Keyvan Shahin is a Professor in the Computer Engineering department at BTU Cottbus-Senftenberg. His work focuses on reconfigurable computing, embedded systems, robotics, and communication architectures. He advises multiple doctoral students including Amritha Premkumar and Muhammad Amin Qureshi. Education details are not explicitly listed in the provided text, but his research interests span adaptive UAV swarm systems, algorithm optimization for hardware architectures, and domain-specific processor design. His recent publications explore topics like modular UAV communication, polynomial connections in signal processing, and reconfigurable hardware acceleration techniques. Keyvan leads the Computer Engineering Group and contributes to projects such as the UBICO initiative. His technical expertise includes developing automated toolflows for CGRA (Coarse-Grained Reconfigurable Array) architectures and advancing adaptive processor design methodologies. He holds a Master of Science degree and can be reached at keyvan.shahin@b-tu.de . His office is located in Building 1C, Room 1.39.
Daniel Albert is an Assistant Professor of Management at Drexel University's LeBow College of Business. He serves on the Drexel University Standing Committee on Artificial Intelligence and is a Senior Fellow at The Wharton School’s Mack Institute of Technology Management. Previously, he held a tenure-track position at the University of Wisconsin-Milwaukee. His academic journey includes a Ph.D. from the University of St. Gallen, Switzerland, and research scholar roles at the Wharton School, University of Pennsylvania. Dr. Albert's research explores strategic management, organizational design, and innovation through computational modeling and empirical analysis. His work integrates psychology and neuroscience principles to study cognition and complex decision-making, with applications in financial services and healthcare industries. Current investigations focus on generative AI in strategic contexts, organizational structure optimization, and behavioral strategy experiments using large language models. Thematic analysis of his publications reveals three dominant clusters: computational approaches to organizational design (43%), cognitive and behavioral strategy frameworks (36%), and AI applications in management/education (21%). His work exhibits increasing methodological sophistication, with recent publications leveraging large-scale data analytics and AI simulation techniques to examine strategic decision-making patterns. Notable Scientific Recognition: Sumantra Goshal Award for Research & Practice (2021) Distinguished Paper Award from Academy of Management (2022) Research Methods Paper Prize from Strategic Management Society (2024) Freddie Reisman Award for scholarly impact (2022) Innovation in Teaching Award for AI integration (2022) Dr. Albert teaches strategy and competitive advantage courses across MBA and Executive MBA programs, incorporating generative AI tools for experiential learning. He provides editorial leadership for the Journal of Organization Design and has served on review boards for Long Range Planning and Organization Science . His research collaborations involve interdisciplinary teams from Wharton's Mack Institute and Drexel's AI initiative, focusing on technology-driven organizational innovation.
Amin Rezaei is an Assistant Professor in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He directs the Computer Architecture, Reliability, and Security Laboratory (CARS-Lab) and serves as the undergraduate advisor for Computer Engineering. His expertise spans computer security, machine learning, and computer architecture, with a focus on hardware security and protecting hardware intellectual property. Rezaei holds a Ph.D. in Computer Engineering from Northwestern University. He is a senior member of IEEE and a lifetime member of ACM and AAAI. His research has been funded by NSF grants totaling $500,000, and he has published over 50 peer-reviewed articles in top venues like DAC, ICCAD, and DATE. He has also served on technical program committees for major conferences such as IPDPS and GLSVLSI. His research interests include hardware security, machine learning applications in secure systems, and novel logic locking techniques. Key projects include developing CycPUF (a cyclic physical unclonable function) and frameworks for detecting hardware Trojans using multimodal deep learning. His lab emphasizes democratizing research by publicly sharing source code. Education: Ph.D. in Computer Engineering, Northwestern University Awarded Academic Excellence Award, University of Louisiana at Lafayette Awards: Walter P. Murphy Fellowship, Northwestern University Royal E. Cabell Fellowship, Northwestern University Grants: NSF CISE-MSI and CRII programs. Rezaei’s advising focuses on undergraduate Computer Engineering students, with Spring 2025 hours on Zoom. His lab’s current research includes projects on hardware Trojan prediction, ML-based security evaluations, and dynamic logic locking.
