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Yixin SunView profile

Assistant Professor

• Network Security
• Privacy
• Routing Security
• +8 more

Yixin Sun is an Anita Jones Career Enhancement Assistant Professor in the Department of Computer Science and an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia's School of Engineering and Applied Science. Their research focuses on network security and privacy, including routing security, anonymity systems, and network attacks and defenses. Sun holds a B.A. from the University of Virginia (2013) and a Ph.D. from Princeton University (2019). Research interests span critical areas such as TLS and DNS security, IoT device vulnerabilities, connected vehicle systems, and privacy-preserving protocols like those in the Tor network. Their work addresses real-world challenges, such as securing internet infrastructure against routing attacks and enhancing anonymity in dynamic network environments. Recent publications explore topics like RPKI-based Tor relay selection, Mutual TLS certificate configurations, and Bluetooth traffic interception from health devices. Despite no explicitly listed scientific awards, Sun’s contributions to network security have advanced methodologies for detecting malicious DNS activities, securing vehicular networks, and improving threat prioritization systems. Their dual appointments reflect expertise bridging computer science and electrical engineering, emphasizing interdisciplinary approaches to cybersecurity.

Ziawasch AbedjanView profile

Professor

• Data Science
• Machine Learning
• Database Systems
• +5 more

Ziawasch Abedjan is a full professor of computer science and chair of the Data Integration and Data Preparation (D2IP Lab) Group at Technische Universität Berlin, part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a PhD from the Hasso Plattner Institute (2014) and was previously a junior professor at TU Berlin (2016–2020), with postdoctoral work at MIT (2014–2016). His research focuses on scalable methods for processing large heterogeneous datasets, emphasizing automated data preparation, extraction, and cleaning for data science workflows. Education: PhD in Computer Science, Hasso Plattner Institute (2014) MSc in Computer Science, Hasso Plattner Institute (2010) BSc in Computer Science, Hasso Plattner Institute (2008) Research Interests: Dr. Abedjan’s work bridges database systems and machine learning, addressing challenges in data integration, automated data cleaning, and scalable data science tools. His team develops systems like Blend for unified data discovery and MATE for multi-attribute table extraction. Recent projects include exploring the environmental impact of AutoML and advancing catalog enrichment techniques. Awards & Recognition: First Prize, GI Data Science Challenge (BTW 2023) SIGMOD Reproducibility Award (2019) Best Dissertation Award (2013/2014) Teaching & Service: Teaches foundational courses in data structures and databases. Serves on committees for major conferences (SIGMOD, VLDB) and chairs roles such as Reproducibility Chair for BTW (2023/2025). Active in editorial roles for journals including ACM JDIQ and IEEE Data Engineering Bulletin. Labs & Teams: Leads the D2IP Lab, collaborating with universities and industry. Current projects include TDC2 NFDI4DS. Offers thesis topics in data science lifecycle optimization and ML system design.

• Computer Engineering
• Multimedia Security
• Embedded Systems
• +4 more

Cem Taşkın is an Assistant Professor at the Department of Computer Engineering, Trakya University, and has served as Department Head since January 2023. Previously, he was Assistant Professor at Tunca Vocational School (2011-2014) and held administrative roles as School Director (2013-2017) and Deputy Director (2012-2013). He earned his PhD in Computer Engineering from Trakya University in 2010. High School: Kırklareli Anatolian High School (1999) Bachelor's: Trakya University, Computer Engineering (2003) Master's: Trakya University, Computer Engineering (2006) PhD: Trakya University, Computer Engineering (2010) His research focuses on Computer Engineering , Multimedia Security , and Embedded Systems , with a strong emphasis on encryption techniques for MPEG video streams and mobile/web application development. Recent work includes applying interactive mobile applications for pediatric chemotherapy treatment (2022) and optimizing embedded systems as dashboards (2016). Publication trends reveal expertise in video encryption (I-frames, byte alignment, XOR operations), chaotic algorithms for sound data security, and software development for educational and administrative systems. His work bridges theoretical cryptography with practical implementations in multimedia and embedded environments. Supervised Master's Theses: Altuğ Yiğit - Human Insight in Business Processes with Deep Learning Temel Yavuz - Corporate Content-Information Management Systems Murat Emre Yücel - Text Classification with Active Learning Projects: Led initiatives at Trakya University on mobile application development laboratories (2016), campus automation systems (2016), and MPEG audio querying (2008). Participated in KOSGEB's R&D Innovation Project for Dream Cash Register/Barkot Systems (2012).

