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Chaitali ChakrabartiView profile

Professor

• Algorithm-architecture co-design
• Low power embedded systems
• Heterogeneous SoC systems
• +7 more

Chaitali Chakrabarti is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). She has been at ASU since 1990 and serves as the Associate Director of the Center for Wireless Information Systems and Computational Architectures (WISCA). Her research focuses on algorithm-architecture co-design for signal processing, low-power embedded systems, heterogeneous computing, AI hardware acceleration, and federated learning. She has led numerous NSF and industry-funded projects, including collaborative research on 3D ultrasound imaging and energy-efficient computing systems. Education: Ph.D. in Electrical Engineering (University of Maryland, 1990), M.S. in Electrical Engineering (University of Maryland, 1986), B.Tech. in Electronics and Electrical Communication Engineering (Indian Institute of Technology Kharagpur, 1984). Research Interests: Low-power embedded systems, near-threshold computing, heterogeneous SoC design, AI/ML hardware acceleration, medical imaging systems, and federated learning. Her work emphasizes energy-efficient architectures and algorithm-hardware co-design for applications like ultrasound imaging and wireless communication. Awards include IEEE Fellow, Distinguished Alumnus Awards from IIT Kharagpur and University of Maryland, and multiple teaching excellence awards (Top 5% Teaching Award at ASU from 2012–2024). She has authored over 200 publications and holds patents in energy-efficient computing and hardware security. Grants: Over $10M in funding from NSF, industry partners (e.g., Google, Intel), and government agencies. Notable projects include SHF: Medium: Collaborative Research on 3D Ultrasound (NSF, 2014–2018) and Energy-Efficient Near-Threshold Computing (2012–2018). Labs/Teams: Leads WISCA and collaborates with SenSIP. Her group develops reconfigurable architectures (e.g., Versa, Millipede) and AI accelerators like MAX2 and Sonic Millipede.

Christopher BrintonView profile

Associate Professor

• Network optimization
• machine learning
• edge and fog computing
• +9 more

Christopher Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. His work sits at the intersection of networking, communications, and machine learning, investigating contemporary network architectures including Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. Dr. Brinton received his PhD in Electrical Engineering from Princeton University in 2016, following a Master's degree in EE from Princeton in 2013 and a BSEE from The College of New Jersey in 2011. Prior to joining Purdue, he served as the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering at Princeton University. His research focuses on network optimization, machine learning, edge and fog computing, signal processing, wireless networks, distributed computing, communication and information theory, and social learning networks. Dr. Brinton's work employs foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in emerging network architectures. His research spans from theoretical foundations in information theory to practical implementations in next-generation wireless systems, with an increasing emphasis on distributed learning approaches that maintain privacy while achieving high performance. Dr. Brinton's recent publications demonstrate a strong trajectory toward integrating machine learning with networking challenges, particularly in distributed and edge computing environments. His work shows growing emphasis on federated and decentralized learning approaches that address privacy concerns and communication constraints in real-world network deployments, while also advancing theoretical understanding of network optimization problems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton has been active in teaching across multiple levels, lecturing courses on network principles, signals and systems, and computer communication networks. He is also the co-author of "The Power of Networks: Six Principles That Connect Our Lives," which has been used for introductory college courses worldwide and formed the basis for popular MOOCs that have collectively enrolled over 400,000 students. His teaching spans undergraduate and graduate levels, including specialized courses on wireless communication networks and Python for data science. As leader of the ION research lab, Dr. Brinton oversees a team investigating the theoretical and practical aspects of network optimization, with projects spanning fog computing systems, IoT, NextG Wireless, and social learning networks. His lab collaborates with major industry partners including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson on cutting-edge research in next-generation networking technologies.

