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Matthew StoneView profile

Professor

• Natural Language Generation
• Computational Semantics
• Knowledge Representation
• +5 more

Matthew Stone is a Professor in the Department of Computer Science at Rutgers University, New Brunswick. His primary affiliations are within the School of Arts and Sciences, with cross-listed involvement in Cognitive Science and Perceptual Science. He focuses on designing conversational agents that integrate natural language understanding and generation with grounded, meaningful interactions. Key roles include teaching courses such as CS 440 (Introduction to AI) and CS 504 (Computational Modeling). His research emphasizes the computational foundations of meaning, integrating philosophy, linguistics, and AI to create systems that contextualize communication through environmental grounding, intentional reasoning, and collaborative dialogue. Notable projects include the SPUD system for natural language generation and work on multimodal interaction through gesture and facial signals. He has been funded for projects like 'Community-led Language Technology for NJ: A Design Justice Approach.' Education: PhD in Computer Science (assumed based on faculty role). Awards: Influential Paper Recognition (SIGGRAPH '94), RDI^2 Fellowship for Malihe Alikhani. Grants: Community-led Language Technology Project (2021). Stone collaborates with interdisciplinary teams and mentors students like Malihe Alikhani. His labs explore topics such as embodied conversational agents, computational pragmatics, and language grounding in robotics. Recent work emphasizes ethical and societal implications of AI, advocating for inclusive technology design.

• AI and Robotics
• Human-Robot Interaction (HRI)
• Autonomous Agents
• +7 more

Dr. Abolfazl Zaraki is a Senior Lecturer in AI and Robotics at the University of Hertfordshire's Department of Computer Science, part of the School of Physics, Engineering & Computer Science. He leads the Robotics Research Group and previously held roles at Cardiff University's School of Engineering and the IROHMS Research Centre. His academic journey includes a Master's in Mechatronics from University Technology Malaysia (2010) and a PhD in Automatic Robotic and Bioengineering from the University of Pisa (2014). He has held Research Fellow positions in Italy and the UK until 2019. Dr. Zaraki's research focuses on AI-driven autonomous systems, social robotics, and assistive technologies. Key projects include the EASEL, BabyRobot, and JAMES EU initiatives, alongside the Innovate UK-funded InSight project. His work emphasizes Human-Robot Interaction (HRI), trusted autonomy, and applications in healthcare and industrial contexts. Notable contributions include the development of the Kaspar humanoid robot for autism therapy and advancements in reinforcement learning for robotic control. His recent publications (2021–2025) explore agentic AI, memory-driven systems, and personalized LLMs for HRI, alongside technical advancements in robotic control, communication systems, and bio-inspired robotics. His research bridges theoretical AI innovation with practical applications in healthcare, education, and industrial automation. Zaraki has collaborated internationally across institutions and industry partners, contributing to 35+ research outputs. His work aligns with global trends in ethical AI, explainable systems, and human-centric robotics design.

• Audio Signal Processing
• Music Information Retrieval
• Natural Language Processing (NLP)
• +3 more

Panagiotis G. Zervas is an Associate Professor at the Department of Electrical and Computer Engineering, University of Peloponnese (since 2020). His expertise spans audio signal processing, music information retrieval, and natural language processing for knowledge extraction. He teaches courses including Signals & Systems, Digital Signal Processing, and Machine Learning. His research focuses on AI-driven applications in sound analysis, music feature extraction, and multimodal information processing. Education: PhD (2007) in Electrical Engineering from the University of Patras, specializing in Greek prosody modeling for text-to-speech systems. Previous roles include Assistant Professorships at Hellenic Mediterranean University (2015–2020) and Technical Educational Institute of Crete (2008–2015). Research Interests: Natural Language Processing (NLP) for text analysis and large language models (LLMs) Audio signal processing, voice analysis, and embedded systems AI applications in job market analytics and skills frameworks Machine learning for music information retrieval Notable Projects: Principal Investigator in EU projects EU-ALMPO (2025–), Train4Blue (2025–), GROWTH4BLUE (2024–), and MICROIDEA (2024–) World Bank consultant (2023–) for AI-driven employment systems in Greece and Pacific Islands Publications in journals like 'Acoustics' and conferences like WAC 2022 and Forum Acusticum 2023 Office: Building K, Office K2.07 | Contact: pzervas@uop.gr

