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Kangsoo Kim is an Assistant Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary, where he leads the Human-X Interaction (HXI) Lab. He holds a Ph.D. in Computer Science from the University of Central Florida and previously served as a postdoctoral researcher at the University of Delaware and the Synthetic Reality Lab at UCF, with an additional appointment in the College of Nursing. Education: Ph.D. in Computer Science, University of Central Florida, 2018 M.S. in Electronics and Computer Engineering, Hanyang University, 2011 B.S. in Electronics and Computer Engineering (Cum Laude), Hanyang University, 2009 Dr. Kim's research focuses on Human-Computer Interaction, particularly in immersive eXtended Reality (XR), including Virtual, Augmented, and Mixed Reality. His work explores virtual avatars, social interaction in XR, perception and cognition, and the integration of XR with IoT and digital twins. He investigates how embodied virtual humans influence social presence, trust, and user behavior in educational, healthcare, and industrial settings. His recent publications span top-tier venues such as IEEE TVCG, ISMAR, and IEEE VR, with themes centered on virtual agents, empathy in XR, avatar embodiment, and industrial applications of XR and digital twins. Trends include the use of AI-powered avatars, physiological feedback integration, and XR for public education and infrastructure safety. Scientific Awards: AKCSE Early Achievement Award (2023) Innovation Award at TechConnect World (2021) ACM SUI Best Paper Award (2019) ICAT-EGVE Best Demo Audience Choice Award (2020) ACM VRST Best Student Paper Award (2017) Dr. Kim actively mentors students and has advised numerous graduate researchers in areas including virtual pets, avatar behavior, XR for health, and industrial applications. He has secured research funding through competitive fellowships and grants, including UCF Graduate Fellowships and IEEE VR Doctoral Consortium support. He has also contributed to patents in AR magnification and serves as an Associate Editor for Presence: Virtual and Augmented Reality. He leads the HXI Lab, which conducts interdisciplinary research bridging computer science, psychology, and engineering to advance XR technologies for real-world impact in education, healthcare, and industry.

• Human-Computer Interaction
• eXtended Reality (XR)
• Virtual Reality (VR)
• +13 more

Vikas Kumar Garg is an Assistant Professor at Aalto University specializing in Quantum Computing and Artificial Intelligence (AI)/Machine Learning (ML)/Deep Learning (DL), with research spanning theoretical and applied domains including Computational Biology, Healthcare (protein and drug design), Material Synthesis, Cybersecurity, Energy systems, E-commerce, Wireless networks, Blockchain, IoT, Computer Vision, and Natural Language Processing. His work focuses on human-assisted drug discovery , generative models , and learning under uncertainty , intersecting quantum AI with optimization and game theory. Key application areas include Biopharma, Retail, Manufacturing Automation, and FinTech through his industry collaborations. Professor Garg maintains extensive academic-industrial partnerships with leading researchers: Tommi Jaakkola (PhD supervisor, MIT) Cynthia Rudin (SM supervisor, Duke University) Raquel Urtasun (MS supervisor, University of Toronto/Waabi) Regina Barzilay and Stefanie Jegelka (MIT) Microsoft Research teams (Adam Kalai, David Alvarez-Melis, Ofer Dekel) CMU (Steven Wu), Caltech/Hebrew University (Katrina Ligett) Amazon Research (Inderjit Dhillon), Facebook AI (Lin Xiao) University of Cambridge (Roberto Cipolla), University of Chicago (Risi Kondor) As Co-Founder and Chief Scientist of YaiYai Oy, he drives quantum-AI solutions for global clients across Biopharma, Energy, and Gaming sectors while maintaining active academic research.