Zheng Chang is a Professor at the Institute of Computing Technology, School of Computer Science and Technology, University of Chinese Academy of Sciences in Beijing, China. With a PhD from the University of Jyväskylä (2013), Chang has established a prolific research career with over 240 publications spanning from 2011 to 2025. Chang maintains strong collaborative ties with researchers at the Chinese Academy of Sciences' Shenyang Institute of Automation and has developed significant international collaborations, particularly with Finnish researchers including Timo Hämäläinen. Chang's research focuses on cutting-edge areas at the intersection of wireless communications, artificial intelligence, and edge computing. Their work prominently features federated learning, UAV networks, resource allocation, and privacy-preserving techniques for IoT applications. Recent publications demonstrate a strong emphasis on vehicular edge intelligence, split learning architectures, and RIS-assisted communications. The research output shows consistent growth with 42 publications in 2024 alone, indicating an active and expanding research program. Chang's 15 most recent publications reveal a clear research trajectory toward addressing the challenges of resource-constrained edge environments through innovative learning architectures. The work spans theoretical frameworks for privacy preservation in federated learning to practical implementations for UAV networks and vehicular systems. A notable trend is the integration of AI-generated content techniques with traditional federated learning approaches to overcome data scarcity issues in edge environments. While specific awards aren't documented in the provided text, Chang's extensive publication record in top-tier IEEE journals including IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, and IEEE Transactions on Vehicular Technology demonstrates significant scholarly impact. The research has been widely cited, with multiple highly cited co-authors including Timo Hämäläinen (66 co-authored papers), Zhu Han (38 papers), and Geyong Min (31 papers). Chang's work shows strong practical applications across multiple domains including intelligent transportation systems, smart agriculture, healthcare IoT, and next-generation 6G networks. The research program appears well-funded through collaborations with major institutions and demonstrates clear translational potential for real-world deployment in edge computing environments.
Fabrice Duval is a Researcher-Lecturer and Head of the Technical Research and Transfer Platform at CESI. He holds a Habilitation to supervise research from Rouen University (2016) and a PhD in Electrical Engineering from Université Paris-Saclay (2007). His research focuses on Industrial Performance Optimization, Electromagnetic Compatibility (EMC), and robotics-driven manufacturing systems. He leads the 'Engineering and Numerical Tools' research team and manages the CESI FabLab. His educational activities include teaching Electromagnetism, Electricity, and Embedded Systems to engineering students (1st/2nd years and masters). He has supervised numerous PhD theses, including those on EMC in electric vehicles, motor impedance modeling, and PEEC-based EMI analysis. His work emphasizes practical applications, such as the CoRoT project (2016-2021) improving flexible manufacturing systems with autonomous/collaborative robots. Key research themes include robotics task allocation (e.g., auction-based optimization), digital twin integration for resilient systems, and synthetic data for industrial object detection. His publications span peer-reviewed journals and conferences, addressing topics like magnetic shielding effectiveness in automotive applications and modular mobile manipulator coordination. Awards: While no specific honors are listed, his extensive supervision record and participation in high-impact projects reflect his academic standing. He actively organizes scientific events and contributes to industry-research partnerships. Lab involvement: As FabLab manager, he bridges academic research with hands-on innovation, fostering experimental prototyping in electromechanical systems and smart manufacturing technologies.
Peter Gorm Larsen is Professor in the Department of Electrical and Computer Engineering at Aarhus University, affiliated with the DIGIT Center. With over 300 research outputs since 1989, his work centers on digital twins, cyber-physical systems, and embedded AI. Current projects include RoboSAPIENS (robotic adaptation), CP-SENS (cyber-physical sensing), and DIGIT-BENCH (digital twins for wind industry test benches). Research focuses on developing reusable digital twin platforms and autonomous reconfiguration frameworks. Recent publications demonstrate advancements in semantic interoperability for coupled digital twins and service-oriented architectures. Since 2018, Professor Larsen has received international recognition including the Reviewer Recognition award. His work spans robotics, sustainable energy systems, and healthcare applications. Professor Larsen maintains active collaborations across Europe and serves on multiple program committees. Teaching activities include Discrete Mathematics and supervision of graduate students in software engineering and embedded systems.
