جست‌وجوی استاد و پژوهشگر

Francesco Semeraroمشاهده پروفایل

پژوهشگر

• Human-Robot Collaboration
• Human-Robot Interaction
• Robotics
• +۳ مورد دیگر

Francesco Semeraro is a Postdoctoral Research Associate at the University of Manchester, working within the Cognitive Robotics laboratory of the Machine Learning and Robotics research group. He is an active member of the Manchester Centre for Robotics and AI and currently contributes to the Horizon Europe MUSAE project (Ref. 101070421), focusing on non-dyadic human-robot collaboration under Professor Angelo Cangelosi's supervision. His academic journey includes: Master of Science in Robotics and Computation from University College London (2018-2019, awarded 2020) Master of Science in Bionics Engineering from Scuola Superiore Sant'Anna The BioRobotics Institute (2015-2018) Bachelor of Science in Electronic Engineering from Universita di Pisa (2012-2015) Semeraro's research centers on advancing human-robot collaboration through machine learning, with core expertise in deep learning, reinforcement learning, and affective computing. His work specifically targets flexible manufacturing environments, developing robotic systems capable of adaptive interaction and emotional state recognition to enhance human-robot teamwork efficiency and safety. His publication portfolio reveals a clear trajectory toward triadic collaboration systems, computational trust modeling, and robustness in occluded environments. Recent works demonstrate increasing sophistication in mediated object alignment and prediction tree applications, bridging theoretical machine learning with practical industrial robotics challenges. His contributions have earned significant recognition: UKRI EPSRC/BAE Systems plc. DTP CASE-conversion award for "Human-Robot Collaboration for Flexible Manufacturing" (2020) 1st Prize as Young Researcher at Ambient Assisted Living conference (2018) 1st Prize in Engineers in Business Competition (2019) RPL Summer School distinction (2022) "Vincenzo Tagliasco" Prize for robotics applications (2018) From 2021-2024, Semeraro served as Graduate Teaching Assistant for eight advanced courses including Cognitive Robotics and Foundations of Machine Learning, while securing competitive funding through the UKRI EPSRC/BAE Systems collaboration and Horizon Europe initiatives. His research integrates user study design with machine learning to create trustworthy robotic systems for industrial applications. Embedded within the Manchester Centre for Robotics and AI, Semeraro collaborates across the RAI Centre for Robotics and Artificial Intelligence project, contributing to multi-institutional efforts in developing cognitive architectures for human-robot teams. His current work explores computational trust metrics and triadic interaction frameworks to enable seamless human-robot co-production in dynamic manufacturing settings.

Maciej Bestaمشاهده پروفایل

پژوهشگر

• Sparse Graph Computations
• Large Language Models
• Graph Neural Networks
• +۷ مورد دیگر

Maciej Besta is a leading researcher at ETH Zurich's Institute for Computing Platforms, where he heads research initiatives at the Scalable Parallel Computing Lab (SPCL) and contributes to the ETH Future Computing Laboratory (EFCL). Working under the mentorship of Professor Torsten Hoefler, he has established himself as a prominent figure in high-performance computing, graph processing, and large language models. Position: Researcher at Institute for Computing Platforms, ETH Zurich Research Leadership: Head of Sparse Graph Computations and Large Language Models Research at SPCL Collaboration: Leads project management for SPCL's contributions to ETH Future Computing Laboratory Besta's research spans multiple abstraction levels, from hardware and network topologies to middleware, algorithms, and programming models. His primary focus areas include graph-enhanced language models, graph neural networks, graph databases, and sparse models, with applications across various computational settings. He approaches these problems through rigorous performance modeling and formal reasoning, emphasizing both scalability and practical implementation. His recent publications reveal a clear trend toward integrating graph structures with language models and AI systems. Besta has pioneered work on graph databases, knowledge graphs of thoughts, and higher-order graph neural networks, while maintaining his strong foundation in high-performance computing and network topology design. His research bridges traditional HPC with cutting-edge AI, creating novel approaches for efficient large-scale computation. IEEE TCSC Award for Excellence in Scalable Computing (Early Career, 2023) Multiple Best Paper Awards at Supercomputing conferences (2022, 2023) ACM SIGHPC Doctoral Dissertation Award (2022) ETH Medal for outstanding doctoral thesis (2021) Fellow of The Explorers Club (2022) Besta actively mentors ETH Zurich students through semester projects, Bachelor's, and Master's theses, focusing on graph processing and related computer science challenges. His mentorship extends beyond technical guidance, incorporating lessons from his extensive polar and mountaineering expeditions that emphasize mental resilience, efficient risk management, and leadership. He has supervised numerous student projects that have resulted in high-impact publications at top-tier conferences. As a core member of the Scalable Parallel Computing Lab, Besta collaborates with researchers across ETH Zurich and international institutions. His unique approach integrates insights from extreme environment expeditions into research methodology, creating a distinctive framework for tackling complex computational problems. The lab's work under his leadership spans theoretical modeling, practical implementation, and real-world deployment of high-performance systems.

