Jesper Jensen is a Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on acoustic signal processing, machine learning, and speech enhancement, with particular expertise in applications like hearing aids, noise reduction algorithms, and deep learning architectures for audio systems. He serves as a project supervisor at institutions including Oticon A/S since 2007, and co-leads the CASPR (Centre for Acoustic Signal Processing Research) center. Key research interests include multichannel signal processing, robust speech enhancement in reverberant environments, and adaptive filtering techniques. His work integrates Bayesian methods, deep neural networks (DNNs), and sparse modeling to address challenges in audio localization, speech presence probability estimation, and sound zone control systems. Notable Achievements: Recipient of the prestigious 'Stor international pris' award in 2017 Over 135 publications in journals like IEEE Signal Processing Letters and conference proceedings Active collaborations with industry partners such as Oticon A/S His research outputs emphasize practical applications, including voice control systems for hearing aids, binaural speech enhancement in noisy environments, and acoustic reflector localization for robotics. Recent work explores transformer networks and learning-based frameworks for real-time audio processing.
Minghui Zheng is an Associate Professor in Mechanical Engineering at Texas A&M University's College of Engineering. His research focuses on human-robot collaboration, robotic motion planning, and learning-based control systems for heterogeneous robots. He leads the Control and Robotics (CtrlRobot) Lab, which develops solutions for disassembly, recycling, and remanufacturing through advanced robotics and AI. Dr. Zheng holds a Ph.D. from UC Berkeley (2017), an M.S. from Beihang University (2011), and a B.S. from Beihang University (2008). His work integrates machine learning with robotics to address challenges in collaborative environments, including task allocation, real-time human motion prediction, and ergonomic improvements in e-waste handling. Key research directions include: Knowledge-informed motion planning for manipulators Uncertainty-aware human behavior modeling Cable-driven robotic hand design Connected vehicle systems for collaborative estimation He has received prestigious awards such as the NSF CAREER Award (2021) and the Exceptional Scholar: Young Investigator Award (2023). His lab develops platforms like VR Co-Lab for disassembly training and tools like SIMPNet for spatial-informed motion planning. Publications span robotics journals and conferences, emphasizing interdisciplinary solutions at the intersection of AI, control theory, and mechanical engineering. Current projects explore multi-task learning, diffusion models for motion prediction, and energy-efficient robotic systems.
Scott L. Miller is a Professor of Electrical and Computer Engineering at Texas A&M University, holding the Debbie and Dennis Segers '75 Professorship. He serves as a faculty fellow in the Eugene E. Webb ’43 program and is affiliated with the Department of Electrical and Computer Engineering. His work focuses on communication theory, signal processing, and wireless systems design. Educational Background: B.S. in Electrical Engineering, UC San Diego (1985) M.S. in Communication Theory and Systems, UC San Diego (1986) Ph.D. in Communication Theory and Systems, UC San Diego (1988) Research interests include physical layer security, optical communication systems (VLC, FSO-mmWave), acoustic wave propagation for downhole telemetry, and interference mitigation in multiuser systems. His recent work addresses secured signal processing in emerging wireless paradigms and hybrid communication systems. He holds the IEEE Fellow distinction. Awards: IEEE Fellow Advising and grants involve mentoring graduate students in cutting-edge communication technologies. Notable contributions include experimental demonstrations of VLC-based downhole systems and analytical frameworks for IQ imbalance compensation in uplink systems.
Radu Stoleru is a Professor in the Department of Computer Science & Engineering at Texas A&M University, leading the Laboratory for Embedded & Networked Sensor Systems (LENSS). He holds a Ph.D. in Computer Science from the University of Virginia (2007) and has expertise in Edge/Fog Computing, Cyber-Physical Systems, IoT, and Distributed Systems. His research focuses on resilient edge computing frameworks, wireless sensor networks, and network security. Education: Ph.D., Computer Science, University of Virginia, 2007 M.S., Computer Science, Central Michigan University, 1998 M.S., Physics, Central Michigan University, 1997 B.S., Physics, University of Bucharest, Romania, 1993 Research Interests: Edge/Fog Computing and Mobile Edge Clouds Cyber-Physical Systems (CPS) and IoT Wireless Sensor Networks and Distributed Systems Secure Routing and Network Resilience His work emphasizes robust protocols for disaster response systems like DistressNet and secure edge coordination mechanisms. Selected Awards: NSF CAREER Award (2013) Fulbright Scholar (2013) Best Student Paper Award (EUC 2011) Grants & Funding: National Science Foundation (NSF) National Institute of Standards and Technology (NIST) Office of Naval Research (ONR) Advising: Supervised over 20 PhD/MS students, including alumni now at universities and tech firms like NVIDIA and Pinterest. Current advisees include Liuyi Jin (PhD) and Amran Haroon (PhD). Labs/Teams: Director of LENSS, collaborating on systems like EMSAssist (emergency medical voice assistant) and EdgeKeeper (mobile edge coordination).
Paulo Shakarian is an Associate Professor at the School of Computing and Augmented Intelligence, Arizona State University. His research focuses on cybersecurity, neuro-symbolic AI, and predictive modeling. He develops frameworks to analyze cyber threats using dark web data, social network analysis, and advanced machine learning techniques. Key research areas include neuro-symbolic systems for reasoning, AI-driven threat prediction, and enterprise security solutions. His work spans interdisciplinary applications in robotics, finance, and cognitive science. Shakarian has pioneered systems like Darkmention for predicting cyberattacks and frameworks for metacognition in AI. Recent publications emphasize AI robustness, geospatial security, and ensemble methods for price prediction. While no awards are listed, his contributions to cybersecurity and AI integration are notable. Advising details are unavailable, but his research teams collaborate on projects involving malware analysis, causal inference, and social media threat detection.
Prof. Dr. Barbara Hammer is a full professor of Machine Learning at Bielefeld University, Faculty of Engineering, and leads the Machine Learning Group at the Center for Cognitive Interaction Technology (CITEC). She is actively involved in multiple interdisciplinary research centers including the Bielefeld Center for Data Science (BiCDaS), the Research Institute for Cognition and Robotics, and the Institute for Bioinformatics Infrastructure (BIBI). She holds leadership roles in major research initiatives such as the TRR 318 'Constructing Explainability', the graduate school Data-NinJA on Trustworthy AI, and the research network SAIL on sustainable AI systems. Her research focuses on intelligent data analysis , explainable and trustworthy AI , and machine learning in dynamic environments . She investigates foundational algorithms for learning from complex, non-Euclidean, and evolving data streams, with applications in urban infrastructure (especially water systems), life sciences, and socio-technical systems. Her work bridges algorithmic innovation with societal impact, particularly in fairness, ethics, and human-AI interaction. The recent publications highlight a strong trend towards explainability in dynamic environments , concept drift detection and explanation , fairness in streaming data , and real-world applications in critical infrastructure . Her team develops both theoretical frameworks and practical tools, such as EPyT-Flow for water network simulation, and contributes to high-impact AI challenges in health, environment, and industry. ERC Synergy Grant – Smart Water Futures LAMARR Fellow She advises several PhD and Master’s students and leads numerous funded projects from the European Union, DFG, and national agencies. Her leadership extends to editorial roles, including on the IEEE TPAMI editorial board. She is also deeply involved in academic governance, serving on examination boards, habilitation committees, and interdisciplinary research centers, reflecting her central role in shaping AI research and education at Bielefeld and beyond.
Xiaofei LI is an Assistant Professor at Westlake University, Hangzhou, China, a position he has held since March 2020. Prior to this, he was a post-doctoral researcher and later a starting research scientist at INRIA Grenoble Rhône-Alpes, France, from February 2014 to at least 2019. He is affiliated with the PERCEPTION team and has contributed to major projects such as the ERC VHIA and EARS projects, as well as industry collaboration with Samsung Electronics. Education Bachelor of Electronic Information, Beijing Institute of Machinery, 2007 PhD in Electronics, Peking University, 2007–2013 His research focuses on speech and audio signal processing , particularly in challenging real-world environments. Key areas include multi-microphone speech processing for sound source localization, separation, and dereverberation; single-microphone signal processing for noise estimation, voice activity detection, and speech enhancement; and audio-visual fusion for speaker diarization and tracking. He employs advanced techniques such as LSTM networks , Bayesian inference , convolutive transfer functions , and deep learning in the STFT domain. His recent publications (2017–2019) demonstrate a strong trend toward multichannel speech enhancement , online speaker localization and tracking , and audio-visual integration . His work frequently appears in top-tier IEEE journals such as IEEE/ACM Transactions on Audio, Speech and Language Processing and IEEE Transactions on Pattern Analysis and Machine Intelligence. He has developed and evaluated algorithms using real datasets like LOCATA and AVDIAR, emphasizing robustness in reverberant and multi-speaker environments. Scientific Awards and Recognition No specific awards are mentioned in the provided text. Advising and Grants Currently serves as an Assistant Professor, implying student supervision and research advising, though no specific students are named. Research has been funded by prestigious grants including the European Research Council (ERC) Advanced Grant VHIA #340113 and the EU-FP7 STREP project EARS (#609465). Labs and Research Teams PERCEPTION team, INRIA Grenoble Rhône-Alpes, France Involved in the EARS, ERC VHIA, and Samsung Electronics Lito projects Contributor to the development of the AVDIAR and AVTrack-1 datasets
Jian Liu is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering, and an affiliated faculty member in the Statistics Graduate Interdisciplinary Program. He has been with the university since 2008, first as a faculty member from 2008–2014 and continuing in his current role since 2014. PhD in Industrial and Operations Engineering and Mechanical Engineering, University of Michigan, Ann Arbor (2008) MS in Statistics, University of Michigan, Ann Arbor (2006) MS in Industrial and Operations Engineering, University of Michigan, Ann Arbor (2005) MS in Mechanical Engineering, Tsinghua University, Beijing (2002) BS in Precision Instruments & Mechanology, Tsinghua University, Beijing (1999) Dr. Liu’s research centers on data analytics and system informatics, with a focus on integrating engineering knowledge, optimization, and statistical learning to model system performance, prognostics, diagnostics, and risk management. His work applies to manufacturing, civil, chemical, and software systems, emphasizing multi-source, multi-scale data fusion in hierarchical and distributed environments. Key research areas include reliability modeling, quality engineering, machine learning, and decision-making under uncertainty. His recent publications demonstrate a strong trend in applying advanced statistical and machine learning methods to real-world systems such as autonomous vehicles, water distribution networks, UAV/UGV surveillance, and healthcare monitoring. The integration of DDDAS (Dynamic Data-Driven Application Systems) frameworks, tensor decomposition, Bayesian modeling, and digital twins reflects a multidisciplinary approach spanning engineering, computer science, and data science. Honorable Mention for the Best Paper in the 2020 IISE Transactions Focus Issue on Quality and Reliability Engineering Honorable Mention for the Best Paper Award, International Conference on Industrial Engineering and Engineering Management, 2020 Outstanding Associate Editor Award, Journal of Manufacturing Systems, Spring 2019 Dr. Liu has secured research funding from the US National Science Foundation, US Department of Homeland Security, and US Air Force Office of Scientific Research. He has collaborated with domain experts on projects related to machining/assembly process improvement, water system service enhancement, and software reliability. He has advised students and contributed to professional leadership as a council member, board director, and currently as president of the Quality Control and Reliability Engineering (QCRE) Division of IISE. He is actively involved in research teams and labs focused on system informatics, data fusion, and reliability engineering, often employing simulation, sensor networks, and real-time data analysis in applications ranging from manufacturing to public health.
Charalampos Orfanidis is an Assistant Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Embedded Systems Engineering. His research focuses on Internet of Things (IoT), low-power wireless networks, wearable systems, and network performance optimization, with strong alignment to sustainable technological development. His research interests center on enabling efficient, intelligent, and secure embedded systems for real-world applications. Key areas include Low Power Wide Area Networks , Wearable Systems , 5G network performance , and reinforcement learning for industrial IoT . His work emphasizes energy efficiency, user experience, and robustness in mobile and distributed systems. The recent publications highlight a strong trend in applying machine learning and AI techniques to solve practical communication challenges in IoT and 5G environments. Topics span from detecting mobile jammers in LoRa networks to optimizing scheduling in industrial IoT using hierarchical reinforcement learning, indicating a focus on intelligent, adaptive network solutions. Scientific Contributions: Principal Investigator of the research project: Low-Cost Acoustic Networks for Underwater Communication Active contributor to international collaborative studies on 5G performance and IoT security He collaborates extensively with researchers across Europe and contributes to high-impact peer-reviewed venues. While no formal advising list is provided, his role as a PI and co-author on multiple student-involved publications suggests mentorship activity. His work bridges theoretical innovation and practical deployment in next-generation communication systems.