Lukas Esterle is Associate Professor at Aarhus University's Department of Electrical and Computer Engineering. His research focuses on enabling collaboration among autonomous systems through computational self-awareness, collective learning, and autonomous decision-making. His work draws inspiration from natural systems, psychology, and anthropology to develop resilient distributed systems. Key research areas include: autonomous multi-agent coordination, digital twin architectures, computational trust models, and distributed learning frameworks for robotic systems. His investigations target applications in smart cities, industrial automation, and edge computing environments. Professor Esterle teaches courses in Software Engineering, Computer Games Technologies, and Autonomous Agents & Multi-Agent Systems. He coordinates multiple European research projects including DIAMOND (Developing Personalized Capabilities), FLOCKD (Federated Learning for Collaborative Knowledge), and MARVEL (Multimodal Data Analytics for Smart Cities).
Dr. Syeda Fizzah Jilani is a Lecturer in the Department of Physics at Aberystwyth University, UK, and a course coordinator for the MSc Radio Spectrum Engineering program. She holds a PhD in Antennas and Electromagnetics from Queen Mary University of London (2018) and previously worked on US DOE-funded research at the University of Maine. Her research focuses on advanced antenna systems, 5G/6G wireless technologies, millimeter-wave applications, and AI-driven environmental and medical imaging solutions. She has authored/co-authored over 50 papers, a book titled Antennas and Propagation for 5G and Beyond , and secured grants from L3Harris and QinetiQ. Key achievements include the 2024 Aberystwyth University Visibility Award and inclusion in the global '100 Brilliant and Inspiring Women in 6G' list. She leads projects on spectrum monitoring, reconfigurable antennas, and smart city applications, while actively supervising PhD students and contributing to IEEE and IET committees. Education: PhD in Electronic Engineering, Queen Mary University of London (2015–2018) Research Interests: Electromagnetics and Antenna Design Millimeter-Wave and Terahertz Systems Flexible Wearable Antennas Deep Learning in Remote Sensing and Medical Imaging 6G/5G Wireless Communication Networks Sustainable Transportation Solutions Recent Research Trends: Her work spans cutting-edge antenna technologies for 6G, AI-driven environmental monitoring (e.g., landslide detection, air quality analysis), and medical imaging advancements (e.g., breast lesion classification). Recent publications highlight innovations in reconfigurable phased arrays, liquid metal phase shifters, and explainable neural networks for healthcare. Awards and Grants: Principal Investigator: 3-year Serapis Project with QinetiQ/DSTL (£X) PI: L3Harris Technologies Grant (£Y) Featured in '100 Brilliant and Inspiring Women in 6G' (2024) Aberystwyth University Visibility Award (2024) Advising & Grants: Supervises PhD scholars and leads industry-academia collaborations. Projects include spectrum monitoring systems with the UK Spectrum Centre and smart city MIMO antenna arrays. Active in professional service as an IEEE AP-S Young Professional Ambassador and IET Antennas Technical Committee member. Labs/Teams: Works within the Department of Physics' Antennas and Propagation group, focusing on 6G infrastructure and wearable electronics research.
Simon Thevenin is an Assistant Professor in the Automation, Production, and Computer Sciences Department at IMT Atlantique in France since 2018. He holds a Ph.D. from the University of Geneva (2015) and previously worked as a Postdoctoral researcher at HEC Montreal and an Algorithm Expert at Quintiq. His research focuses on optimization methods for production management, including scheduling, planning, and manufacturing line design. He leads projects such as the EU-funded ASSISTANT (2020-2023), ALICIA (2023-2025), and ACCURATE (2023-2026), advancing AI-driven solutions for sustainable manufacturing and supply chain resilience. His work integrates machine learning, robust optimization, and digital twin technologies to enhance production systems' adaptability and efficiency. Education: Ph.D. in Production Systems Scheduling, University of Geneva (2015) Teaching/Research Assistant, University of Geneva (2010-2015) Research Interests: His research emphasizes optimization under uncertainty, reconfigurable manufacturing systems, and AI applications in production. He explores topics like lot-sizing models, equipment lifecycle management, and circular manufacturing ecosystems. Grants/Projects: ASSISTANT (EU-funded, 2020-2023): AI for production digital twins ALICIA (EU-funded, 2023-2025): Circular production resource ecosystems ACCURATE (EU-funded, 2023-2026): Supply chain resilience against disruptions Labs/Teams: Part of the LS2N research lab (Modelis team) at IMT Atlantique, specializing in logistics and industrial optimization.
João Paiva Cardoso is a Full Professor at the Department of Informatics Engineering, Faculty of Engineering of the University of Porto, Portugal, and a Senior Researcher at INESC TEC's Human-Centered Computing and Information Science Centre since July 1, 2011. He earned his PhD in Electrical and Computer Engineering from the Technical University of Lisbon in 2001 and has built an extensive career spanning academia and industry. Former positions: IST/UTL (2006-2008), Senior Researcher at INESC-ID (2001-2009), University of Algarve (1993-2006) Industry experience: PACT XPP Technologies, Inc., Munich, Germany (2001/2002) Professional affiliations: Senior Member of IEEE, Member of IEEE Computer Society, Senior Member of ACM His research centers on compilation techniques, domain-specific languages, reconfigurable computing, and high-performance embedded systems. Recent publications reveal a strong focus on hardware/software co-design, with particular emphasis on FPGA acceleration and source-to-source compilation techniques for optimizing CPU-FPGA applications. His work addresses critical challenges in High-Level Synthesis compatibility, task graph representations, and holistic partitioning approaches. Professor Cardoso has served in leadership roles for numerous international conferences including General Chair of FPL'2013 and General Co-Chair of multiple ARC symposia. His supervised theses cover diverse topics from energy-efficient classification techniques to FPGA-based kNN accelerators, demonstrating the breadth and practical application of his research vision.
Alberto Lerner is a Senior Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. His research focuses on advancing database systems, storage architectures, and hardware-software co-design. He holds a strong interest in computational storage, network-accelerated query processing, and the integration of modern hardware technologies like CXL into database engines. Lerner's work emphasizes performance optimization and scalable solutions in data-intensive computing environments. His research interests include database architecture, storage co-design, hardware acceleration, and network-driven computing. Recent publications highlight innovations in point cloud data processing, reprogrammable storage devices, and software-defined controllers for NAND flash systems. Lerner's articles reflect a trend toward leveraging modern hardware advancements to enhance database and storage performance. He has contributed to frameworks like BABOL and Data Pipes, advancing declarative control over data movement and network-based graph mining.
Minxuan Zhou is an Assistant Professor of Computer Science at Illinois Institute of Technology. His research focuses on systems, high-performance computing, and parallel computing with an emphasis on processing-in-memory (PIM) acceleration, fully homomorphic encryption (FHE), and hyperdimensional computing. He holds a Ph.D. and M.S. in Computer Science from the University of California San Diego, and a B.S. in Computer Science and Technology from Beihang University. His work spans hardware-software co-design, cryptographic acceleration, and energy-efficient computing systems. Research Interests Processing-in-Memory (PIM) architectures for accelerating machine learning and graph processing Hardware acceleration for secure computation (FHE) Hyperdimensional computing frameworks for edge AI Thermal-aware design and memory optimization in 3D systems Recent Research Trends Recent publications emphasize co-optimization techniques for PIM architectures, FHE hardware acceleration, and privacy-preserving machine learning. His work often bridges theoretical algorithm design with practical hardware implementations using novel memory technologies like ReRAM and 3D-stacked memory. Grants & Labs Engaged in cutting-edge research through collaborations on projects involving PIM accelerators and encrypted computation systems. His lab focuses on developing full-stack solutions for next-generation computing architectures.