• Artificial Intelligence
• Robotics
• Human-Computer Interaction
• +3 more

Bart Craenen is a Research Fellow at the Institute of Sensors, Signals & Systems within the School of Engineering & Physical Sciences at Heriot-Watt University, United Kingdom. His research focuses on human-robot interaction, planning systems, and explainable AI, with specific applications in underwater robotics and human-aware mission specification. His primary research spans artificial intelligence, robotics, and human-computer interaction, emphasizing human response modeling, preference elicitation in planning algorithms, and transparent decision-making systems for autonomous robots. He conducts empirical user studies to analyze collaborative human-robot task performance, cognitive load, and behavioral adaptation in joint-task environments. Analysis of his 2020-2025 publications reveals consistent advancement in explainable planning frameworks, particularly for underwater robotics where human oversight is critical. Key trends include integrating real-time human preferences into mission specification, developing XAIP (Explainable AI Planning) interfaces, and quantifying human behavioral responses during robot task execution. His work bridges theoretical planning algorithms with practical human-robot collaboration scenarios, emphasizing usability and trust calibration.

Seyed Majid ZahediView profile

Assistant Professor

• Computer Architecture
• Computer Systems
• Theoretical Computer Science
• +4 more

Seyed Majid Zahedi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on the intersection of computer architecture, computer systems, and theoretical computer science, with particular emphasis on game theory applications in distributed systems and resource allocation fairness. Education: PhD in Computer Science, Duke University, USA (2018) MSc in Computer Engineering, University of Tehran, Iran (2012) BSc in Computer Engineering, University of Tehran, Iran (2009) Research Interests: Zahedi explores advanced topics such as multi-agent systems, computational sprinting, and incentive mechanisms for distributed resource management. His work bridges theoretical foundations with practical implementations in data centers and multiprocessor systems. Teaching: Zahedi has taught courses including Real-Time Operating Systems (ECE 350), Systems Programming and Concurrency (ECE 252), and Special Topics in Computer Software (ECE 750). He emphasizes hands-on learning through lab projects and quizzes. Awards: Zahedi has received several accolades, including the Best Paper Award at HPCA 2018 and recognition in IEEE Micro Top Picks for contributions to resource fairness in multiprocessor systems. Advising & Grants: He currently advises over a dozen graduate and undergraduate students, focusing on projects like incentive-compatible blockchain protocols (Algorand) and cooperative load management (Malcolm). His research has been supported by grants for cybersecurity innovations.

• Real-Time Systems
• Multiprocessor Scheduling
• Real-Time Operating Systems
• +4 more

Cláudio Maia is a Lecturer and Integrated PhD Researcher at the Faculty of Engineering of the University of Porto, Portugal. He holds a B.Sc. and M.Sc. in Computer Engineering from the Polytechnic Institute of Porto and a Ph.D. in Electrical and Computer Engineering from the University of Porto (2018). His research focuses on real-time systems, including real-time operating systems, hypervisors, multiprocessor architectures, and scheduling theory. He has contributed to advancements in task scheduling for multiprocessor platforms, resource contention analysis, and WCRT (Worst-Case Response Time) methodologies. His work has been published in venues such as RTCSA, RTNS, and DATE, and includes a Best Paper award at ICESS 2021. He is affiliated with the CISTER Research Centre, where he collaborates on projects involving embedded systems, real-time critical CPS, and automotive systems integration. His recent research explores memory and bus contention analysis in real-time task models, with applications to multiprocessor systems and industrial automation. Education: Bachelor’s Degree (B.Sc.) in Computer Engineering, School of Engineering, Polytechnic Institute of Porto Master’s Degree (M.Sc.) in Computer Engineering, School of Engineering, Polytechnic Institute of Porto Ph.D. in Electrical and Computer Engineering, University of Porto (2018) Research Interests: Real-Time Scheduling (WCRT Analysis, Partitioned/Global Scheduling) Multiprocessor Architectures and Resource Contention Real-Time Operating Systems and Hypervisors Parallel Task Models (Fork-Join, 3-Phase Tasks) Embedded Systems and Cyber-Physical Systems (CPS) Awards: Best Paper Award at ICESS 2021 for 'Bus-Contention Aware WCRT Analysis' Key Contributions: Developed contention-aware scheduling models for real-time multiprocessor systems Advanced WCRT analysis techniques for 3-Phase Task Models Contributed to frameworks for automotive systems integration and CPS emulation Labs/Teams: Active member of the CISTER Research Centre, collaborating on industrial and academic projects in real-time systems and embedded computing.

