His recent studies explore the ethical and practical implications of AI in workflows. Awards: Best Paper Award (2021), Utrecht University Research Portal. Advising & Grants: Supervised 6 doctoral projects (details on Scopus). Active in securing grants for projects on process mining, RPA, and organizational change. Labs & Teams: Leads the Process Science group at Utrecht University, collaborating with industry partners to deploy process analytics solutions in real-world settings.

Anthony ContaView profile

Visiting Assistant Professor

• User Experience (UX) Design
• Human-Computer Interaction
• Cognitive Load Theory
• +7 more

Anthony Conta is a Visiting Assistant Professor at the School of Information at Pratt Institute, where he contributes to the academic development of future designers through teaching and mentorship. With over a decade of experience in design across entertainment, education, and media, he brings real-world expertise into the classroom. He has taught at CUNY and led design bootcamps, demonstrating a strong commitment to design education at multiple levels. His research and professional interests lie at the intersection of user experience, cognitive psychology, and digital product design. He emphasizes ethical design practices, user research methodologies, and the application of design thinking across industries. His work explores cognitive load, deceptive design patterns, survey design, and storytelling in UX, all aimed at creating more intuitive and humane digital experiences. His publications, particularly in UX Planet and UX Collective , reflect a consistent focus on foundational UX principles, practical frameworks, and case studies. These works span from 2018 to 2023 and show a trajectory toward both educational content and critical analysis of industry practices, including evaluations of platforms like Best Buy, Netflix, and Ling’s Cars. Themes include usability, discoverability, cognitive overload, and ethical responsibility in design. Notable honors include contributions to Webby award-winning products and Emmy-nominated interactive content, highlighting his impact in the professional design world. He has authored the book The Art and Science of UX Design (Pearson, 2023), which serves as a comprehensive guide for aspiring designers. Contributor to Webby award-winning products Contributor to Emmy-nominated interactive content Anthony Conta mentors emerging design talent on a 1:1 basis and has structured educational experiences around design for diverse audiences. He has worked with major clients including Amazon Music, Vimeo, Discovery-Food Network, Viacom-Nickelodeon, Kaplan, Boston Consulting Group, PricewaterhouseCoopers, and Mattel-Funemployed. While no formal advising of graduate students is listed, his extensive mentorship and teaching suggest a strong commitment to student development. There is no mention of research grants in the provided text. He is an active contributor to the design community through his writing on Medium, personal website, and professional networks. His work bridges academia and industry, making him a prominent figure in contemporary UX discourse.

• Hydrology
• Remote Sensing
• Artificial Intelligence
• +3 more

Mathew Herrnegger is a Senior Scientist at the Institute of Hydrology and Water Resources Management (HyWa) at the University of Natural Resources and Life Sciences, Vienna (BOKU). He completed his doctorate in hydrology after studying Environmental Engineering and Water Resources Management. His research focuses on hydrological modeling, integration of remote sensing data, artificial intelligence, and machine learning in water management applications, climate change impacts on water resources, and big data analytics. Primary research areas: Hydrology, Water Resources Management, Climate Change, Artificial Intelligence, Remote Sensing, Machine Learning Dr. Herrnegger leads research groups since 2013 and has published extensively on hydrology and water management, particularly in alpine and East African contexts. His work includes flood risk assessment, groundwater recharge modeling, and soil erosion studies using advanced statistical and machine learning techniques. He actively participates in international conferences and collaborates with institutions in Kenya, Uganda, and Austria. His recent article analyses span from 2023 to 2025, covering glacier melt contributions to runoff under climate change, water quality degradation mapping, and extreme flood estimation using synthetic weather data. Key subtopics include snow-hydrology coupling, transboundary river basin management, and AI applications in stream temperature prediction. Dr. Herrnegger contributes to university teaching and supervises student theses while engaging in knowledge transfer through GIS training workshops in Uganda and Kenya. He maintains active roles in international hydrological conferences and local Austrian climate change initiatives.