Enrico Biniمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Control Theory
• +۳ مورد دیگر

Enrico Bini is an Associate Professor at the Department of Computer Science, Università degli Studi di Torino, Italy. His research focuses on real-time systems, control theory, and embedded systems. He has contributed extensively to scheduling algorithms, resource management, and system optimization in both academic and practical contexts. His work spans topics such as feedback-based resource allocation, multiprocessor scheduling, and the design of predictable interconnected systems. Recent publications emphasize real-time control systems, low-latency task scheduling, and efficient resource utilization in heterogeneous environments. Bini’s articles highlight advancements in optimizing task execution times, minimizing jitter, and achieving fairness in multi-core systems. His research also includes contributions to Linux kernel modules for temporal verification and novel approaches to handling communication delays in networked control systems. No scientific awards or grants are explicitly mentioned in the provided materials. His advising record is currently empty. He is affiliated with the University of Turin and collaborates with institutions in formal methods and embedded systems research.

• Embedded Systems
• Computer Architecture
• Extended Reality (XR)
• +۳ مورد دیگر

Yang Liu is a PreDoc Researcher at the Embedded Computing Systems group (Institute of Computer Engineering, Faculty of Informatics) at TU Wien, actively teaching courses including Advanced Computer Architecture and Computer Engineering projects/practicals. His office is located at Treitlstrasse 3, Room DE0210. His research centers on Embedded Systems and Computer Architecture, with specialized focus on Extended Reality (XR) and 6G Networks. He investigates scalable multi-user interaction for heterogeneous XR devices and AI-native network architectures for next-generation telecommunications, emphasizing practical implementations in distributed computing environments. Mr. Liu's 2024 IEEE Network publications reveal converging trends in XR scalability and AI-driven network design, highlighting cross-disciplinary integration of embedded systems, real-time networking, and machine learning. His work prioritizes bridging theoretical frameworks with hardware-aware solutions for emerging technologies. As a core member of TU Wien's Embedded Computing Systems research team, he contributes to both academic instruction and cutting-edge projects in cyber-physical systems, with future work likely extending into AI-optimized network architectures and immersive multi-device interaction frameworks.

• Formal Methods
• Temporal Logic
• Automata Theory
• +۵ مورد دیگر

Michele Chiari is a PostDoc Researcher at TU Wien's TrustCPS Group led by Prof. Ezio Bartocci. Previously, he was a PhD candidate and PostDoc at Politecnico di Milano's DEIB in the DeepSE group. His primary affiliations include TU Wien and the TrustCPS group, with a focus on formal verification and cyber-physical systems. Education: PhD candidate and PostDoc at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB). Research interests include formal methods for safety-critical systems, temporal logic, automata theory, model checking for recursive probabilistic programs, infrastructure-as-code modeling (DOML), floating-point computation verification, and approximate computing. Research highlights: Development of POTL (Probabilistic Operator Temporal Logic), model checkers for operator precedence languages (POMC), and contributions to the PIACERE and CORPORA projects. His work bridges theoretical formal methods with practical applications in software engineering and embedded systems. Key projects include leadership of the EU-funded MSCA PF CORPORA project (2023–present) and contributions to the PIACERE H2020 initiative. Collaborations include tools like TAFFO (floating-point precision tuner) and DOML (Infrastructure-as-Code modeling framework). Labs/Teams: Core member of TU Wien's TrustCPS Group and former contributor to Politecnico di Milano's DeepSE Group. Active in conferences like CAV, RV, and CAiSE, with recent service roles on OOPSLA and RV program committees.