Anne C. ElsterView profile

Professor

• High-Performance Computing (HPC)
• Parallel Computing
• GPU Computing
• +6 more

Prof. Anne C. Elster is a Professor of Computer Science at NTNU's Department of Computer & Information Science (IDI), leading the HPC-Lab. She specializes in High-Performance Computing (HPC), GPU acceleration, and heterogeneous systems. Her work spans HPC applications in medical imaging, seismic processing, and oil & gas simulations, with collaborations at CERN, NVIDIA, and Schlumberger. Elster holds a PhD in Electrical Engineering from Cornell University (1994) and is an IEEE Senior Member since 2000. She has supervised over 70 master students and numerous PhD candidates, emphasizing GPU computing. Her teaching includes courses like Parallel Computing and Compilers , with a focus on programming and problem-based learning. She leads EU projects like CLOUDLIGHTNING (2015–2018) and has organized major conferences (e.g., ISC, SC, PARA). Her HPC-Lab is a CUDA Research and Teaching Center, and she advocates for HPC infrastructure investments in Norway through policy engagement.

Matthias JanetschekView profile

Associate Professor

• Computer Science
• Cybersecurity
• Distributed Systems
• +5 more

Matthias Janetschek holds the position of Associate Professor at the Management Center Innsbruck (MCI), Austria, since September 2020. Previously, he served as a Lecturer at MCI (2018–2020) and was a Co-Founder/Senior Developer at Felix Solution UG (2013–2015). He earned a PhD in Computer Science from the University of Innsbruck (2011–2019), alongside earlier degrees in Computer Science (BSc 2002–2007, MSc 2007–2011). His research focuses on distributed systems, workflow management, cybersecurity, and IoT , with notable contributions to scientific workflow execution on cloud/manycore architectures and AR/VR applications in education. He has supervised over 18 bachelor/master theses addressing topics like IoT security, reinforcement learning, and edge computing. Teaching responsibilities include courses such as Distributed Systems , Virtual Reality , and Computer Architecture at MCI. He has also completed further training in online education tools (e.g., Sakai, Adobe Connect) and corporate communication. His publications span journals like Multimodal Technologies and Interaction and conference proceedings on workflow systems, cloud computing, and AR usability. Current work emphasizes large-scale IoT security assessments and edge computing optimizations.

Luigi PannocchiView profile

Researcher

• Computer Networking
• Real-Time Systems
• Autonomous Systems
• +3 more

Luigi Pannocchi is a Postdoctoral Researcher at Sant'Anna School of Advanced Studies since September 2021. His research focuses on optimizing network infrastructures, virtualized systems, and autonomous systems. He holds a PhD from 2015 to 2018, with a strong emphasis on real-time systems, embedded architectures, and control frameworks. Research interests include datacenter optimization, virtual machine (VM) placement in NFV environments, AI-driven network management, and the integration of real-time systems with autonomous robotics. His work bridges theoretical models with practical implementations in cloud computing and embedded systems. Recent publications highlight advancements in VM placement algorithms using logic programming, AI applications in network virtualization, and optimization techniques for NFV infrastructures. His 2023 IEEE TCCPS Early-Career Award recognizes contributions to softwarized network services and real-time systems. Key contributions include frameworks for autonomous systems prototyping, power consumption modeling in embedded systems, and HIL testing methodologies for multi-vehicle systems. His research spans interdisciplinary areas such as robotics, cloud engineering, and real-time computing.

