Anna Brunström is a Full Professor and Head of the Distributed Intelligent Systems and Communications Research Group (DISCO) at Karlstad University's Department of Computer Science. She holds a part-time role as a Researcher at the University of Malaga's Institute of Software Engineering and Technologies (ITIS). Her research focuses on computer networking, Internet architectures, low latency communication, and 5G/6G mobile systems. She leads the nationally funded DRIVE initiative and collaborates on European projects like 6G-PATH. She actively contributes to IETF standardization, notably as a former rmcat WG chair. Her work spans over 200 publications, emphasizing network measurement, latency optimization, and multipath protocols. Education: Ph.D. (1996) and M.Sc. (1993) from College of William & Mary, B.Sc. (1991) from Pepperdine University. Research Interests: Distributed systems, IoT networking (NB-IoT), satellite communication (Starlink), machine learning for positioning, and transport protocols (QUIC, MPTCP). Recent work includes latency-aware scheduling, 5G/6G performance analysis, and edge computing frameworks. Publications highlight trends in: 1) Satellite network throughput modeling, 2) 5G/6G architecture validation, 3) Machine learning applications for positioning and network analysis, 4) Cross-layer optimization of latency-critical services. Collaborations with industry and academia drive applied research in smart grids, healthcare, and automotive communication. Labs/Teams: DISCO group at Karlstad University, leading the DRIVE research profile and 6G-PATH consortium involvement.
Frederik Questier is a Researcher in Educational Science at Vrije Universiteit Brussel (VUB), located at Pleinlaan 2 in Brussels, Belgium. He holds a PhD in clustering and feature selection methods, demonstrated through his supervision of doctoral theses such as 'Contributions to Clustering and Feature Selection Methods for Clustering' (2005). His work focuses on educational technology, blended learning models, and international ICT initiatives in education systems. Research Interests: Questier explores the intersection of technology and education, including digital media literacy, mobile-assisted language learning (MALL), open-source software implementation, and public health education. His recent work on face masks during the COVID-19 pandemic highlights interdisciplinary collaboration between education and healthcare sectors. Projects & Grants: Active in both fundamental and applied research, he leads projects like the FOD27 e-health initiative (2016–2019) and MarMOOC (2016–2020), which developed hybrid learning systems in Moroccan universities. He also oversees Ghana's ICT education project (2013–2015) and manages international collaborations through VLIR-UOS in Ethiopia. Awards & Recognition: While no explicit awards are listed, his extensive publication record (113+ outputs) and h-index of 17 reflect peer recognition. His work on digital media literacy (2024) and MALL (2019) have garnered significant citations. Labs & Teams: Collaborates with institutions like Routledge (peer-review committee), Vlaams Forum voor Onderwijsonderzoek, and partners in Morocco, Ghana, and Ethiopia through projects addressing ICT integration in education systems.
Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.
Dr. Debajyoti Mondal is an Associate Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on algorithms, network visualization, computational geometry, and visual analytics. He holds a PhD from the University of Manitoba and has held postdoctoral positions at the University of Waterloo and Microsoft Research. Mondal's work spans interdisciplinary applications, including collaborations with Saskatoon Transit and academic medicine. He has authored over 100 peer-reviewed publications and secured grants such as NSERC Discovery, CFI, and Canada First Research Excellence grants. His awards include the 2023 New Scholar RSAW Award. Education: Ph.D. in Computer Science, University of Manitoba, 2016 MSc in Computer Science, University of Manitoba, 2012 BSc. Engg. in Computer Science, Bangladesh University of Engineering and Technology, 2009 Research Interests : Algorithms, graph drawing, computational geometry, visual analytics, and interdisciplinary applications in software engineering, transportation, and bioinformatics. His lab (VGA Lab) develops visualization systems for big data analysis. Key Contributions : Advanced theoretical foundations in computational geometry and graph drawing, developed practical visualization tools, and contributed to climate-related projects like Global Water Futures. Grants & Awards : NSERC Discovery Grant (2018-2024) CFI Grant (2021-2025) Microsoft Research Internship (2015-2016) New Scholar RSAW Award (2023) Labs/Teams : Leads the VGA Lab, collaborating with interdisciplinary teams on projects like Clone-World (software clone visualization) and SET-STAT-MAP (mixed data visualization).