• Embedded Systems
• Hardware/Software Co-Design
• Electronic Design Automation
• +۵ مورد دیگر

Vasileios Tenentes is an Associate Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He has held research positions as a Postdoctoral Researcher at the University of Southampton (2014–2018) and a Research Engineer at ARM Ltd (2017–2018). His work focuses on reliability engineering and energy efficiency in embedded systems, with expertise in hardware/software co-design, fault modeling, and VLSI architectures. Education: B.Sc. in Computer Science, University of Piraeus, 2003 M.Sc. in Computer Science, University of Ioannina, 2007 Ph.D. in Computer Science and Engineering, University of Ioannina, 2013 Research Interests: He investigates design methodologies for testability, dependability (reliability, availability, maintainability, security), and energy efficiency in digital systems. His work addresses challenges in many-core architectures, signal/power reliability characterization, and wear-out effects in IoT applications. He contributed to the PRiME project (collaborating with Imperial College, Manchester, Newcastle, and industry partners like ARM Ltd) to optimize embedded system performance and reliability. Scientific Awards: Best Doctorate (Aristoukhos Didaktes) in Informatics, University of Ioannina (2013) Advising and Grants: Advises students via MS Teams under the 'student advisor' program. His Ph.D. research was funded by the Hercules II program, which accepted only 10% of proposals. He co-developed novel architectures for control systems and participated in the PRiME project, a collaborative effort to reduce energy consumption and improve reliability in multi-core embedded systems. Labs and Teams: Affiliated with the VLSI Systems and Computer Architecture Laboratory (VCAS) at the University of Ioannina, focusing on advanced system design and reliability assessment.

Alexis Lechervyمشاهده پروفایل

استادیار

• Multimodal Learning
• Frugal AI
• Metric Learning
• +۳ مورد دیگر

Alexis Lechervy is an Assistant Professor at the University of Caen within the GREYC Laboratory's Image team. His research develops machine learning methods for efficient multimedia analysis and retrieval systems. Core research themes include: Multi-exit neural architectures for adaptive computation Multimodal fusion strategies for visual and textual data Metric learning for content-based retrieval Interactive learning systems for human-AI collaboration Resource-efficient deep learning models Dr. Lechervy's work bridges fundamental machine learning research with applications in medical imaging, biometrics, and multimedia retrieval. His algorithms optimize the balance between computational efficiency and predictive accuracy in resource-constrained environments.

• Programming Languages
• Data Structures and Algorithms
• Concurrency
• +۳ مورد دیگر

Matti Rintala is a University Lecturer at Tampere University's Faculty of Information Technology and Communication Sciences, specifically within the Computing Sciences department. His primary responsibilities include teaching core courses such as Principles of Programming Languages and Functional Programming 1, alongside contributing to Data Structures and Algorithms 1. His expertise spans programming languages, data structures, algorithms, and concurrency. Research interests focus on C++ programming paradigms, concurrency mechanisms, and educational technology. Notable work includes leveraging modern C++ features for high-level synthesis, optimizing exception handling in concurrent systems, and analyzing student preferences in hybrid learning environments. His articles consistently address practical challenges in software engineering and distributed systems. While no formal advising or grant details are provided, his teaching and research contributions highlight a commitment to advancing both theoretical and applied aspects of computing sciences. No scientific awards are documented in the provided materials.

John Antonioمشاهده پروفایل

استاد

• Distributed Computing
• High-Performance Computing
• Cloud Computing
• +۳ مورد دیگر

Dr. John Antonio is currently the Dean of the Mewbourne College of Earth and Energy and holds the Lester A. Day Family Chair at the University of Oklahoma. He is also a Professor of Computer Science, maintaining active research and teaching roles. Previously, he served as Senior Associate Vice President for Research and Partnerships, Senior Associate Dean in the Gallogly College of Engineering, and held the Howard & Suzanne Kauffmann Chair. His research focuses on distributed computing, cloud optimization, and embedded systems. Education: Ph.D. in Electrical Engineering, Texas A&M University (1989) M.S. in Electrical Engineering, Texas A&M University B.S. in Electrical Engineering, Texas A&M University Research Interests: Dr. Antonio specializes in high-performance computing architectures, optimal scheduling for data centers, low-power embedded systems, and reconfigurable computing platforms. His work includes developing ultra-low power UAV-based radar processing systems and optimizing distributed computing frameworks for global enterprises like MSCI, Inc. Awards: Lockheed Martin Award for Excellence in Engineering Teaching Halliburton Award for Excellence Ruth and Joel Spira Outstanding Teacher Award Dedicated Service Awards from Texas Tech University and OU Grants & Projects: Center for Intelligence and National Security (DOD, $330,509) CORPS Educational Partnership ($550,000) RiskMetrics Group: Distributed Computing Optimization He has overseen significant growth in OU's CS program, tripling research expenditures during his tenure as CS Director. Labs/Teams: His work involves collaborations with defense agencies (DARPA, DoD/STTR) and industry partners (MSCI, Inc.), focusing on real-time systems and large-scale computing platforms.