• Machine Learning
• Evolutionary Algorithms
• Optimization
• +5 more

Fabien TEYTAUD is a Maître de Conférences (Associate Professor) with Habilitation à Diriger des Recherches (HDR) in computer science, indicating his qualification to supervise PhD students within the French academic system. His research spans multiple interconnected domains within artificial intelligence and computational optimization. Dr. TEYTAUD's primary research interests include: Evolutionary algorithms and black-box optimization techniques Monte Carlo Tree Search and game AI applications Deep learning architectures, particularly Generative Adversarial Networks Multi-objective and multi-modal optimization approaches Applications of machine learning in agriculture and computer graphics His recent publications demonstrate a strong focus on improving optimization techniques through novel approaches that combine evolutionary algorithms with Bayesian methods, bandit algorithms, and deep learning architectures. He has made significant contributions to noise characterization in Monte Carlo rendering, hyperparameter optimization, and diverse image generation. Dr. TEYTAUD has an extensive publication record in top-tier conferences including GECCO, ICMLA, PPSN, and IEEE CEC, with consistent contributions from 2014 through 2022. His collaborative work spans multiple French institutions and international research teams, reflecting his active engagement in the global optimization and AI research community.

• Real-Time Systems
• Scheduling Algorithms
• Network Protocols
• +3 more

Radu Dobrin is a Senior Lecturer and Head of Department at the Department of Computer Science and Software Engineering, Mälardalen University. He is associated with the Academy of Innovation, Design and Technology. His research focuses on real-time systems, scheduling algorithms, network protocols, and embedded systems. He also contributes to curricular development in computer science education, as evidenced by his work on curriculum mapping frameworks and ethics integration. Dobrin's recent work includes advancements in TSN (Time-Sensitive Networking), fault-tolerant scheduling, and probabilistic analysis of real-time systems. Despite no explicit mentions of awards or grants, his extensive publication record indicates substantial contributions to both theoretical and applied aspects of computer science. His research trends emphasize improving scheduling efficiency in real-time environments, handling cache-related preemption delays, and enhancing network resilience through adaptive frameworks like MALOC. Collaborations with industry are evident in projects like HUBLINKED and curriculum mapping initiatives. While specific student advisees are not listed, his academic role suggests involvement in mentoring graduate students in computer science and engineering disciplines. Technical responsibilities include managing the department's academic operations and contributing to Mälardalen University's digital infrastructure, as seen through his involvement in accessibility improvements and web-related projects. No specific grants or lab affiliations are detailed in the provided texts, though his research areas imply potential involvement in EU-funded or industry-sponsored projects.

Laurent VanbeverView profile

Associate Professor

• Network Programmability
• Internet Routing
• Network Verification
• +5 more

Laurent Vanbever is an Associate Professor at ETH Zürich and heads the Networked Systems Group (NSG). He specializes in network programmability, Internet routing, and network security, aiming to make networks more performant and manageable. Previously, he held a postdoctoral position at Princeton University and earned his PhD from the University of Louvain. Education: PhD in Computer Science, University of Louvain (2012) Postdoctoral Researcher, Princeton University (2012–2014) MSc in Computer Science, University of Louvain (2008) MSc in Management, Solvay Brussels School (2010) Research Interests: His work bridges theory and practice, focusing on network programmability, routing protocols, and sustainable networking. Recent projects include NetFabric, an ETH spin-off for network observability, and studies on energy-efficient router design (e.g., Fantastic Joules ) and BGP reconfiguration ( Taming the transient ). Key contributions include xBGP, Magnifier, and SP-PIFO. Awards: Best Paper Award at HotCarbon '24 SIGCOMM 2015 Best Paper Award Multiple IETF/IRTF Applied Networking Research Prizes Advising and Labs: Supervises PhD students (e.g., Benjamin, Weiran) and manages the NSG lab. Collaborates with NetFabric, offering student projects in network observability and sustainability. Labs & Teams: Leads the NSG and co-founded NetFabric, focusing on next-gen network observability using AI. Active in teaching Communication Networks and Advanced Topics in Communication Networks .

• Evolutionary Algorithms
• Swarm Robotics
• Optimization
• +3 more

Prof. Ben Paechter is a leading academic at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, specializing in evolutionary algorithms and swarm robotics. As a Professor , he has shaped research directions in computational optimization and adaptive systems for over two decades. Research Themes : Evolutionary Swarm Robotics, Multi-Objective Optimization, Hyper-heuristics, Neural Architecture Search Key Projects : FOCAS (European Commission), KTP: Intelligent Agents (Innovate UK), PerAda (Self-Aware Systems) Collaborations : CAVES Research Group, Centre for Algorithms, Visualisation and Evolving Systems His article trends demonstrate expertise in cross-domain hyper-heuristics, swarm behavior tree evolution, and neural network architecture optimization. Recent works (2023-2024) focus on GPU-accelerated evolutionary algorithms and hierarchical swarm control systems. Scientific Awards : Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) GECCO Best Paper Nomination (2018) Advising & Grants : Supervised 5 PhD students including Paul Lapok (planar mechanisms) and Andreas Steyven (swarm diversity). Secured £2.2M+ in funding across 13 projects, including EU FOCAS (£639,999) and Innovate UK grants.