Kevin Farley is a Professor and the Blasland, Bouck and Lee Faculty Chair in Civil & Environmental Engineering at Manhattan College, NY. He holds a PhD from MIT and has extensive experience in water quality modeling, sediment contamination, and toxic chemical bioaccumulation. Education: PhD in Civil-Environmental Engineering, MIT ME and BE in Environmental/Civil Engineering, Manhattan College Research Interests: Focuses on water quality modeling, sediment contamination (e.g., PCBs, dioxins), metal mobilization, and bioaccumulation in the Hudson River and NY-NJ Harbor. His work integrates environmental chemistry, toxicology, and engineering to address real-world contamination challenges. Professional Contributions: Served on scientific advisory panels for EPA, UNEP, and the Hudson River Foundation. Leads the Institute in Water Pollution Control at Manhattan College and teaches courses like Surface Water Quality Modeling and Environmental Fate of Toxic Contaminants. Grants & Funding: Secured over $4M in grants from agencies like EPA, NIH, and the Hudson River Foundation for projects on metal toxicity, PCB fate, and nitrogen load reduction in Long Island Sound. Labs/Teams: Directs the Institute in Water Pollution Control, fostering collaborative research on water quality and sustainable resource management.
Professor Ivan Z. Milentijevic is a full professor at the Faculty of Electronics in Niš, University of Niš, within the Department of Electrical Engineering and Computer Science. He earned his PhD in Computer Science from the same institution in 1998, following a Master's (1994) and undergraduate degree (1989) in the same field. His research focuses on systolic arrays, digital signal processing architectures, and project-based learning methodologies. He has published 8 papers in journals with impact factor and is currently involved in 1 domestic and 1 international research project. Research interests include: Optimal design of systolic arrays for matrix operations FIR filter architectures and error-tolerant systems Reconfigurable hardware and parallel processing Educational technologies in project-based learning His recent work (2002–2008) emphasizes configurable architectures for signal processing and collaborative learning systems. Earlier contributions (1996–1998) focused on systolic array optimization for linear algebra operations. Advising/grants: No explicit student/advisor relationships listed. Current projects involve 1 domestic and 1 international collaboration. Grants and funding details not specified. Labs/teams: Affiliated with the Faculty of Electronics' research groups in computer engineering and signal processing, though specific lab names are not mentioned.
Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.
Dong S. Ha is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. As Founding Director of the Multifunctional Integrated Circuits & Systems (MICS) Lab, he focuses on advanced circuit design for energy harvesting, RF systems, and high-temperature electronics. His work spans analog/RF ICs, power management circuits, and wireless IoT solutions with machine learning integration. Education: PhD (1986) and MS (1984) in Electrical and Computer Engineering from the University of Iowa; B.S. (1974) in Electrical Engineering from Seoul National University. Research interests include energy harvesting (piezoelectric, thermal, RF), high-temperature RF circuits for oil/gas/spacecraft applications, and smart IoT systems. He actively seeks students for projects in RF design, energy harvesting, and embedded systems. His lab develops cutting-edge solutions for harsh environment communication, sustainable energy systems, and smart agriculture monitoring. Awarded IEEE Fellow (2008) for contributions to VLSI design/test. Key publications span energy harvesting circuits, GaN-based high-temperature systems, and low-power IoT architectures. Current projects include self-sustaining smart farm networks using federated learning and attack-resistant sensor systems. Labs/Teams: Leads MICS Lab focusing on integrated circuits and systems. Collaborates on cross-disciplinary projects involving machine learning, embedded systems, and sustainable energy. Active in industry partnerships for aerospace and automotive applications.
Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.