Laura Maria Comellaمشاهده پروفایل

استاد

• Sensor Development and Integration
• Sensor Networks
• Embedded Systems Design
• +۳ مورد دیگر

Dr.-Ing. Laura Maria Comella is a Professor at the Faculty of Mechanical Engineering and Mechatronics, Hochschule Karlsruhe. She specializes in sensor development, embedded systems, and intelligent algorithms. Her research focuses on advancing stretchable electronics, microfluidic device fabrication, and material science innovations. Her key projects include the GALLISENSE initiative and O2 Sensor System for Living Materials. She supervises doctoral candidates Christoph Lehmann and Abhishek Sharma. Recent publications emphasize breakthroughs in multi-material bonding techniques and variable-stiffness silicone substrates for flexible electronics. Her work addresses critical challenges in sensor networks and embedded system design. Comella is affiliated with the Cluster of Excellence liv MatS @ FIT and actively contributes to initiatives like the IDEASfactory@FIT. She participates in advanced training programs such as the Master Lab program and Core Lectures.

Steve Kerrisonمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Internet of Things (IoT)
• Public Key Infrastructure (PKI)
• +۴ مورد دیگر

Dr. Steve Kerrison is a Senior Lecturer in Cybersecurity at James Cook University (Singapore Campus). He holds a PhD and MEng in Computer Science from the University of Bristol, and certifications including CISSP and CCSP. His expertise spans IoT cybersecurity, embedded systems, and energy-efficient computing. Education: PhD in Computer Science, University of Bristol (2010-2015) MEng in Computer Systems Engineering, University of Bristol (2005-2009) CISSP and CCSP certifications (ISC)² Research Interests: Focuses on IoT cybersecurity, PKI for IoT, energy-efficient computing, and Industry 4.0. Collaborations include EU-funded projects (ENTRA, ICT-Energy) and Singapore’s National Cybersecurity R&D Program. Teaching: Specializes in open-source tools and community-driven cybersecurity education. Formerly a Senior Research Associate at Bristol and CTO at MICROSEC, a Singapore-based IoT security startup.

Claas Willem Visserمشاهده پروفایل

استادیار

• Fluid Mechanics
• Functional Materials
• Additive Manufacturing
• +۹ مورد دیگر

Claas Willem Visser is an Assistant Professor in the Faculty of Mechanical Engineering at the University of Twente, where his research focuses on developing multi-scale functional materials using fluid droplets and bubbles as building blocks. His work enables the optimization of mechanical, acoustic, electrical, and biological properties for diverse applications, particularly in sustainable technologies. His research interests span fluid mechanics , additive manufacturing , microfluidics , and functional materials . He is particularly known for co-developing In-air microfluidics , a novel technique for particle and 3D printing that has been commercialized through IamFluidics, a spinoff company where he serves as co-founder and Chief Scientific Officer. His lab also works on CO₂ capture materials, conductive foams, and droplet-based bioprinting. Recent publications show a strong trend in environmental materials , particularly using hydrogel particles for carbon capture, and advanced manufacturing of functional foams and sensors. His work integrates fluid dynamics with materials science to enable scalable, sustainable solutions. Rubicon Post-Doctoral Fellowship Visser has supervised several research projects and advised students, contributing to both academic and industrial advancements. His lab collaborates widely, with research supported by national and international projects. He has contributed to over 50 research outputs, including high-impact journal articles and conference proceedings. His research is conducted within interdisciplinary teams focusing on fluid dynamics, materials innovation, and sustainability. The lab leverages advanced imaging, microfluidic systems, and additive manufacturing tools to develop next-generation functional materials.