• Evolutionary Computation
• Swarm Robotics
• Optimisation Algorithms
• +4 more

Professor Emma Hart is a leading academic at Edinburgh Napier University , affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems . Her research focuses on evolutionary computation , swarm robotics , and optimisation algorithms , with applications in ecology, cybersecurity, and AI-driven design. Education: 1 st Class Honours in Chemistry (Oxford), MSc and PhD in AI (Edinburgh) Leadership: Former Editor-in-Chief of Evolutionary Computation (MIT Press, 2017–2023), ACM SIGEVO Board member, and key contributor to Scotland’s AI Strategy (2021). Her research spans evolutionary robotics, lifelong learning systems, and explainable AI. Recent work includes using novelty search to engineer diverse robot swarms, XAI for algorithm configuration, and LLM-evolved heuristics for bin packing. She explores fitness landscapes and attractor analysis to improve optimisation model robustness. Scientific Awards include the ACM SIGEVO Outstanding Contribution Award (2023) Fellow of the Royal Society of Edinburgh (2022) Bronze Humies Award (2018) Best Paper Award@GECCO 2019 She supervises PhD students such as Grant Anderson Kirsty Montague Kelly Hunter Magnus Janson Fiona Stewart and has led over £2M in grants, including EPSRC , Innovate UK , and EU projects on autonomous robot evolution and lifelong learning systems.

Sylvio Barbon JuniorView profile

Associate Professor

• Machine Learning
• Meta-Learning
• Process Mining
• +7 more

Sylvio Barbon Junior is an Associate Professor at the Department of Engineering and Architecture, University of Trieste, Italy. He leads the Machine Learning Lab and previously held roles including Research Group Leader at the State University of Londrina (Brazil) and visiting positions at Università di Modena e Reggio Emilia and Università degli Studi di Milano (Italy). His academic journey includes a Ph.D. in Computational Physics (University of São Paulo, 2011) and multiple postdoctoral and research fellowships. Research interests span Meta-Learning, Process Mining, Stream Mining, Data Mining, and Computer Vision. He focuses on applications such as fraud detection in agrifood systems, cybersecurity in IoT networks, and real-time anomaly detection in business processes. His work integrates machine learning with spectroscopic techniques for quality assessment in agriculture and medical diagnostics. Publications highlight contributions to meta-learning frameworks for graph-based search, adaptive ensemble methods for multi-label streams, and computer vision systems for cocoa classification. Awards include Best Paper recognitions at SBSI and BRACIS conferences for innovations in process mining and anomaly detection. Award highlights include Honorable Mentions for work on fake news classification (SBSI 2019) and Best Paper awards for leveraging anomaly detection in business processes (SBSI 2018). He serves as a reviewer for journals like Elsevier Information Processing in Agriculture and as an editor for Springer’s Data Semantics series. His teaching spans postgraduate and graduate programs in Applied Data Science, Machine Learning Operations, and Computer Graphics. He advises on Ph.D. and Master’s theses focusing on AutoML, Process Mining, and Machine Learning Applications.

José Fernando Lopes BarbosaView profile

Adjunct Professor

• Cyber-Physical Systems
• Multi-Agent Systems
• Manufacturing Automation
• +3 more

José Barbosa is a Senior Researcher at the Research Centre in Digitalization and Intelligent Robotics (CeDRI) and an Invited Professor at the Department of Electrical Engineering, Polytechnic Institute of Bragança, Portugal. He holds a PhD in Automation and Computer Science from the University of Valenciennes (France) and has extensive experience in European-funded projects like ARUM, GRACE, DA.RE, GO0DMAN, and PERFoRM. His expertise spans Cyber-Physical Systems, Multi-Agent Systems, and manufacturing automation, with over 40 international publications. Research focuses on self-organizing manufacturing architectures, digital twins, and bio-inspired mechanisms. He teaches courses including Microcontroller/Microprocessor Systems, Automation, and Electric Propulsion. Projects emphasize Industry 4.0, zero-defect manufacturing, and intelligent products. His work intersects IoT, data analytics, and human-machine integration in industrial systems.