Charles E. Leiserson is a Professor of Computer Science and Engineering at MIT, holding the Edwin Sibley Webster Professorship in Electrical Engineering and Computer Science. He leads the Supertech Research Group and is Faculty Director of the MIT-Air Force AI Accelerator. His work focuses on parallel computing, performance engineering, and algorithms. Leiserson is renowned for co-authoring the foundational textbook Introduction to Algorithms , widely used in computer science education globally. He has pioneered technologies like the Cilk multithreaded programming language and contributed to supercomputing architectures such as the Connection Machine CM-5. His research bridges theoretical computer science with practical applications, emphasizing cache-oblivious algorithms and compiler optimizations. Leiserson has received multiple awards for his academic contributions and educational impact, including the ACM-IEEE Ken Kennedy Award and Margaret MacVicar Fellow distinction at MIT. Education: B.S., Yale University, 1975 Ph.D., Carnegie Mellon University, 1981 Research Interests: Leiserson’s work addresses performance engineering challenges in post-Moore’s Law computing. His group develops algorithms, software systems, and hardware strategies for scalable parallelism. Key areas include parallel programming frameworks (e.g., OpenCilk), cache-aware algorithms, and compiler optimizations. He emphasizes making parallel computing accessible to mainstream programmers through tools like Cilk and educational initiatives such as MIT’s Software Performance Engineering course. Projects & Leadership: Leiserson leads the Supertech Research Group and contributed to the Cilk Arts Inc. venture, acquired by Intel. He chairs the MIT Undergraduate Practice Opportunities Program (UPOP) and teaches courses on algorithms and discrete mathematics. His leadership workshops for faculty have educated hundreds worldwide on team management in academia. Awards & Recognition: 2014 ACM-IEEE Ken Kennedy Award IEEE Taylor L. Booth Education Award ACM Paris Kanellakis Theory and Practice Award Member of the National Academy of Engineering Labs & Teams: Active in MIT’s CSAIL, Leiserson collaborates through the Supertech Group and Theory of Computation communities. His current projects include Tapir compiler infrastructure, graph neural network applications for anti-money laundering, and deterministic parallel scheduling algorithms.
Samir Ouchani is a Research Director at the CESI LINEACT laboratory (Aix-en-Provence, France), affiliated with the CESI Engineering School. He holds a PhD in Computer Science from Concordia University (2013) and an HDR (Accreditation to Supervise Research) from CNAM Paris (2022). His research focuses on securing cyber-physical systems (CPS) through formal methods, blockchain, and AI-driven approaches. Key roles include leading projects on resilient CPS architectures, IoT security, and federated learning in industrial contexts. Education: 2022: HDR in Security and Reliability of Smart CPS (CNAM Paris) 2013: PhD in Computer Science (Concordia University, Montreal) 2006: Master in Computer Science (Lorraine University, France) 1997: Engineering Degree in Computer Science (Djillali Liabess University, Algeria) Research Interests: His work emphasizes secure CPS design, including cryptographic protocols for IoT, formal verification frameworks, and AI applications for intrusion detection. He explores blockchain for smart cities, federated learning in distributed systems, and resilience engineering for autonomous vehicles. Recent projects include developing PUF-based authentication protocols and digital twin architectures for resource-constrained systems. Advising & Collaborations: Supervised PhD theses on IoT security (Fahem Zerrouki), smart city formal verification (Walid Miloud Dahmane), and federated learning in industrial CPS (Souhila Bedra Guendouzi). Collaborates with institutions like Blida University (Algeria) and HESAM University. Active in conferences such as CRISIS, ICFNDS, and IEEE WETICE. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT, focusing on model-based design, CPS simulation, and cybersecurity tool development. Engaged in EU-funded projects on Industry 4.0 and smart infrastructure security.