Andrea MorichettaView profile

Researcher

• Machine Learning
• Distributed Systems
• Edge Computing
• +7 more

Andrea Morichetta is a University Assistant (Postdoc) at the Institute of Information Systems Engineering, Technische Universität Wien, Austria. He leads research in distributed systems, focusing on the intersection of machine learning and systems design. His work emphasizes edge computing, cloud-native systems, and AI-driven resource management. Education: PhD in Electrical, Electronics, and Communications Engineering (Politecnico di Torino, 2020), supervised by Prof. Marco Mellia. Visiting researcher at Cisco (2017), AIT (2019). Research Interests: ML for cloud/edge systems, federated learning, predictive scaling, cybersecurity in distributed environments, and intent-based systems management. Recent work explores multi-dimensional elasticity in stream processing, asynchronous federated learning, and self-referential digital anatomies. Teaching: Courses include Distributed Systems (VO), Distributed Systems Technologies (VU), and project/seminar courses (SE/PR). Actively supervises bachelor/master theses on topics like federated learning, edge computing, and serverless architectures. Key Contributions: Developed Polaris scheduler for SLO-aware edge scheduling, FL-SERENADE for network anomaly detection, and RainCloud for IoT swarm coordination. Co-authored over 50 peer-reviewed papers in top venues (IEEE Cloud, SOSE, ICSOC). Awards: 2019 Best Poster Runner-Up (TMA Paris), 2018 Best Student Paper (ITC Vienna).

• High Performance Computing
• MPI Programming
• Parallel Algorithms
• +2 more

Ioannis Vardas is a Researcher in Parallel Computing at TU Wien's Faculty of Informatics. His work focuses on optimizing high-performance computing systems through advanced process mapping, MPI programming, and performance analysis tools. Key research areas: Topology-aware process mapping for HPC MPI library development and optimization Performance profiling of parallel applications Resource allocation in hierarchical architectures Energy-efficient parallel computing He contributes to projects improving MPI performance tools and develops methods for efficient resource utilization in supercomputing environments.

• Optimization
• Scheduling Algorithms
• Artificial Intelligence
• +2 more

Dr. Felix Winter is a PostDoc Researcher in the Databases and Artificial Intelligence group at Vienna University of Technology. He teaches courses including Bachelor Thesis supervision, Project in Computer Science, and Database Systems. His research focuses on optimization algorithms for industrial scheduling problems, including oven scheduling, artificial teeth production scheduling, employee task distribution, and paint shop scheduling. Methodologies include hybrid approaches combining exact methods with metaheuristics. Recent publications demonstrate consistent application of operations research techniques to manufacturing contexts, with emphasis on developing practical solutions for complex scheduling challenges in automotive supply chains and dental production environments. Dr. Winter supervises student research projects including diploma theses on optimization topics. Current projects include the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017-2025).

• Distributed Systems
• Mobile Edge Computing (MEC)
• Resource Management
• +3 more

An Du is a Visiting Scientist at TU Wien's Distributed Systems research group (E194-02), affiliated with the Institute of Information Systems Engineering. His research focuses on Mobile Edge Computing (MEC), hierarchical collaborative computing frameworks, and resource management in distributed systems. He collaborates with Northeastern University, China, and has contributed to optimizing service placement and task scheduling in MEC environments. His work addresses challenges in real-time processing and resource allocation across cloud-edge-device hierarchies, aiming to minimize system costs while ensuring performance stability. Funded by grants from the National Natural Science Foundation of China and others, his research emphasizes long-term system efficiency and cross-node resource utilization. Key projects include the development of an online optimization framework for MEC systems, leveraging Lagrangian relaxation techniques to enhance computational efficiency. His findings have been published in IEEE Transactions on Services Computing and presented at international conferences.

Pantelis FrangoudisView profile

Researcher

• Mobile/Wireless Networking
• Edge Computing
• Network Softwarization
• +4 more

Pantelis Frangoudis is a PostDoctoral Researcher at the Distributed Systems Group of the Vienna University of Technology since 2019. His academic journey includes a Ph.D. in Computer Science (2012), M.Sc. (2005), and B.Sc. (2003) from the Department of Informatics, Athens University of Economics and Business (AUEB) , Greece. His research spans Edge Computing , IoT , Network Softwarization , Federated Learning , and Wireless Networking , focusing on adaptive systems and security. Education B.Sc. in Computer Science (2003), AUEB M.Sc. in Computer Science (2005), AUEB Ph.D. in Computer Science (2012), AUEB His research explores Edge Computing Continuum systems, with emphasis on QoS-aware orchestration , serverless edge deployment , and self-adaptive IoT services . Recent work addresses federated learning for 6G , WebAssembly for edge serverless , and dynamic resource allocation in heterogeneous environments. Key projects include AloTwin (2023–2025), INTEND (2024–2026), and EDENSPACE (2019–2022). His publications (2025–2020) cover machine learning for 5G/6G , MEC application orchestration , and IoT security , with a focus on distributed systems and edge-cloud collaboration . Scientific Awards ERCIM Alain Bensoussan Post-Doctoral Fellowship He has supervised multiple diploma theses at TU Vienna, including topics like serverless edge computing , traffic light AI optimization , and vertical farming IoT systems . His collaborations with institutions like EURECOM (France) and IRISA/INRIA (France) reflect a global academic footprint.

• Engineering Management
• Strategy and Organizational Behavior
• Transportation and Logistics
• +5 more

Dr. Amir Abdekhodaee is a Senior Lecturer at the School of Engineering, Swinburne University of Technology. He specializes in Engineering Management, focusing on enhancing learning through innovative pedagogical approaches such as wiki-based collaborative projects and transparency initiatives. He is the Work Integrated Learning (WIL) Coordinator, driving strategies to align academic programs with industry needs, particularly in construction management and digital literacy. His research spans strategy, organizational behavior, transportation logistics, and applied mathematics, with notable contributions to BIM adoption in facilities management, performance management of virtual teams, and sustainability practices in SMEs. He has supervised doctoral research in sustainability management, banking efficiency, and digital skills in construction. Awards: MH de Fina Memorial Prize (1999, University of Melbourne) Industry Partnerships: Collaborates with Turner and Townsend on engineering project management units. Teaching: Designs and convenes core engineering management units, emphasizing practical industry relevance. His work integrates academic and industrial perspectives, fostering employability through enhanced student placements and industry-driven curricula.

Martin KristjansenView profile

Researcher

• Computer Science
• Real-Time Systems
• Model Checking
• +5 more

Martin Kristjansen is a Researcher in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. He is part of the Distributed, Embedded and Intelligent Systems research group, focusing on real-time systems, model checking, and distributed computing. His research interests span multiple areas of computer science with a focus on: Real-Time Systems and Schedulability Analysis Model Checking and Formal Verification using UPPAAL Distributed Systems and Multi-core Architectures Reinforcement Learning applications in battery management Fleet management in noisy environments Automata theory and symbolic model learning Dr. Kristjansen's publication record shows a consistent research trajectory from avionics systems in 2019 to more recent work on stochastic digraph real-time task models in 2024. His work demonstrates a strong focus on applying formal methods to practical engineering problems, particularly in safety-critical systems, with research areas spanning automaton theory (100%), mobile robotics (85%), multicore systems (85%), and performance analysis (85%). He has been actively involved in significant research projects including: STORM_SAFE: Software reliability for critical infrastructures (2024-2027) as a Project Participant Digital technologies for Industry 4.0 (2019-2021) as a Project Participant Dr. Kristjansen collaborates extensively with Professor Kim G. Larsen and other researchers at Aalborg University, contributing to the institution's strong reputation in formal methods and real-time systems research. His work includes two significant datasets related to symbolic timed models and distributed fleet management, demonstrating commitment to research reproducibility.