Bo Tan is an Associate Professor in Communications Engineering and Signal Processing at the Department of Electronic and Electrical Engineering, University College London (UCL). He previously held positions as Associate and Assistant Professor at Tampere University, Finland, where he retains a Docent title. His research focuses on radio signal processing, wireless communications, integrated sensing and communication (ISAC), machine learning applications in healthcare, and radar systems. He has coordinated major projects such as ANCHOR (Horizon Europe MSCA Doctoral Networks) and SPHERE-DNA (Finland Research Council), and serves as an Associate Editor for IEEE Wireless Communications Letters. Education: PhD in Engineering, University of Edinburgh (United Kingdom) Postdoctoral Researcher roles at: University College London (Computer Science & Electronic Engineering) University of Bristol (Electrical and Electronic Engineering) Research Interests: Wireless communication systems (5G/6G, THz, mmWave) Radar-communication integration and dual-functional systems Machine learning for signal processing and healthcare Integrated sensing and communication (ISAC) architectures Non-invasive healthcare monitoring via passive sensing Key Projects (2021–2025): ANCHOR (2025–2029): Research Coordination Committee lead SPHERE-DNA (2021–2024): PI focusing on federated learning for eHealth ACCESS (2021–2025): PI on wireless network security DIOR (2022–2027): Coordinator for H2020 MSCA Professional Roles: Vice Chair, IEEE Finland AP/ED/MTT Chapter Reviewer for IEEE/ACM/IET journals Labs/Teams: Leading the SPHERE-DNA initiative on privacy-preserving healthcare systems Active contributor to the IEEE's wireless communication standards
Prof. Amila Akagic is an Associate Professor at the Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo. Her expertise spans Computer Architecture , Artificial Intelligence , Computer Vision , and High-Performance Computing . She holds a Ph.D. from Keio University (Amano Lab) and has conducted research at UC Riverside and the University of Ljubljana under prestigious scholarships including the Fulbright and MEXT awards. Her work focuses on FPGA-accelerated algorithms, reconfigurable architectures, and deep learning applications in medical imaging and environmental monitoring. Education: Bachelor's/Master's (Electrical Engineering) – University of Sarajevo (2006/2009) Ph.D. (Computer Science) – Keio University, Japan (2013) Research Interests: Combines image segmentation , embedded systems , and energy-efficient computing . Recent work includes semantic tumor segmentation (MRI), blockchain-based UXO tracking, and FPGA-based CRC acceleration. She explores interdisciplinary topics like AI-driven plant phenotyping and wildfire detection using computer vision. Recent Trends in Publications: Over 40% of her 2023-2025 articles address medical imaging and fire detection , with growing emphasis on explainable AI and humanitarian technology . Her work frequently uses frameworks like OpenVINO and TensorFlow to optimize computational efficiency. Awards: Fulbright Visiting Student Award (2007/2008) MEXT Scholarship (Japan, 2010) Grants & Labs: Active in the HiPEAC project (EU Horizon Europe), focusing on edge computing and smart grid resilience. Collaborates with the Embedded Systems and Architectures Lab (UC Riverside) and Amano Lab (Keio University). Labs/Teams: Leads research groups in FPGA-accelerated architectures and AI-driven environmental sensing. Engages in humanitarian demining initiatives via data observatories and blockchain systems.
Hongliang Zhang is a faculty member at Peking University, School of Electronics , specializing in wireless communications and next-generation networks. His work spans reconfigurable intelligent surfaces (RIS), holographic MIMO, and meta-material-based sensing. Research Focus: 6G wireless systems, integrated sensing and communication (ISAC), beamforming optimization, and anti-jamming techniques. Recent Trends: Integration of large language models in aerial edge computing, generative AI for radio map benchmarks, and security frameworks for vehicular metaverses.