Aleksandar Jevremović is a Full Professor at the Faculty of Informatics and Computing, Singidunum University (Belgrade, Serbia), and holds multiple academic and professional roles. He is the Serbian representative at the UNESCO IFIP Technical Committee on Human-Computer Interaction since 2018. He has served as Vice-Dean of his faculty (2015–2018) and held visiting professorships at institutions like Ss. Cyril and Methodius University (North Macedonia) and Tallinn University (Estonia). His research focuses on cybersecurity, IoT, AI, and e-learning innovation. Education and Affiliations: External Researcher at the Mathematical Institute of the Serbian Academy of Sciences and Arts Visiting Scholar at Cyprus Interaction Lab (Cyprus University of Technology) Alumni/Postdoc Researcher at Tallinn University's HCI Group Member of IEEE and the Informatics Association of Serbia Research Interests: Jevremović’s work spans cybersecurity (e.g., intrusion detection, secure IoT protocols), human-computer interaction (HCI), AI-driven education tools, and neurotechnological applications like EEG-based assessment systems. He emphasizes practical solutions for digital safety, such as children’s online protection and cryptographic key generation from biometric data. Grants and Projects: Member of the External Advisory Committee for the EU-funded ONTOCHAIN project (2022–2023) Mentor for training schools like AAPELE Training School and NET4Age-Friendly initiatives Trainer in IoT, cybersecurity, and health promotion programs across Europe Labs and Teams: He collaborates with interdisciplinary teams on projects like CASPER (Children Agents for Secure and Privacy Enhanced Reaction) and led the development of WIDE, a collaborative web development education platform.
James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications
Prof. Panayot Vassilevski is a faculty member at the Faculty of Informatics of the Università della Svizzera italiana (USI) in Switzerland. He is engaged in collaborative research involving high-performance computing (HPC) and computational methods for partial differential equations (PDEs), with a focus on scalable discretization and solver development. His work intersects applied mathematics and computer science, particularly in the context of discrete networks and GPU-accelerated algorithms. Fields of Interest: Applied Mathematics, High Performance Computing, Machine Learning, Computational Science, Partial Differential Equations Recent work includes the GPU Accelerated Shifted Penalty Multigrid for Contact Elasticity (2024), which highlights his expertise in computational methods and HPC. This project builds on prior successful collaborations, such as the LLNL sabbatical of Prof. Rolf Krause and the internship of his student Patrick Zulian, which resulted in a publication. The collaboration also involves Dr. Zulian and the Swiss Supercomputing Center (CSCS) in Lugano. During the visit, Prof. Vassilevski and his PhD student Austen Nelson will work with Prof. Krause's team on reciprocal knowledge exchange. Advising: Austen Nelson, a PhD student under Prof. Vassilevski, is participating in the project. The visit project (Grant #229884, funded by the Swiss National Science Foundation with 10,500 CHF) emphasizes in-depth methodological collaboration and personnel exchange between USI and Portland University, as well as LLNL and CSCS. Labs and Teams: The project involves the Euler Institute at USI and the Swiss Supercomputing Center (CSCS) in Lugano. These institutions are pivotal for advancing the HPC-software project, which combines theoretical and algorithmic tasks in scalable discretization methods and PDE solvers.
Thomas Hellstrom is a Professor at the Department of Computer Science , Umeå University, Sweden. He leads the Intelligent Robotics group and is affiliated with the Center for Transdisciplinary AI . His research spans human-robot interaction (HRI) , deep learning applications , robot ethics , and field robotics for agricultural and forestry automation. Coordinated EU projects: INTRO (FP7/ITN), SOCRATES (H2020), CROPS, SWEEPER Developed intelligent walker for stroke patients with CMTS/MT-FoU/Umeå Stroke Center Key contributions in robot learning , causal reasoning , and natural language understanding Research Focus : His work emphasizes understandability in robot behavior, including causal modeling , multi-modal communication , and ethical frameworks for autonomous systems. Current project ROCC (Swedish Research Council) explores robot causality, while SOCRATES addressed social robotics in eldercare. Scientific Awards : • Erdös-Bacon-Sabbath number ≤ 13 Grants & Funding : • ROCC (2023, 3.7M SEK, Principal Investigator) • SCAI (2022, 3.7M SEK, Co-Applicant) • VINNOVA (2019, 3.47M SEK, Co-Applicant)