• Stream Reasoning
• Semantic Web
• Knowledge Graphs
• +۴ مورد دیگر

Pieter Bonte serves as an Assistant Professor (tenure track) in the Department of Computer Science at KU Leuven's Faculty of Engineering Science, based at the Kulak Kortrijk Campus. He holds dual institutional affiliations as a core member of Leuven.AI - KU Leuven Institute for Artificial Intelligence and participates in academic governance through the Council of the Faculty of Engineering Science, Computer Science Department Council, and Science, Engineering and Technology Group Council at Campus Kulak Kortrijk. His research program centers on Stream Reasoning and Semantic Web technologies, with specialized expertise in Knowledge Graph embeddings , neurosymbolic reasoning , and edge analytics for IoT data streams . Current investigations focus on resource-optimized stream processing, cross-context learning architectures, and ontological frameworks for heterogeneous streaming data. His work bridges theoretical foundations with healthcare and communications network applications, emphasizing practical deployment in distributed environments. Recent publications (2023-2025) reveal dominant themes in RDF stream processing systems, benchmarking methodologies for knowledge graph embeddings, and ontology engineering for streaming linked data. His output consistently targets high-impact venues including VLDB Journal, Semantic Web journal, and DEBS conference, demonstrating leadership in stream reasoning standardization through comprehensive surveys and vision papers. The research trajectory shows increasing integration of symbolic AI with neural approaches for edge-constrained environments. As principal investigator, Bonte leads three major funded projects through 2028: Stream Reasoning for efficient Edge Processing , Neurosymbolic Stream Reasoning , and Edge analytics of heterogeneous IoT data streams . His teaching portfolio includes core computer science courses in Operating Systems (X0A48A), Declarative Languages (X0C99A), Computer Systems Organization (X0E31A), and Scientific Training (X0D49A), reflecting his commitment to foundational and emerging topics in data-intensive systems. Bonte's research group operates within KU Leuven's Leuven.AI ecosystem and Kulak campus infrastructure, maintaining extensive international collaborations evident in multi-institutional publications. The team specializes in developing semantic stream processing frameworks for real-world applications in healthcare monitoring and communications networks, with active partnerships across European research institutions.

Jose Manuel Claver Iborraمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +۷ مورد دیگر

Dr. José Manuel Claver Iborra is a Full Professor at the Department of Computer Science, School of Engineering (ETSE-UV), University of Valencia. He holds a PhD in Computer Science (Parallel and Distributed Computing program) from the Technical University of Valencia and an MSc in Physics (specialized in Electronics and Computer Science) from the University of Valencia. As an IEEE Senior Member, he focuses on cloud computing, video coding, parallel/distributed systems, reconfigurable computing, and network protocols for real-time applications. Current Research: Cloud-based video encoding, GPU acceleration for DNA analysis, FPGA-based network protocols, and indoor localization systems Academic Leadership: Coordinator of the UV-Tirant node in the Spanish Supercomputing Network (RES) His recent publications analyze GPU-based motion estimation, heterogeneous computing for video standards (H.264/AV1), and QoS scheduling algorithms. He supervises PhD and Master's theses on sensor networks, FPGA programming platforms, and parallel applications. Scientific Awards IEEE Senior Member He has directed funded projects on cloud infrastructure, distributed video processing, and reconfigurable systems since the 1990s.

Zhenlin Wangمشاهده پروفایل

استاد

• Compilers
• Operating Systems
• Computer Architecture
• +۹ مورد دیگر

Zhenlin Wang is a Professor and Chair of the Department of Computer Science at Michigan Technological University's College of Computing. He earned a BS (1992) and MS (1995) from Peking University, and a PhD in Computer Science from the University of Massachusetts, Amherst (2003). He joined Michigan Tech in 2003 as an assistant professor, became associate professor in 2009, and full professor in 2015. University: Michigan Technological University School: College of Computing Department: Computer Science Academic Rank: Professor His research bridges compilers, operating systems, and computer architecture , with core focus on memory system optimization and virtualization. Key research areas include: Memory hierarchy optimization Cache replacement modeling GPU programming and architecture Virtualization and cloud computing Datacenter resource management Heterogeneous memory systems Recent publications analyze GPU speculation (GSpecPal), hardware-assisted virtualization (Accelerating Address Translation), and graph neural network-based memory inefficiency detection (GRAPHSPY). His work often integrates compiler analysis with hardware insights for performance improvements. Scientific Awards NSF CAREER Award 0643664 (2007-2012) NSF SaTC2225424 (2022-2025) Best Paper Award at ICS’23 (FLORIA paper) He has advised numerous PhD and MS students in memory systems, virtualization, and distributed computing. Current advisees include Shiwei Ding (PhD candidate) and Junyao Yang (PhD candidate).