جستجوی استاد راهنمای دانشگاهی

Marko Porjazoskiمشاهده پروفایل

استاد

• Telecommunications
• Network Security
• Quality of Service (QoS)
• +۶ مورد دیگر

Marko Porjazoski is a Professor at the Faculty of Electrical Engineering and Information Technologies, University Ss. Cyril and Methodius, Macedonia. His academic roles include Professor (2021–present), Associate Professor (2017–2021), and Assistant Professor (2012–2017). He holds a Ph.D. in Telecommunications (2012), M.Sc. (2006), and Dipl.Ing. (2000) from the same institution. His research focuses on telecommunications, wireless networks, Quality of Service (QoS), LTE/LTE-Advanced, and network security. He has authored over 20 peer-reviewed publications, including works on network forensics, OTT billing systems, and interference coordination in LTE. His teaching spans undergraduate and postgraduate courses in network forensics, telecommunications services, and smart society ICT solutions. He leads projects on network performance analysis and security, with recent work emphasizing cloud-based video streaming and deep learning for cybersecurity. Education: Ph.D. in Telecommunications, 2012 M.Sc. in Telecommunications, 2006 Dipl.Ing. in Electronics and Telecommunications, 2000 Research Interests: Wireless network optimization, LTE/LTE-Advanced QoS management for video/OTT services Network forensics and cybersecurity Radio access technology selection Key Contributions: Developed a Service Quality Testing System for mobile networks Proposed architectures for OTT billing systems Analyzed fractional frequency reuse in LTE Designed algorithms for heterogeneous network performance Labs/Teams: Telecommunications Institute at FEIT.

• Computer Networks
• Video Streaming
• Multimedia Systems
• +۴ مورد دیگر

Prof. Turhan TUNALI is a distinguished Professor in the Department of Computer Engineering at Izmir University of Economics' Faculty of Engineering. With a Ph.D. in Nonlinear Control Systems from Washington University in Saint Louis (1985), his academic career spans over three decades since beginning as an Assistant Professor at Ege University in 1986. Ph.D. - Nonlinear Control Systems, Washington University in Saint Louis (1985) M.S. - Applied Statistics, Ege University (1980) B.S. - Electrical Engineering, Middle East Technical University (1978) Professor TUNALI's research focuses primarily on computer networks, with specialized expertise in video streaming systems, optical networks, and intelligent transportation systems. His work bridges theoretical networking principles with practical implementations in multimedia delivery and transportation applications. He has made significant contributions to peer-to-peer video streaming architectures, bandwidth adaptation algorithms, and optical network resilience strategies. His research demonstrates a consistent trajectory from foundational network protocols to advanced multimedia delivery systems. Analysis of his 15 most recent publications reveals a strong concentration in networked multimedia systems, particularly peer-to-peer video streaming technologies and optical network architectures. His work shows evolution from basic streaming protocols to sophisticated hierarchical clustering frameworks, backup strategies for resilient networks, and security implementations for scalable video coding. The publications span high-impact journals including IEEE Access, Signal Processing: Image Communication, and Computer Standards and Interfaces. Professor TUNALI has served as Editor for the Turkish Journal of Electrical Engineering and Computer Sciences (2015-2016) and has contributed to TÜBİTAK projects. His research has been supported by various funding mechanisms, though specific grants are not detailed in the provided information. He has supervised an impressive 20 graduate students to completion, including 11 PhD candidates and 9 master's students, primarily at Ege University. His advisees have pursued research in areas including video streaming, optical networks, intelligent transportation systems, and healthcare information systems, reflecting his broad research influence. Professor TUNALI maintains an active research laboratory focused on networked multimedia systems, with particular emphasis on resilient video delivery architectures and intelligent transportation applications. His current work appears to be investigating advanced optical networking strategies and next-generation video streaming technologies.

Rebecca Balasundaramمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Blockchain Technology
• +۳ مورد دیگر

Dr. Rebecca Balasundaram is a Lecturer and Module Director at York St John University's York Business School in London, UK, and an adjunct professor at Vellore Institute of Science and Technology, India. With over 20 years of teaching experience and a decade of research in Machine Learning, Artificial Intelligence, Blockchain, and Cybersecurity, she holds a PhD in Computer Science from Bharathiyar University. She previously served as an Associate Professor at SRMIST, India, and leads the research group 'MetaLearn AI Innovators.' Her teaching spans Python/R for Machine Learning, Cryptography, and Software Engineering. She has secured research funding, including a 2023 QR-funded project on AI for predicting premature births using Machine Learning. She actively publishes in top journals, filed five patents, and presents globally on AI, Blockchain, and security. Recent activities include keynote speeches at the International Conference on Resilience Management (2023) and a training session for Nigerian security operatives (2023). Her research focuses on AI-driven security solutions, blockchain optimization, and educational tech innovations like 'Teachable Machine Using Mixed Reality.' She mentors students through SAAR projects and collaborates with industry on applied research.