Andreas ZieglerView profile

Professor

• System Software
• Linux Configuration
• Binary Tailoring
• +6 more

Andreas Ziegler is a Professor at the Department of Computer Science (INF) of Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on system software optimization , particularly in Linux kernel configurability and binary tailoring . He leads the Chair of Computer Science 4 (System Software) and develops open-source tools for minimizing software stacks while preserving functionality. University: Friedrich-Alexander University Erlangen-Nürnberg School: College of Engineering Department: Department of Computer Science Rank: Professor Research Interests: Ziegler investigates methods to automate software stack tailoring. His work spans: Configuration Interface Utilization (e.g., Linux kernel modules) Binary-Level Code Removal (ELF file manipulation without source access) Maintenance Impact Quantification (AST hashing for change detection) Publication Trends: Recent works emphasize attack surface reduction and scalable configuration testing , while earlier studies focus on feature modeling and compilation redundancy . Tools developed include GitHub-hosted open-source solutions . Advising: Supervised multiple Master’s theses on topics like header analysis for dead code detection and dynamic variability management in Linux systems.

Mostafizur RahmanView profile

Assistant Professor

• Beyond CMOS Computing
• Nanoscale 3-D Integrated Circuits
• Neuromorphic Computing
• +3 more

Mostafizur Rahman is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City. His research focuses on nanoscale computing paradigms, including Beyond CMOS computing with emerging devices, neuromorphic systems mimicking mammalian brain functions, and 3-D integrated circuits with advanced thermal management. He holds a Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst. His expertise spans nanoscale 3-D integrated circuits, crosstalk-based computing architectures, and experimental prototyping of novel nanodevices. Key contributions include developing Skybridge-3D-CMOS technology for fine-grained 3-D integration and exploring crosstalk computing as a foundation for reconfigurable digital logic. He investigates thermally aware circuit design, addressing challenges in transistor-level 3-D integration and scalability. Research trends in his articles emphasize scalable crosstalk computing (7nm to 180nm nodes), FPGA security through bitstream analysis, and neuromorphic systems leveraging wave interference functions. His work bridges nanoscale device physics with system-level design, targeting applications in high-density, energy-efficient computing systems. No scientific awards are explicitly documented in the provided materials. Advising and grants details are not included, but his labs focus on experimental prototyping of nanoscale fabrics and advanced 3-D IC technologies.

• Wireless Communication
• 5G Networks
• LTE-WiFi Coexistence
• +3 more

Dr. Vasilis Maglogiannis serves as a Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His work focuses on wireless communication systems, particularly addressing spectrum sharing challenges between LTE, Wi-Fi, and emerging 5G/6G technologies. Research areas include vehicular communication (V2X), machine learning for spectrum management, and hybrid network architectures Supervised PhD candidate Merkebu Tekaw Girmay (2018-2023) Recent publications highlight his contributions to: 5G network optimization for industrial applications and port environments Dynamic spectrum sharing between LTE/NR and Wi-Fi networks Machine learning applications in multi-RAT network environments Adaptive MBSFN resource allocation for improved network efficiency

• Human-Robot Interaction
• Automated Assembly and Disassembly
• Flexible Grippers and Manipulators
• +5 more

Karel Kellens serves as an Associate Professor in the Department of Mechanical Engineering within the Faculty of Engineering Technology at KU Leuven. He holds dual campus appointments at Diepenbeek and Leuven, coordinating research and education initiatives for Materials and Mechanical Engineering across both Geel and Diepenbeek campuses. As a senior academic staff member, he participates in the Council of the Faculty of Engineering Technology and the Mechanical Engineering Department Council, contributing to institutional governance and academic strategy. His research centers on Human-Robot Interaction , Automated Assembly and Disassembly , and Energy-Efficient Manufacturing , with particular emphasis on flexible gripper systems and circular economy applications. Current projects span robotic demanufacturing of batteries (AUDRI, DEMAN), 3D concrete printing (3D4WALL), and high-speed aseptic filling lines (ISOLATOR5.0_IRVA), demonstrating strong industry alignment in sustainable manufacturing and waste valorization. Analysis of his recent publications reveals a dominant focus on waste sorting automation and battery remanufacturing , with technical contributions in multi-robot coordination algorithms, adaptive gripper design, and radiation mapping for hazardous environments. His work consistently bridges theoretical robotics with industrial implementation, particularly in pharmaceutical and recycling sectors. Kellens actively supervises doctoral candidates including Engelen, Peeters, and Cramer, with current grants totaling over €10 million in collaborative European and national projects. He leads the Robotics, Automation and Mechatronics (RAM) research unit at Diepenbeek Campus, which operates specialized laboratories for robotic disassembly, gripper development, and sustainable manufacturing validation. Future research directions emphasize autonomous demanufacturing systems and resource-efficient circular production models.

• Bayesian Statistics
• Gaussian Processes
• Variational Inference
• +5 more

Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute , Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling. His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency. Research keywords : Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.