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +15 more

Dr. Tim Schwartz is an Associated Member at the German Research Center for Artificial Intelligence (DFKI) located at the Saarland Informatics Campus in Saarbrücken, Germany. He is affiliated with the Ubiquitous Media Technology Lab (UMTL) where he conducts research at the intersection of human-robot interaction, multimodal interfaces, and industrial applications. His work spans over two decades with significant contributions to the fields of robotics, augmented reality, and Industry 4.0 implementations. Dr. Schwartz's research interests focus primarily on Human-Robot Interaction , Multi-modal Interaction , and Industry 4.0 applications. His work explores how humans and robots can effectively collaborate in industrial settings, with particular attention to communication modalities, task division, and intuitive interfaces. Recent projects include human-robot collaboration in assembly cells, social cognitive robots for warehouse environments, and augmented reality applications for aircraft manufacturing. Analysis of his publication history reveals a clear trajectory from foundational work in multimodal interfaces and context-aware computing (2005-2015) toward increasingly applied research in industrial robotics and human-robot collaboration (2016-2024). His most recent publications demonstrate a strong emphasis on practical implementations in manufacturing and warehouse environments, with a particular focus on optimizing work dynamics between humans and robots. Dr. Schwartz actively engages in academic supervision, offering thesis opportunities such as the user-study on optimal work dynamics in human-robot collaboration at the Power4Production Hall in Saarbrücken. His collaborations extend to institutions including ZeMA (Zentrum für Mechatronik und Automatisierungstechnik gGmbH), indicating strong industry-academia partnerships. At DFKI, he works within a vibrant research community that includes numerous colleagues in the Ubiquitous Media Technology Lab, contributing to a collaborative environment focused on cutting-edge research in human-robot interaction, multimodal systems, and industrial applications of artificial intelligence.

• Human-Robot Interaction
• Multi-modal Interaction
• Industry 4.0
• +3 more

Dr. Alfonso José López Rivero is a Professor at the School of Computer Science , Pontifical University of Salamanca , specializing in Statistics and Operations Research . He earned his PhD from the Universidad Pontificia de Salamanca in 2004 with a thesis on Software Quality in Information Selection and Classification , supervised by Dr. Luis Joyanes Aguilar. Education: PhD in Computer Science (2004), Universidad Pontificia de Salamanca. His research spans Digital Transformation , Machine Learning , and Sustainable Mobility , with a focus on applications in healthcare, battery recycling, and strategic management. Recent work includes IoT systems for voice-based disease detection and AI-driven sustainability in SMEs. Key trends in his publications include Electric Vehicle (EV) Optimization , IoT in Healthcare , and Ethical AI . He leads the Gestión tecnológica y ética del conocimiento research group, emphasizing technological ethics and data-driven decision-making.

• Statistics
• Operations Research
• Strategic Management
• +12 more

Ahmad Lotfi is a Professor of Computational Intelligence and Head of Department of Computer Science at Nottingham Trent University , with a Visiting Professor role at Tokyo Metropolitan University . He leads the Computational Intelligence and Applications (CIA) research group and has supervised over 30 PhD students to completion. PhD in Learning Fuzzy Systems (University of Queensland, 1995) MTech in Control Systems (Indian Institute of Technology, India) BSc in Control Systems (Isfahan University of Technology, Iran) His research spans computational intelligence , ambient intelligence , robotics , and machine learning , with applications in dementia monitoring , smart environments , and healthcare technology . Recent work focuses on using thermal sensor arrays for privacy-preserving human activity analysis. He has secured funding from Innovate UK , EPSRC , The Royal Society , and Horizon 2020 , with projects like iCarer (assistive living), SmartBerry (agricultural AI), and BigSpark (financial data augmentation). His 15 most recent articles demonstrate expertise in Wi-Fi-based activity recognition , EEG fall detection , and thermal sensor fusion . Senior Member IEEE Member of British Computer Society (MBCS) Editorial roles in Soft Computing and Journal of Ambient Intelligence and Smart Environments He has served as Program Chair for conferences like PETRA and ICCRT , and as Keynote Speaker at PETRA 2023 . His 28+ years of academic leadership include organizing UKCI and UKRAS conferences.

• Computational Intelligence
• Ambient Intelligence
• Robotics
• +7 more

Jonathan White serves as a Senior Lecturer in Cyber Security within the College of Arts, Technology and Environment at the University of the West of England (UWE). With over 23 years of prior industry experience in telecommunications critical infrastructure systems, he joined UWE in January 2020 after transitioning from roles as software developer, product specialist, and management leader in real-time embedded systems. His educational background includes an M.Sc. in Cyber Security (with Distinction) and B.Sc. in Computing for Real-time Systems, both from UWE, where he is currently pursuing a PhD focused on Federated Learning security tradeoffs. White's research centers on Federated Learning applications for IoT security, container security analysis, and machine learning-driven threat detection in home networks. Analysis of his publication record reveals a strong focus on practical security implementations, particularly in containerized environments (Docker security analysis, cyber ranges) and Federated Learning security frameworks. His work consistently bridges theoretical machine learning concepts with tangible security applications for IoT and edge devices, emphasizing privacy-performance tradeoffs in distributed systems. Scientific Recognition: Fellow of the Higher Education Academy (FHEA) White actively contributes to cyber security education through innovative teaching methods including the 'Cyber Funfair' immersive learning platform and Scalextric-based physical system hacking demonstrations. His industry background in telecommunications critical infrastructure informs his practical approach to security education and research, particularly regarding real-time system vulnerabilities and high-availability network security requirements. His technical expertise spans C and Python programming, network security protocols, and specialized knowledge in securing containerized environments and IoT ecosystems. Current research includes longitudinal analysis of container image vulnerabilities and development of modular cyber range infrastructure for security training.

• Cyber Security
• Federated Learning
• Network Security
• +5 more

Professor Ulysses Sengupta is a leading academic and practitioner in architecture and urbanism at the Manchester School of Architecture , where he holds the Professor of Architecture and Urbanism title. He is the founding director of the [CPU]lab (Complexity Planning and Urbanism Research Laboratory) and leads the [CPU]ai master’s design atelier, which integrates data-driven design and computational thinking into architectural education. His work bridges academic research, pedagogy, and practice through Softgrid Limited , an internationally networked architecture and urban research firm. Founding director of [CPU]lab Leader of [CPU]ai atelier Director of Softgrid Limited International collaborations with institutions like the Architectural Association and University of Westminster Research Interests focus on cities as complex adaptive systems, utilizing big data, IoT, and machine learning to address urban sustainability and governance. Key themes include: Smart cities and digital urban disruption Agile governance and participatory planning Complexity-based design methodologies Global South urbanism and resource-limited settings Interdisciplinary tools for urban data analysis Eco-architecture and futureproofing urban systems Articles highlight his work on urban data analysis, mobility-as-a-service, transdisciplinary systems mapping, and the intersection of social/environmental justice in smart cities. His Google Scholar profile reflects a consistent focus on computational urbanism and sustainable transformation frameworks. Scientific Awards include shortlisting for the Colvin Prize (2025), a prestigious honor in architectural research. His projects span ESRC, H2020, and Innovate UK grants, including leadership in the DACAS network and Synchronicity EU initiative. Professional Contributions encompass advisory roles for international organizations like RIBA, policy briefs for the United Nations University, and design competitions in China, India, and the UK. His [CPU]ai atelier redefines architectural education through computational complexity.

• Architecture
• Urbanism
• Complexity Science
• +14 more

Antonios Deligiannakis is a Professor at the School of Electronic and Computer Engineering of the Technical University of Crete, specializing in database systems and distributed data processing. His academic career includes a postdoctoral position at the National and Kapodistrian University of Athens (2006-2007) and a visiting researcher role at AT&T Labs-Research (2003). His educational background includes: PhD in Computer Science, University of Maryland, USA (2005) Master's Degree in Computer Science, University of Maryland, USA (2001) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1999) Professor Deligiannakis's research spans Databases , Stream Processing , and Sensor Networks , with pioneering work in Approximate Query Evaluation for massive datasets and Complex Event Processing in distributed environments. His contributions enable efficient analytics in resource-constrained settings through techniques like synopses-based engines and windowed outlier detection. His 15 most recent publications (2020-2025) reveal a dominant focus on distributed streaming analytics, with recurring themes of cross-platform integration, federated learning, and extreme-scale interactive systems. Key innovations include the INFORE framework for interactive analytics, DAG* for IoT workflow optimization, and communication-efficient federated learning techniques—demonstrating consistent translation of theoretical advances into production-ready platforms. Scientific Awards: No specific awards were listed in the provided material. Information about advisees and research grants was not provided in available documentation, though his leadership in the Distributed Information Systems and Applications laboratory suggests active mentorship and project direction. He directs research in the Distributed Information Systems and Applications laboratory, developing systems for real-time analytics across domains including maritime surveillance, financial technology, and IoT platforms, with emphasis on scalability and fault tolerance in geo-distributed environments.

• Databases
• Sensor Networks
• Analytical Data Processing
• +4 more

Pedro Miguel Sanchez Sanchez is a researcher affiliated with the University of Murcia , specializing in Machine Learning , Cybersecurity , and IoT . He earned his doctorate in 2024 with the thesis Identical IoT device identification via hardware performance fingerprinting and Machine Learning , supervised by Dr. Alberto Huertas Celdrán and Dr. Gregorio Martínez Pérez. Research Interests Pedro's work focuses on applying Machine Learning and Federated Learning to solve critical challenges in Cybersecurity and IoT environments. His research includes: Developing decentralized federated learning frameworks (e.g., Flighter, ProFe) for secure and efficient model training. Designing malware detection systems using system call data and large language models . Enhancing IoT device authentication via hardware fingerprinting techniques. Exploring moving target defense strategies to counter zero-day attacks on IoT networks. Building knowledge graphs for cyber defense applications. Recent Publications Pedro's 2025–2024 publications demonstrate a strong focus on decentralized federated learning , malware mitigation , and hardware-based security . Key trends include: Advancements in zero-shot learning for multilingual tasks on edge devices. Security frameworks (e.g., Cyberforce, Sentinel) for military reconnaissance and industrial IoT . Behavioral analysis techniques for ransomware detection and continuous authentication . Robustness studies on federated learning architectures under adversarial conditions. Creation of benchmarks like LwHBench for hardware performance evaluation. Collaborations He has collaborated with researchers in the Intelligent Systems and Telematics group, contributing to projects in 5G security , crowdsensing platforms , and trusted execution environments .

• Machine Learning
• Cybersecurity
• IoT
• +3 more

Craig Shultz is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign and co-founder of Fluid Reality . His research focuses on interactive embedded systems and haptic actuation , particularly tactile interfaces for VR/AR, IoT, and wearable devices. He employs an interdisciplinary approach blending psychophysics, electrical and mechanical engineering, and human-computer interaction (HCI) to advance haptic rendering. Specializes in surface haptics , non-contact haptics , and electroosmotic pump arrays . Develops technologies for scalable shape displays , mid-air haptic interactions , and haptic-augmented spatial computing . Teaches Interactive Haptic Systems (since 2025). His research has led to 6 best paper awards and nominations at ACM and IEEE venues, including the IEEE Transactions on Haptics Best Application Paper of the Year (2018). He received the Sony Faculty Innovation Award (2025) and the IEEE Technical Committee on Haptics Early Career Award (2025) . His publications emphasize haptic rendering , low-latency touchscreens , and non-contact tactile actuation . He actively collaborates with institutions like Northwestern University and KAIST HCI . Key research areas: tactile interfaces, scientific engineering, VR/AR haptics, and haptic design practices. Technologies developed: Fluid Reality haptic gloves, DynaButtons, LRAir synthetic jets, and TriboTouch micro-patterned surfaces.

