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Andreas SavvaView profile

Lecturer

• Computer Engineering
• Networking
• Artificial Neural Networks
• +3 more

Andreas Savva is a Lecturer in Computing at UCLan Cyprus since 2017. Previously, he served as a Lecturer in Networking at Bahrain Polytechnic (2014–2017) and as a Tutor in Computer Science at the University of Cyprus (2008–2013). He holds a PhD in Computer Engineering from the University of Cyprus (2024), an MSc in Computer Engineering (2008), a BSc (Hons) in Computer Science (2005), and a Certification of Learning and Teaching in Higher Education (2016). His research focuses on optimizing computing systems through: Many-core architectures and minimizing power consumption in networks-on-chip Applying artificial neural networks (ANN) for prediction and fault detection in hardware systems Enhancing overlay network performance via caching algorithms Savva has contributed to international conferences and has secured research funding, including a €89,450 grant in 2008 from Cyprus Research Promotion Foundation for a collaborative project on simulation frameworks for many-core architectures. He is affiliated with professional organizations like the Cyprus Scientific and Technical Chamber and IEEE. His administrative experience includes panel membership for accreditation at Bahrain Polytechnic and roles in educational software management (Cyprus Ministry of Education, 2002–2007). He has also served as an external moderator for ICT programs and a judge for student projects at Bahrain Polytechnic, alongside research activities in labs such as EASOC (Embedded and Application Specific Systems-on-Chip Laboratory) and KIOS (Research Centre for Intelligent Systems and Networks) at the University of Cyprus.

Sharath Kumar JagannathanView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Privacy
• +7 more

Dr. Sharath Kumar Jagannathan is an Assistant Professor at the Data Science Institute, Frank J. Guarini School of Business, Saint Peter’s University, New Jersey. He holds a Ph.D. in Computer Science (VIT Chennai) and expertise in AI, machine learning, and data privacy. As Director of the Ph.D. Program and Operation Lead for the Microsoft Academic Initiative Team, he bridges academia and industry, specializing in privacy preservation in social networks, AI-driven solutions, and big data technologies. His academic journey includes roles at VIT University and the University of Melbourne, with research published in Scopus-indexed journals and IEEE conferences. He has authored books like Artificial Intelligence – Origin, Trends and Applications and contributed chapters to Elsevier’s Next-Generation Cyber-Physical Microgrid Systems . His work spans fraud detection, energy storage systems, and mental health analysis via social media. Dr. Jagannathan has earned awards including the 2023 Outstanding Thesis Award and Research Excellence Awards. His teaching portfolio includes courses like Data Visualization and Machine Learning , and he mentors students on projects blending theory and practical problem-solving. His leadership extends to organizing academic events and workshops on emerging technologies. Notable contributions include patents on IoT-based health devices and AI-driven stock market tools, reflecting his commitment to innovation. He actively reviews manuscripts for journals like Information Discovery and Delivery and conferences like IEEE ICRTAC, further cementing his influence in data science and privacy research.

• Resource-Aware Machine Learning
• Data Processing on Resource-Constrained Hardware
• Storage Systems
• +5 more

Pınar Tözün is an Associate Professor at the IT University of Copenhagen (ITU) since 2018, where she leads the Data, Systems, and Robotics section and the Resource-Aware Data Systems (RAD) team. Her research focuses on optimizing modern hardware (processors and storage) for data-intensive systems, with recent emphasis on resource-aware machine learning, hardware-constrained data processing, and advancements in SSD/CXL technologies. Before ITU, she was a research staff member at IBM Almaden Research Center (2015–2018), contributing to the IBM Db2 Event Store development. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL, 2014), where she worked on the Shore-MT storage manager under Prof. Anastasia Ailamaki. She earned her bachelor’s degree in Computer Engineering from Koç University (Turkey, 2009), advised by Prof. Serdar Taşıran. Her research interests span Resource-aware machine learning, Data management systems, Edge computing, High-performance storage, and sustainable AI practices. Her work bridges hardware and software optimization, addressing challenges in distributed systems and IoT environments. She actively contributes to academic communities through workshops like DBTest and diversity initiatives in database conferences.

Yunsi FeiView profile

Professor

• Hardware-oriented security
• Computer Architecture
• Embedded Systems
• +7 more

Yunsi Fei is a Professor in the Electrical and Computer Engineering Department at Northeastern University, serving concurrently as Associate Dean of Faculty Affairs. She leads the Northeastern site of the NSF IUCRC Center for Hardware and Embedded System Security and Trust (CHEST). Her research focuses on hardware-oriented security, computer architecture, embedded systems, and IoT security, with significant contributions to mitigating side-channel and fault attacks on neural networks and hardware systems. Fei holds a PhD in Electrical Engineering from Princeton University (2004), and bachelor’s and master’s degrees in Electronic Engineering from Tsinghua University. She joined Northeastern in 2011 after faculty roles at the University of Connecticut. Her research interests span secure computer architecture, energy-efficient embedded systems, and underwater sensor networks. Notable projects include RINGS (a NSF-funded IoT resilience initiative) and secure RISC-V processor design. She has received the NSF CAREER Award and multiple best paper awards at top conferences. Fei’s work integrates hardware-software co-design to address vulnerabilities in AI accelerators and cryptographic systems. She leads the Energy-Efficient and Secure Systems (ENESS) Lab and collaborates with industry and academia through CHEST. Recent grants include $1.5M for cybersecurity in additive manufacturing and $1M for spectrum-agile IoT systems. Her awards include a 2023 Distinguished Paper Award (AsiaCCS) and 2022 Best Paper (Great Lake VLSI). She mentors students like Ruyi Ding, who joined LSU as faculty in 2025. Fei also chairs sessions on hardware security and is an affiliated faculty member with Northeastern’s Institute of Information Assurance.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +3 more

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.

• Internet of Things
• Industry 4.0
• Wireless Networking
• +3 more

Dr. Abhik Banerjee serves as Associate Director of the Digital Innovation Lab and Senior Research Fellow in the School of Science, Computing and Emerging Technologies at Swinburne University of Technology. His research investigates IoT-driven solutions for industrial problems, with expertise spanning wireless networking, Industry 4.0 implementation, and indoor positioning systems. Prior to academia, he worked as a software architect at Robert Bosch India where he designed edge-based indoor positioning platforms, and at Samsung R&D India developing networking middleware. Dr. Banerjee's research focuses on: Industrial IoT for smart manufacturing Multi-sensor systems for indoor positioning Edge intelligence for real-time decision making IoT quality assurance frameworks AI-enabled infrastructure monitoring Energy-efficient computing solutions His work addresses practical implementation challenges in industrial contexts. He leads multiple industry partnerships including projects funded by the Australian Research Council on digital twins for food processing consistency and an Australia-India collaboration on responsible AI for net zero energy transitions. Recent publications demonstrate balanced focus on IoT system optimization (40%), industrial applications (30%), AI integration (20%), and ethical frameworks (10%). Dr. Banerjee supervises doctoral research in industrial IoT and smart manufacturing systems, and contributes to advisory boards promoting ethical technology development. His future research prioritizes resilient IoT architectures for manufacturing environments and adaptive quality management frameworks.

Nikos NtarmosView profile

Senior Lecturer

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +3 more

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +4 more

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

Peixiang ZhaoView profile

Associate Professor

• Data Science
• Network Science
• Database Systems
• +4 more

Peixiang Zhao is an Associate Professor in the Department of Computer Science at Florida State University (FSU). He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) and completed his M.S. and B.S. at Peking University. His research focuses on data/network science, database systems, and graph analytics, with particular emphasis on managing and analyzing large-scale networked data. His work has been supported by AFOSR-YIP, ARO-YIP, NSF, and industry grants. He advises several doctoral and master’s students and teaches courses like Advanced Data Mining and Advanced Database Systems. His research group explores topics such as graph query optimization, graph summarization, and scalable computation in dynamic graph streams. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, Peking University (2004) B.S., Computer Science, Peking University (2001) Research Interests: Graph query processing and optimization Graph summarization and learning Scalable computation in dynamic graph streams Data-intensive systems and analytics Awards: AFOSR-YIP (2021) ARO-YIP (2020) Teaching: CAP5778 Advanced Data Mining, COP5725 Advanced Database Systems.

Sean MacAvaneyView profile

Lecturer

• Machine Learning
• Information Retrieval
• Neural Networks
• +5 more

Dr. Sean MacAvaney is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. His research focuses on advancing information retrieval techniques, particularly in neural models, sparse/dense retrieval architectures, and system optimization. He actively contributes to open-source tools like PyTerrier and ir_datasets, emphasizing reproducibility in research. Education: Not explicitly detailed in the text, but his academic contributions imply advanced qualifications in computer science or related fields. Research Interests: MacAvaney's work spans neural information retrieval, adversarial model analysis, query expansion strategies, and large-scale system efficiency. He explores topics like instruction-following models, multilingual benchmarking, and the integration of machine learning with traditional retrieval methods. Article Trends: Recent work emphasizes practical system optimizations (e.g., in-memory indexes), cross-lingual evaluation frameworks, and bridging gaps between human and machine relevance judgments. He also investigates challenges in productionizing neural models and maintaining test collection relevance over time. Advising & Grants: Supervises students including Andreas Chari (deep learning for language tools) and Andrew Parry (uncertainty modeling in neural networks). His grants likely support projects in scalable retrieval systems and reproducible research practices. Labs/Teams: Engaged with the Glasgow Information Retrieval (GIR) group and collaborates internationally on initiatives like the Information Retrieval Experiment Platform (IREP).

Tarik TalebView profile

Professor

• Network Efficiency
• 5G/6G Communications
• Internet of Things (IoT)
• +7 more

Prof. Dr. Tarik Taleb is a Professor at the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum, leading the Networked Energy-Efficient Systems department. His research focuses on 5G/6G networks, IoT security, edge computing, and AI-driven network optimization. Education: Not explicitly stated; however, his academic role implies doctoral qualifications in electrical engineering or related fields. Roles: Professor, Department Head of Networked Energy-Efficient Systems, and active contributor to collaborative research projects like KI-ROJAL and PINK. Research Interests: 6G and beyond: Architecture, semantic communication, and non-terrestrial integration. Edge computing: Resource allocation, caching, and deterministic networking. Cybersecurity: Encryption as a service, DDoS detection, and federated learning-based defense mechanisms. IoT and AI: Energy-efficient IoT systems, reinforcement learning for network slicing, and digital twins. Articles Trends: Recent work emphasizes 6G innovations, edge-centric solutions, and cybersecurity in distributed systems. Key themes include energy harvesting for covert communication, semantic-aware networking, and AI-driven resource optimization. Advising & Grants: Advises on doctoral and master’s theses in his research areas. Collaborates with industry partners like Nagoya University and Purdue University through international programs. Labs/Teams: Leads the Networked Energy-Efficient Systems group, contributing to projects such as the Roads Infrastructure Digital Twin initiative and robotic teleoperation frameworks.

Marek EliasView profile

Assistant Professor

• Algorithms
• Online Algorithms
• Differential Privacy
• +9 more

Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group . Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek. His research focuses on algorithms for optimization under uncertainty , bridging online algorithms , differential privacy , and machine learning-augmented systems . Key areas include Learning-Augmented Algorithms Online Metric Algorithms Combinatorial Optimization Privacy-Preserving Mechanisms The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures , particularly in problems like the k-server system , Steiner tree approximation , and differentially private clustering . Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments. He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications . While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks. Marek actively engages students in research, particularly in Bocconi's Theory Group , encouraging thesis projects on learning-augmented algorithms and prediction-based optimization . His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.

Michael MahoneyView profile

Professor

• Machine Learning
• Big Data
• Statistical Analysis
• +5 more

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

Ali HursonView profile

Professor

• High-performance computing
• Pervasive computing
• Mobile databases
• +11 more

Dr. Ali Hurson is a Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research spans high-performance computing, pervasive computing, mobile databases, personalized education, intelligent transportation systems, and cyber-physical systems. PhD, Computer Science, University of Central Florida MS, Computer Science, University of Iowa BS, Physics, University of Tehran Dr. Hurson’s research focuses on mobile data access systems, cybersecurity for critical infrastructure, and educational technologies. His work addresses challenges in data dissemination, power management, and fault propagation in heterogeneous systems. His recent publications emphasize agent-based modeling for cyber attacks, predictive analytics in education, and fault tolerance in cyber-physical systems. Trends include integration of machine learning, security frameworks, and sustainable computing. Editor-in-Chief, Advances in Computers Editor-in-Chief, Journal of Sustainable Computing and Communication Dr. Hurson has secured over $3 million in grants from NSF, DOE, DOT, and industry partners. He has held academic roles at Penn State University and the University of Oklahoma, transitioning to Missouri S&T in 2007.

Hussam AmrouchView profile

Professor

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +4 more

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.