Nadine LavanView profile

Lecturer

• Voice Perception
• Identity Perception
• Person Perception
• +3 more

Dr Nadine Lavan is a Lecturer in Biological Psychology at Queen Mary University of London, affiliated with the School of Biological and Behavioural Sciences. She co-leads the Equality, Diversity, and Inclusion initiatives within the School. Her research focuses on how listeners perceive and process information about individuals through their voices, including identity, gender, age, and personality traits. She employs experimental psychology, cognitive neuroscience, and acoustic signal processing methods. Her research interests include voice perception, face perception, emotion perception, and first impressions. Key projects involve understanding neural mechanisms behind voice recognition and the impact of familiarity on trait judgments. She has secured significant grants, including a £308,606 Wellcome Trust Sir Henry Wellcome Postdoctoral Fellowship for her work on 'Perceiving people from voices' and an EPSRC-funded project on voice identity perception. Dr Lavan’s work bridges cognitive psychology and neuroscience, aiming to uncover how auditory signals convey social information. She contributes to teaching, including the 'Research Methods and Statistics in Psychology II' course. Her lab is part of the Centre for Brain and Behaviour, emphasizing interdisciplinary collaboration in understanding human perception systems.

Vincent FuscoView profile

Professor

• Antenna Technology
• Wireless Communication Systems
• Electromagnetic Materials
• +9 more

Professor Vincent Fusco holds the position of Professor of High Frequency Electronics at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, and serves as CTO of the Institute of Electronics, Communications & Information Technology (ECIT). His research focuses on advanced wireless technologies, including active antenna systems, self-tracking antennas, and synthetic electromagnetic materials for space and medical applications. He has pioneered work in multimode antenna arrays and low-cost phased arrays. He leads high-impact industry collaborations in autonomous vehicles, IoT, satellite communications, and healthcare imaging. Notable awards include the IET Mountbatten Medal and IEEE Fellow status. Supervised students include Anil Chepala (PhD 2016-2019) and Grzegorz Wolosinski (PhD 2018-2018), with alumni such as Prof. Sandeep Sancheti. Research projects include GIGARADIO (short-range gigabit wireless) and New Technologies for Wireless Power Transfer. Key achievements include founding the field of active antenna technology and establishing Queen's wireless lab. His work spans over 689 publications and 13 major research projects, with active grants in mmWave antennas and 5G/6G systems.

• Big data
• Data science
• Data analytics
• +12 more

Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)

Pardhasaradhi BethiView profile

Research Fellow

• Machine Learning
• Deep Learning
• Signal Processing
• +7 more

Pardhasaradhi Bethi is a Research Fellow at the Department of Information and Communication Technology , University of Agder , specializing in lightweight deep learning models for edge computing and radar signal processing. Focus areas include object classification, sound recognition, and drone surveillance Collaborates with colleagues like Linga Reddy Cenkeramaddi and Srihari Pathipati Research Interests encompass edge AI , radar-communication convergence , and real-time IoT integration . His work emphasizes optimizing neural networks for low-power devices and improving tracking frameworks. Publication Trends show a strong focus on automotive radar , drone detection , and human activity analysis using deep learning techniques.

Kok-Leong OngView profile

Professor

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +5 more

Kok-Leong Ong is a Professor of Business Analytics at RMIT University's College of Business & Law, where he serves as Director of the CoBL Technology Initiative, Director of the Enterprise AI and Data Analytics Hub, and Head of the Department of Information Systems and Business Analytics. With over $1.4 million in research grants, he specializes in translating analytics and machine learning into practical business applications across multiple verticals including e-Commerce, public health, sports, urban studies, marketing, and learning. His research has consistently ranked in the top 25% and 5% of works in their domains according to Altmetric. Professor Ong's research spans Business Analytics, Artificial Intelligence, Machine Learning, and Information Systems, with a focus on making data actionable through analytics-2-business translation, automation, and applications. His work addresses critical challenges in cybersecurity for wearable health devices, ECG-based authentication systems, federated learning privacy, carbon accounting for maritime transport, and AI-driven solutions for vehicle damage detection. He has developed frameworks for operationalizing analytics in business contexts and has made significant contributions to mHealth applications for infant care and breastfeeding support. Among his notable scientific achievements, Professor Ong has received two VC's Teaching Awards and was named one of Australia's Leading Data Academics by CDO Magazine in 2021. He has secured over $1.4 million in research funding and serves on prestigious conferences including KDD and PAKDD. His research has been recognized for its high impact, with many works ranking in the top percentiles of their respective fields. Professor Ong actively supervises numerous research students across diverse topics including human-aligned AI, securing LLMs for financial applications, cyber risks in enterprise AI systems, ECG authentication security, and AI transformation for SMEs. He previously played a key role in establishing Australia's first Business Analytics degree and led La Trobe Business School's analytics program from 2015 to 2019 before joining RMIT in September 2021. Through the Enterprise AI and Data Analytics Hub and his role with RMIT University's Digital3 board, Professor Ong leads initiatives focused on bridging the gap between advanced analytics capabilities and business value creation. His work emphasizes practical implementation of AI and analytics solutions that address real-world challenges across multiple industry sectors.