• Interactive Embedded Systems
• Haptic Actuation
• Surface Haptics
• +7 more

Jesse Sowell is an Associate Professor at the University College London (UCL) in the STEaPP (Science, Technology, Engineering and Public Policy) department. His work intersects internet governance , cybersecurity policy , and public policy analysis , with a focus on emerging technologies like artificial intelligence and the Internet of Things. He contributes to the Sustainable Development Goal 11 (Sustainable Cities and Communities) through digital policy research. Email: j.sowell@ucl.ac.uk Collaboration Network: Active in international policy and cybersecurity research Research interests span: AI-driven policy crafting and systematic review methodologies Security lifecycle of IoT devices and networks Adaptive governance frameworks for digital risks Competition dynamics in internet infrastructure Transnational cybersecurity norms and incident response IPv4-IPv6 transition policy comparisons Publication trends reveal a focus on internet governance , cybersecurity , and AI policy , with increasing attention to IoT security (2017-2021) and AI integration in policymaking (2025). His work bridges technical and policy dimensions, emphasizing network resilience and multi-stakeholder collaboration .

• Internet Governance
• Public Policy
• Cybersecurity
• +5 more

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +7 more

Ramavarapu S Sreenivas is a Professor in the Industrial and Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign , with research appointments at the Coordinated Science Laboratory (CSL) and the Information Trust Institute (ITI ). He holds a joint affiliation with the Electrical and Computer Engineering department and serves as the Arthur Davis Faculty Scholar since 2016. Ph.D. , Electrical and Computer Engineering, Carnegie Mellon University (1990) M.S.E.E. , Carnegie Mellon University (1987) B.Tech , Electrical Engineering, Indian Institute of Technology Madras (1985) His research focuses on Discrete-Event/Discrete-State (DEDS) systems , applying Coding Theory, Machine Learning, and Information Theory to develop near-optimal supervisory policies for applications in wireless networks, automated manufacturing, and healthcare systems . He leads the Center for Autonomous Construction and Manufacturing at Scale (CACMS) , established in 2023. Recent publications highlight advancements in liveness enforcement in Petri nets , fault-tolerant control , and IoT-based load scheduling . His work bridges theoretical rigor with practical implementations in Distributed Control, Network Coding , and Reinforcement Learning . UIUC Campus Award for Excellence in Graduate and Professional Teaching (2023) Arthur Davis Faculty Scholar (2016) Senior Member, IEEE (2002) James Franklin Sharp Outstanding Teaching Award in Industrial Engineering (2017, 2012) Sreenivas has taught graduate and undergraduate courses in Control Systems, Integer Programming, and Financial Computing since 1992. He co-instructed courses in Health Technology and contributed to the Master of Science in Financial Engineering (MSFE) program, which ranks 4th nationally.

• Discrete-event systems
• Control theory
• Petri net modeling
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dejan Markovic is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), holding the Mukund Padmanabhan Term Chair in Electrical Engineering and serving as Area Director for Circuits and Embedded Systems. His research spans implantable neuromodulation systems, domain-specific compute architectures, and energy-efficient design methodologies for biomedical and embedded applications. Dr. Markovic earned his PhD (2006) and MS (2000) from UC Berkeley and BS (1998) from the University of Belgrade, Serbia. His research focuses on ultra-low-power integrated circuits for neural interfaces, including artifact-free stimulation/sensing platforms and neural recording front-ends, alongside domain-specific architectures for sparse linear algebra in IoT and mobile computing. Recent work demonstrates strong trends in medical neuromodulation and energy-efficient VLSI for biomedical signal processing and wireless communications. His scientific contributions have been recognized with prestigious awards including IEEE Fellow (2021), ISSCC Lewis Award (2014), ISSCC Jack Raper Award (2010), and NSF CAREER Award (2009). Additional honors include UC Berkeley's David J. Sakrison Memorial Prize (2007) and multiple best paper awards. Dr. Markovic co-founded semiconductor IP startup Flex Logix Technologies (2014) and leads the DMGroup research lab at UCLA. He teaches core courses including ECE 115C (Digital Integrated Circuits), ECE M216A (VLSI Design), and BME M260 (Neuroengineering), mentoring students in cutting-edge VLSI design for medical and communication applications. The DMGroup lab specializes in developing implantable neuromodulation systems, domain-specific compute engines, and novel design methodologies, with recent projects featured in UCLA's coverage of brain-computer interface technology and Anari AI's $2M funding for personalized AI hardware.

• Implantable neuromodulation systems
• Domain-specific compute architectures
• Design methodologies
• +3 more