• Decision support systems
• Computational intelligence
• Recommender systems
• +۴ مورد دیگر

Distinguished Professor Jie Lu is Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where she also serves as Director of the Australian Artificial Intelligence Institute (AAII). With over two decades of academic service at UTS, she has held progressively senior roles including Professor since 2007, Head of School of Software (2011-2014), and Director of the Centre for Artificial Intelligence (2017-2020) before assuming her current leadership positions. Professor Lu earned her PhD from Curtin University in 2000 and joined UTS the same year. Her academic journey progressed from Lecturer (2000-2001) to Senior Lecturer (2002-2004), Associate Professor (2005-2007), and ultimately Professor (2017-present). Professor Lu's research spans computational intelligence with particular expertise in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. Her work bridges theoretical innovation with practical applications across transportation, telecommunications, healthcare, and education sectors. She has pioneered approaches in data-driven decision making that deliver tangible economic benefits and risk management solutions for industry partners. Her recent publications demonstrate continued leadership in AI research, with a focus on addressing challenges in non-stationary environments, out-of-distribution detection, cross-domain recommendation, and healthcare applications. Her work increasingly integrates large language models with specialized domain knowledge for enhanced decision support. Officer of the Order of Australia (2022) 2023 NSW Premier's Prize for Excellence in Engineering or Information & Communication Technology Australian Laureate Fellow (2019) IEEE Fellow for contributions to fuzzy machine learning and decision making (2018) Fellow of International Fuzzy Systems Association (2017) Australia's Most Innovative Engineer Award (2019) Multiple IEEE Transactions Outstanding Paper awards Professor Lu has supervised over 50 PhD students to completion, with half pursuing academic careers and half working in industry. She has secured over A$10 million in research funding as lead Chief Investigator, including 10 highly competitive ARC Discovery Projects. Her industry collaborations include significant projects with Optus, Sydney Trains, Domain Holdings, and healthcare organizations. As Director of the Australian AI Institute, Professor Lu leads Australia's largest AI research hub with over 250 researchers and PhD students. Under her leadership, AAII has secured 47 ARC grants and over 110 industry projects since 2017. She also serves as Editor-in-Chief of Knowledge-Based Systems, a leading journal in the field.

Paul L Bendichمشاهده پروفایل

استاد مدعو

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +۴ مورد دیگر

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +۳ مورد دیگر

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

Ritske Jongمشاهده پروفایل

استاد

• Cognitive Psychology
• Attention Mechanisms
• Electroencephalography
• +۲ مورد دیگر

Ritske Jong is a Professor in Experimental Psychology at the Faculty of Behavioural and Social Sciences. His research utilizes electroencephalography (EEG) to explore cognitive mechanisms underlying attention, task-switching, and time perception. He investigates neural correlates of performance variability and decision-making under cognitive constraints. Research Focus: Jong's work spans attentional bias, temporal processing, and neurocognitive modeling. Key themes include EEG signatures of time perception, resit exam effects on learning investment, and implicit temporal regularities. His lab employs behavioral experiments and neuroimaging to dissect cognitive control mechanisms. Publication Trends: Recent articles (2017–2022) cluster in three domains: 1) EEG-informed temporal cognition studies, 2) attentional processing in learning environments, and 3) computational modeling of cognitive tasks. Cross-disciplinary collaborations extend to healthcare technology and educational psychology.

• Computer Vision
• Deep Learning
• Continual Learning
• +۴ مورد دیگر

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

Yehia Elkhatibمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Yehia Elkhatib is a Reader (Associate Professor) in Computing Science at the University of Glasgow, where he leads research in distributed systems that traverse infrastructural boundaries. His work focuses on developing data-driven approaches to improve performance and programmability of distributed systems, with particular emphasis on cloud interoperability, intent-driven networking, systems of systems composition, and edge computing. Dr. Elkhatib earned his PhD in Network Measurement & Grid Computing from Lancaster University in 2011, following an MSc with Distinction in Networking and Internet Systems from the same institution. His academic journey demonstrates a consistent focus on network systems and distributed computing. His research interests center on developing data-driven solutions to optimize complex systems, with particular emphasis on addressing resource utilization challenges from a user-centric perspective. His work spans decision-support systems for cost-effective cloud deployment, memory-aware ML scheduling to reduce training errors and GPU resource requirements, energy-efficient scheduling of scientific workflows, enabling seamless interoperability between services, developing systems of systems in IoT environments, designing intent-driven network architectures, and caching in information-centric networks. Analysis of his recent publications reveals a strong focus on sustainability in computing, with multiple papers on energy efficiency, carbon footprint estimation, and carbon-aware execution of scientific workflows. His research also shows significant contributions to IoT systems, cloud computing, and quality of experience in multimedia systems, demonstrating a diverse yet cohesive research portfolio addressing critical challenges in distributed computing. Dr. Elkhatib has secured substantial research funding including AI4ME (EPSRC) £3.8m, ABC (EPSRC) £775k, and DIONASYS (CHIST-ERA) €1.03m, demonstrating the significance and impact of his research. He actively supervises PhD students working on cutting-edge topics including DNS infrastructure placement, memory-aware scheduling, and carbon-aware execution of scientific workflows. His teaching responsibilities include Cloud Systems, Systems Programming, Advanced Programming, Building Big Data Systems, Distributed Systems, and Data Science Fundamentals, reflecting his expertise across multiple domains of computing science. His office is located in The Sir Alwyn Williams Building, room 322a at the University of Glasgow.

Nico Christiansonمشاهده پروفایل

پژوهشگر ارشد

• Algorithms
• Machine Learning
• Optimization
• +۴ مورد دیگر

Nico Christianson is a Stanford Energy Postdoctoral Fellow at Stanford University, hosted by Ellen Vitercik and Ram Rajagopal. In Fall 2026, he will join Johns Hopkins University as an Assistant Professor of Computer Science and a member of the Data Science and AI Institute and the Ralph O’Connor Sustainable Energy Institute. His research bridges algorithms, machine learning, and optimization to create reliable systems for decision-making under uncertainty, with applications in energy and datacenters. PhD in Computing and Mathematical Sciences, Caltech BSc in Applied Mathematics, Harvard Research focuses on online algorithms, learning-augmented systems, uncertainty quantification, risk-aware optimization, and sustainable energy applications. His work integrates theoretical rigor with practical deployment, addressing challenges in energy efficiency and grid stability. Recent publications highlight algorithmic innovation for energy systems, including robust scheduling, adaptive bitrate control, and uncertainty-quantified calibration. He actively recruits PhD students and postdocs for his future group at Johns Hopkins. NSF Graduate Research Fellowship PIMCO Data Science Fellowship Ben P.C. Chou Doctoral Prize in Information Science and Technology Demetriades-Tsafka-Kokkalis Prize in Renewable Energy

• Deep Learning
• Computer Vision
• Medical Image Understanding
• +۳ مورد دیگر

Adrian Barbu is a Professor in the Department of Statistics at Florida State University. His research focuses on deep learning, computer vision, and medical image understanding, with applications in feature selection, unsupervised learning, and scalable algorithms. He has contributed to advancements in neural networks, probabilistic models, and medical imaging technologies. His work spans theoretical developments in machine learning, including stochastic optimization, clustering methods, and feature selection techniques. Notable contributions include PCA-UNET architectures, compact support neural networks, and methodologies for automated image analysis in healthcare and materials science. Barbu's publications explore cutting-edge topics like semi-supervised few-shot learning, hierarchical classification systems, and the application of Monte Carlo methods in complex data analysis. His research often bridges theory and practice, addressing challenges in big data, real-time processing, and medical diagnostics. While no specific awards are listed, his extensive publication record highlights a prolific career in advancing machine learning and computational methods across interdisciplinary domains.

• Biomedical Image Analysis
• Medical Imaging
• Computer-Aided Diagnosis
• +۴ مورد دیگر

Marios Pattichis is a Professor of Electrical and Computer Engineering at the University of New Mexico (UNM), serving as Director of Online Programs since 1999. He leads the Image and Video Processing and Communications Lab (ivPCL) and co-founded Cosmiac. His work focuses on biomedical image analysis, video analytics for clinical decision support, and educational technology initiatives like the AOLME project targeting underrepresented middle-school students. Pattichis holds 10 patents, over 80 journal papers, and $15.9M in research funding from NSF, NIH, and AFRL. Education: Ph.D. and M.S. in Computer/Electrical Engineering from UT Austin (1998, 1993), with dual B.A./B.Sc. in Mathematics and Computer Sciences (1991). Research Interests: Biomedical CAD systems, explainable AI in imaging, dynamically reconfigurable architectures, and bilingual STEM education. His lab develops tools for stroke risk assessment via carotid ultrasound, solar coronal hole segmentation, and classroom activity analysis to measure student participation. Awards: 2022 EAMBES Fellow for contributions to biomedical image analysis. Editorships include IEEE Transactions on Image Processing and IEEE Signal Processing Letters. Grants & Teams: Directed over $15.9M in grants, advised numerous students (though none listed explicitly), and collaborates with educators on AOLME's integrated math/CS curricula. Active in codec comparisons for healthcare video streaming (VVC vs. AV1) and FPGA-based image processing acceleration. Labs/Initiatives: ivPCL (medical imaging/video systems); DRASTIC (adaptive video processing); ESTRELLA (bilingual STEM outreach). Involved in IEEE journal special issues on video analytics and multilingual education.

Christof Wolfمشاهده پروفایل

استاد

• Social Inequality
• Social Change
• Health
• +۴ مورد دیگر

Prof. Dr. Christof Wolf holds the Chair of Social Structural Analysis at the University of Mannheim's Faculty of Social Sciences. His research focuses on social inequality, health, social networks, and religion, approached through a structural lens. He collaborates with the Mannheim Centre for European Social Research (MZES) and GESIS – Leibniz Institute for the Social Sciences. His work emphasizes methodological advancements in data collection and analysis, particularly in open science frameworks. Research interests span interdisciplinary topics including the interplay between social structures and individual well-being, quantitative methodologies, and the application of computational tools to sociological inquiry. His team explores innovative approaches to social science research, leveraging advanced data analysis techniques. Publications highlight expertise in multimedia systems, adaptive streaming, and serious games for educational and therapeutic purposes. Collaborations with technical institutions reflect a commitment to bridging social science and computational research. He actively contributes to teaching, developing interactive learning scenarios and leveraging mobile technologies for classroom engagement.

Savannah Wei Shiمشاهده پروفایل

دانشیار

• Digital Marketing
• Consumer Behavior
• Data Analytics
• +۳ مورد دیگر

Savannah Wei Shi is an Associate Professor in the Marketing Department at Santa Clara University’s Leavey School of Business, holding the J.C. Penney Research Professorship. She joined the faculty in 2011 after earning her Ph.D. in Marketing from the University of Maryland, College Park. As Chair of the Undergraduate Leadership Team (ULT), she oversees academic and strategic initiatives at the Leavey School. Education : Ph.D. in Marketing, University of Maryland, College Park (2011) Research Interests : Savannah’s work focuses on consumer decision-making in digital environments, employing advanced metrics like eye-tracking, pupillary response, and click-stream data. Her studies explore how consumers interact with online platforms, product assortments, and mobile applications. She has published in top-tier journals including Management Science , Marketing Science , and Journal of Marketing , and contributed to the Wiley International Encyclopedia of Marketing . Awards : MSI H. Paul Root Award (Journal of Marketing) Donald R. Lehmann Award (Journal of Marketing Research) Outstanding Teaching, Research, and Service Award (Leavey School of Business) Teaching & Advising : Savannah teaches courses such as Internet Marketing, Retail Strategy, and MBA Capstone. She has advised numerous students on research projects and is recognized for her innovative pedagogy. No formal advisee list is provided in the materials. Labs/Teams : No specific labs or teams are mentioned in her profile, though her research often involves collaborations with industry partners in retail and digital marketing.

DING Xuhuaمشاهده پروفایل

استاد

• System/software security
• Applied cryptography
• Hardware-assisted secure systems
• +۴ مورد دیگر

DING Xuhua is a Professor of Computer Science at the School of Computing and Information Systems , Singapore Management University . He directs the Centre on Security, Mobile Applications & Cryptography (SMC) and leads research in system/software security and applied cryptography. PhD in Computer Science from the University of Southern California (2003) Prior degrees: B.Eng and M.Sc from Shanghai JiaoTong University His research focuses on trustworthy systems using hardware virtualization to counter kernel/user-space attacks, with emphasis on: Virtualization-based malware analysis Hybrid symbolic execution frameworks Secure forensics on compromised TrustZone/ARM devices Privacy-preserving cryptographic protocols Content-based video stream authentication His recent publications (2025-2021) span computer science , cybersecurity , and applied cryptography , with a trend toward hardware-assisted security and dynamic analysis tools. Notable awards include the Best student paper (ACISP 2012) and Best paper (IFIP CMS'12) . He mentors PhD students and leads two NRF research grants , with open positions for postdocs and engineers at OASIS and SMC labs.