Björn Olofsson is an Associate Professor and Senior Lecturer in the Department of Automatic Control at Lund University's Faculty of Engineering. He also serves as the Director of First and Second Cycle Studies and is a Project Manager. He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). His academic affiliations span Lund University and Linköping University, where he was appointed Docent in 2020. He holds an M.Sc. in Engineering Physics and a Ph.D. in Automatic Control, both from Lund University. His academic journey reflects a strong foundation in engineering and control systems. His research focuses on the autonomy of robots and vehicles, with emphasis on motion planning and optimal motion control. He explores applications in ground vehicles, unmanned aerial and surface vehicles, and industrial robotics. His work intersects with key global challenges, including sustainable transport and digitalization, aligning with UN Sustainable Development Goals related to technology and health. The 15 most recent publications analyzed show a consistent trend in autonomous systems, predictive control, and robotics. Topics include uncertainty-aware motion planning, human-robot collaboration, maritime autonomy, and learning-based control. The research integrates AI, machine learning, and advanced control theory, applied across aerial, marine, and terrestrial domains. Björn actively supervises multiple PhD students and has led numerous research projects, such as ELLIIT B14 and the Center for Construction Robotics. He is involved in organizing academic events like Robotics Week for Schools and manages the RobotLab LTH infrastructure. He has taught a range of courses including Applied Robotics, Autonomous Vehicles, and graduate-level courses on motion planning and optimal control. He also supervises Master’s theses in Automatic Control and Vehicular Systems.
Shahbaz Khan is a researcher and Ph.D. candidate in Computing (Cyber Security and AI) at Edinburgh Napier University, UK. He holds B.S. and M.S. degrees in Electronics and Electrical Engineering from NFC IET and HITEC University, Pakistan. He previously served as a Lecturer in Electrical Engineering at HITEC University for eight years. His research focuses on applied cryptography, image encryption, post-quantum cryptography, and intelligent healthcare systems. He is a member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University. Education: Bachelor of Science (B.S.) in Electronics and Electrical Engineering, NFC Institute of Engineering & Technology (NFC IET), Pakistan Master of Science (M.S.) in Electronics and Electrical Engineering, HITEC University, Taxila, Pakistan Research Interests: Image Encryption Techniques (e.g., chaos-based, DNA computing, and hybrid models) Post-quantum cryptographic schemes Federated learning in healthcare and IoT Cybersecurity in industrial IoT and edge computing AI-driven medical diagnostics (e.g., EEG-based MDD detection, skin disease classification) Notable Projects: SafeNet: Carnegie-funded project (2024-2025) to enhance IoT network security through robust cryptographic methods. Development of encryption algorithms like VisCrypt , PermutEx , and Noise-Crypt for secure image transmission. AI models for decentralized EEG-based mental health detection and federated learning in skin disease diagnosis. Awards: None explicitly mentioned in available texts. Grants: £13,381 from the Carnegie Trust for the SafeNet project. Labs/Teams: Active member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University.
Professor Ville Kyrki is a Full Professor at Aalto University's School of Electrical Engineering, leading the Intelligent Robotics research group. His work focuses on intelligent robotic systems, particularly addressing challenges in imperfect knowledge and uncertain sensory data. Key research areas include computer vision, tactile sensing, robotic manipulation, and machine learning applications in robotics. Education details are not explicitly provided in the text, but his academic trajectory includes a progression from Associate Professor (appointed 2012) to Full Professor at Aalto University. Research interests span feature extraction, visual servoing, sensor fusion, and planning under uncertainty. He has pioneered methods for deformable object manipulation, including data-driven grasp synthesis and imitation learning approaches for dynamic tasks like cloth folding. His work integrates reinforcement learning with domain adaptation techniques to bridge simulation-to-real gaps. Awards include the 2022 Best Paper Award and a 2024 nomination for the Best Safety, Security, and Rescue Robotics Paper. His contributions address critical challenges in robotics such as safe human-robot collaboration and autonomous decision-making in dynamic environments. Lab activities center on the Intelligent Robotics group, which develops advanced systems for industrial automation, autonomous navigation, and human-centric robotics. Current research emphasizes scalable solutions for UAV localization, multi-agent coordination, and robust policy learning in uncertain conditions.