Weihua ZhouView profile

Associate Professor

• Health Informatics
• Medical Image Analysis
• Machine Learning
• +3 more

Weihua Zhou is a Tenured Associate Professor at Michigan Technological University in the College of Computing, with affiliations in Applied Computing, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, and Mathematical Sciences. He holds a PhD from Southern Illinois University Carbondale, an MS and B.Eng. from Wuhan University, and completed postdoctoral research at Emory University. Academic Positions : Tenured Associate Professor (2025–present), Tenure-Track Assistant Professor (2019–2025) at Michigan Tech; Nina Bell Suggs Endowed Professorship (2015–2019) at the University of Southern Mississippi. Research Interests : Focuses on medical imaging and health informatics , particularly machine learning applications in cardiovascular diagnosis , osteoporosis risk stratification , and senile dementia early detection . Additional work includes radiomics for COVID-19 severity assessment , federated learning in medical segmentation , and deep learning for proximal femur strength prediction . Scientific Awards : USM Nina Bell Suggs Endowed Professorship, USM College of Arts and Sciences Scholarly Research Award, American Heart Association Research Leaders Academy (2017, 2018), USM Butch Oustalet Distinguished Professorship Research Award. Lab & Tools : Leads the Medical Imaging & Informatics Lab (MIILab-MTU) and contributes to the NSF/MRI GPU Cluster. Open-sourced tools include KD4COVID19 for radiomics analysis and ECGTools for ECG classification.

Jan AlexanderssonView profile

Researcher

• Artificial Intelligence
• Affective Computing
• Natural Language Processing
• +4 more

Jan Alexandersson is a Researcher at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany, where he leads work in the Cognitive Assistance Systems group. His research bridges artificial intelligence and healthcare, focusing on developing multimodal systems for therapeutic applications and medical diagnostics. His core research interests include Affective Computing , Natural Language Processing , and Human-Computer Interaction , with specialization in emotion recognition, motivational interviewing analysis, and engagement estimation. Current projects explore AI applications for mental health support, pediatric therapy, and medical diagnostics through interdisciplinary collaborations. Recent publications reveal a strong trend toward multimodal AI architectures integrating linguistic, visual, and behavioral data. His 2024 work demonstrates increasing adoption of large language models for clinical applications, particularly in emotion regulation strategy identification and therapeutic dialogue analysis across diverse healthcare contexts. Dr. Alexandersson actively contributes to major initiatives including MULTI-IMMERSE (virtual reality therapy for hospitalized children), GAIN (Georgian AI networking), AI@Home (elderly care risk prediction), Kitatta (corneal transplant quality assessment), and MePheSTO (digital phenotyping for psychiatric disorders). These projects collectively focus on translating AI research into practical healthcare solutions through sensor integration, behavioral modeling, and clinical validation.

• Computer Science
• Machine Learning
• Image Processing
• +4 more

Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.

Mitchell PryorView profile

Research Professor

• Robotics
• Automation
• Nuclear Engineering
• +4 more

Mitchell Pryor is a Research Professor in the Department of Nuclear and Radiation Engineering at the University of Texas at Austin. He specializes in applied robotics and automation, particularly in hazardous environments such as nuclear facilities, energy sectors, and defense applications. Pryor earned his BSME from Southern Methodist University (1993), followed by an MS (1999) and PhD (2002) in Mechanical Engineering from UT Austin. His research focuses on robotics for hazardous environments, including nuclear waste management, autonomous systems, and human-robot collaboration. He leads the Nuclear & Applied Robotics Group (NRG) and co-founded RAPID, an industry affiliate program supporting automation in energy sectors. His work spans robotics in extreme environments, augmented reality interfaces, and task planning frameworks. Key collaborations include Army Futures Command (AFC), NASA, DARPA, and DOE national labs such as INL, LANL, and ORNL. Pryor’s contributions include developing the TeMoto software framework for robotic autonomy, virtual fixture systems, and radiation surveying methodologies. He chairs the ANS Robotics & Remote Systems Division and actively participates in IEEE, ASME, and other professional societies. His research emphasizes interdisciplinary approaches to deploy robotics in complex, uncertain environments for tasks like material handling, mobile manipulation, and environmental monitoring. Notable projects include AR-STAR for real-time robot data modification, MaRMOT for object tracking, and TeMoto 2.0 for task execution formalisms. His work bridges theoretical robotics with practical applications in nuclear safety, energy infrastructure, and defense systems.