Iván García Daza is a Professor at the Department of Automática, University of Alcalá, Spain. He leads research in Intelligent Vehicles and Traffic Technologies (INVETT group), focusing on autonomous systems, digital twins, and ADAS technologies. His work integrates computer vision, deep learning, and human-vehicle interaction analysis. Education: PhD in Systems Engineering from University of Alcalá (2011), with a thesis on "Driver Fatigue Detection via ADAS Fusion" supervised by Rafael Barea Navarro and Luis Miguel Bergasa Pascual. Research interests include autonomous vehicle algorithms, real-time sensor fusion, virtual reality simulations for human-factor testing, and ecological conservation projects. His recent work emphasizes digital twin technology to bridge simulation-to-reality gaps in autonomous systems. Publications span autonomous driving validation frameworks, LiDAR-based navigation, and ecological studies. Notable contributions include VR-based human-vehicle interaction testing and synthetic data generation for ADAS systems. Labs/Teams: Director of the INVETT lab at the University of Alcalá, collaborating on EU-funded projects involving automotive industries and environmental agencies.
Milica D. Jovanovic is an Assistant Professor in the Department of Electronics at the Faculty of Electronics, University of Niš, Serbia. She has been with the institution since 2013, progressing from assistant trainee to her current academic rank, and holds a PhD earned in 2017 from the same faculty. PhD, Faculty of Electronics, University of Niš (2017) MSc, Microsystems Design and Engineering, Faculty of Electronics, University of Niš (2011) BSc, Computer Engineering and Informatics, Faculty of Electronics, University of Niš (2003) Her research centers on wireless sensor networks, networks-on-chip, and communication protocols, with a focus on MAC layer design, contention resolution, and indoor localization. She employs signal processing, fuzzy logic, and optimization techniques to enhance network performance and reliability. Her recent publications reflect a strong trend in improving communication efficiency and robustness in constrained environments such as wireless sensor networks and on-chip interconnects. Key themes include tone-based contention resolution, multi-channel MAC protocols, deflection routing, and UWB-based localization, placing her work at the intersection of embedded systems, networking, and signal processing. She is actively involved in one national and one international research project, contributing to both theoretical and applied aspects of electronic systems. Her work has appeared in high-impact journals including The Journal of Supercomputing , Expert Systems with Applications , and Computer Communications . She has collaborated extensively with researchers such as Igor Z. Stojanovic, Sandra Djosic, and Goran Lj. Djordjevic. While no formal advisees are listed, her involvement in doctoral studies and research projects suggests mentorship roles within her lab and department.
Dr. Dragan B. Zivanovic is a Full Professor at the Faculty of Electronics in Niš, University of Niš, Serbia, where he has been a long-standing academic in the Department of Electrical Engineering and Computer Science. His expertise lies in Metrology and Measurement Technology, with a focus on industrial instrumentation, power quality, and embedded measurement systems. PhD in Metrology and Measurement Techniques (2006), Faculty of Electronics, Niš Master’s in Metrology and Measurement Technology (1996), Faculty of Electronics, Niš Bachelor’s in Electrical Engineering and Computer Science (1990), Faculty of Electronics, Niš His research focuses on the design and optimization of measurement systems, including thermocouple linearization, power quality signal generation, and sensor signal processing. He has developed practical instrumentation adopted by international companies such as EADS Astrium and BMW, bridging academic research with industrial innovation. His work emphasizes precision, reliability, and real-world applicability in electrical and industrial metrology. The recent publications highlight a consistent trend in advancing measurement accuracy and system robustness in electrical engineering contexts. His work spans from fundamental sensor linearization to complex signal generation for power quality analysis, reflecting a deep integration of theory and practical engineering. Key themes include signal conditioning, embedded control, and industrial compliance testing. Dr. Zivanovic has contributed significantly to engineering education through textbooks and laboratory manuals, shaping curricula in measurement and instrumentation. He actively supervises students and collaborates on national and international research projects. As a consultant and contract designer, he continues to influence industrial measurement solutions globally. His affiliations and projects reflect strong industry-academia collaboration. Notable partners include: EADS Astrium GmbH Bayerische Motorenwerke AG (BMW) Löcher GmbH Mattson Thermal Products GmbH MAN Technologie AG He has also led the development of internationally adopted measurement systems and transducers, such as the Carbo100E transducer and RF ion engine testing systems.