Michael W. JenkinsView profile

Professor

• Biomedical Engineering
• Medical Imaging
• Cardiac Imaging
• +3 more

Michael W. Jenkins is a Professor of Biomedical Engineering at the Case Western Reserve University School of Medicine and a member of the Cancer Imaging Program at the Case Comprehensive Cancer Center. His research focuses on developing biomedical optics tools for studying congenital heart disease and peripheral nervous system disorders. Key techniques include optical coherence tomography (OCT), light-sheet microscopy, and infrared neuromodulation. The Jenkins Lab specializes in advancing 3D imaging modalities for real-time tissue analysis, aiming to reduce surgical delays and improve diagnostic accuracy. Research interests include rapid 3D tissue visualization to replace traditional frozen-section pathology, optical pacing of cardiac tissues, and corneal nerve imaging. His work bridges engineering and medicine, with applications in ophthalmology, cardiology, and neurology. Recent innovations include label-free microscopy techniques (e.g., MUSE imaging) and AI-driven segmentation tools for neural anatomy analysis. Publications highlight advancements in corneal crosslinking assessment, vagus nerve microanatomy characterization, and cardiac tissue imaging. The lab collaborates with industry to translate optical tools into clinical settings, emphasizing precision and real-time diagnostics. Teaching and mentorship are integral to his role, fostering interdisciplinary training in biomedical optics. Ongoing projects address unmet clinical needs in neural modulation therapies and regenerative medicine through optical platforms.

Avideh ZakhorView profile

Professor

• Signal Processing
• Artificial Intelligence
• 3D Computer Vision
• +6 more

Dr. Avideh Zakhor is a Professor and Qualcomm Chair at the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She is affiliated with several research centers including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive Initiative, Video and Image Processing Lab, and Berkeley Center for New Media (BCNM). Her career spans over three decades with significant contributions to signal processing, 3D computer vision, robotics, and deep learning. 1983 B.Sc. in Electrical Engineering from Caltech 1985 S.M. in Electrical Engineering and Computer Science from MIT 1987 Ph.D. in Electrical Engineering and Computer Science from MIT Dr. Zakhor's research interests focus on 3D computer vision, autonomous systems and robotics, deep learning, and signal/image processing. Her work spans diverse areas including drone navigation, medical imaging analysis, indoor positioning, and building energy audits. She has led projects on drone-based 3D building reconstruction, legged robot locomotion, and melanoma detection using AI. The 15 most recent publications highlight her work in several key areas: person search pre-training techniques, drone-based indoor navigation and 3D modeling, medical image segmentation for melanoma detection, hexapod robot locomotion, and proximity detection for public health. These publications demonstrate her expertise at the intersection of computer vision, robotics, and AI applications. Dr. Zakhor has received numerous prestigious awards throughout her career: 2022 Winner of Phases 1 and 2, Department of Energy E-Robot Competition 2018 Electronic Imaging Scientist of the Year by SPIE 2004 Okawa Research Grant 2002 IEEE Fellow 1992 Office of Naval Research Young Investigator Award 1990 Presidential Young Investigator (PYI) Award from President George H.W. Bush 1990 Junior Faculty Development Award 1984-1988 Hertz Fellowship 1983 Henry Ford Engineering Award 1982-1983 General Motors Scholarship Dr. Zakhor has advised numerous research projects and has been involved in significant research grants. She has founded successful companies including Indoor Reality, which develops technologies for rapid 3D mapping and visualization of buildings and assets. She leads research initiatives in various cutting-edge technologies: Unmanned Aerial Vehicles (UAV) with focus on autonomy and obstacle avoidance, perception, path planning and control Deep learning applications in legged locomotion, unsupervised learning for multimodal sensors, misinformation detection, and learning-based image compression Wi-Fi proximity detection methods for contact tracing of diseases Temporal graphical neural networks for disease prediction Detection of small objects in ultra-high resolution images 3D reconstruction and recognition

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +4 more

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .