جست‌وجوی استاد و پژوهشگر

Yingzi Linمشاهده پروفایل

استاد

• Human-Machine Systems
• Biosensing
• Transportation Safety
• +۳ مورد دیگر

Dr. Yingzi Lin is a Professor and Chair of Mechanical and Industrial Engineering at Northeastern University, Boston, MA. She directs the Intelligent Human-Machine Systems (IHMS) Laboratory and specializes in human-machine systems, biosensing, and human factors in healthcare and transportation safety. Her research is funded by NSF, NIH, NIST, and industry partners like GM and Bose. Education: PhD (2004), Mechanical Engineering from the University of Saskatchewan. Research Interests: Includes human-robot interaction, driver-vehicle systems, patient safety, and multimodal pain assessment. She develops technologies like the COMPASS system for objective pain measurement and cognition-driven navigation tools for firefighters. Key Grants: Principal Investigator for NSF-funded projects on pain assessment and Co-PI for NIH-funded VR-based stroke recovery studies. Collaborates on NIST initiatives for firefighter spatial systems. Awards: NSF CAREER Award (2010), NSERC UFA (2004), IEEE Computational Intelligence Society Outstanding Paper Award (2013), and 2023 Excellence in Mentoring Award. Labs & Teams: Leads the IHMS Lab, focuses on human-technology integration and robotics. Engages in interdisciplinary collaborations through Northeastern's Institute for Experiential AI and Experiential Engineering Education.

• Virtual Reality (VR)
• Human-Computer Interaction (HCI)
• Accessibility in Technology
• +۳ مورد دیگر

John Quarles is a Professor at the University of Texas at San Antonio, specializing in Virtual Reality (VR) and Human-Computer Interaction. His research focuses on accessibility in immersive technologies, cybersickness mitigation, and inclusive design. He has contributed over 100 publications across top venues like IEEE VR, ISMAR, and IEEE Transactions on Visualization and Computer Graphics. His work addresses challenges faced by users with disabilities, such as balance impairments and mobility limitations, through innovative feedback systems and adaptive algorithms. Quarles has co-authored influential papers on cybersickness prediction, VR accessibility for persons with Multiple Sclerosis, and disability simulations to reduce societal bias. He has held leadership roles including Program Chair for IEEE VR 2023, demonstrating his influence in academic and industrial VR communities. His research spans interdisciplinary applications in healthcare, education, and rehabilitation, with notable collaborations on datasets like 'Mazed and Confused' and frameworks like SmoothRide. Key themes include multimodal feedback methods, user-centric design principles, and leveraging AI for personalized VR experiences.

Daniel Pizarro Pérezمشاهده پروفایل

استاد

• Computer Vision
• Medical Imaging
• Smart Grids
• +۳ مورد دیگر

Dr. Daniel Pizarro Pérez is a Professor in the Department of Electronics at the University of Alcalá, Spain. He is a member of the GEINTRA research group, which focuses on Electronic Engineering applied to Intelligent Spaces and Transport. He holds a doctoral degree from the University of Alcalá, completing his thesis on 'Localización de robots móviles en espacios inteligentes utilizando cámaras externas y marcas naturales' (2008), supervised by Dr. Manuel Ramón Mazo Quintas and Dr. Enrique Santiso Gómez. His research interests span computer vision, medical imaging, smart grid technologies, acoustic signal processing, and control systems. Notable projects include augmented reality applications in laparoscopic surgery, non-intrusive load monitoring using smart meters, and advanced control methodologies for power electronics. His work bridges theoretical advancements with practical applications in healthcare, energy systems, and robotics. Recent publications highlight contributions to neural radiance fields for minimally-invasive surgery, distributed acoustic sensing in submarine environments, and deep learning-based activity recognition via energy consumption. His research frequently integrates interdisciplinary approaches, such as combining computer vision with medical robotics and leveraging machine learning for real-time control systems. Dr. Pizarro’s work has been supported by grants and collaborations within the GEINTRA group, with applications in intelligent spaces, robotic navigation, and sensor fusion. He has also contributed to educational initiatives like the GEMS Erasmus+ project, emphasizing sensory module development for robotics education.

Tuomas Virtanenمشاهده پروفایل

استاد

• Audio signal processing
• Machine listening
• Sound source separation
• +۵ مورد دیگر

Tuomas Virtanen is a Professor at Tampere University's Signal Processing Research Centre within the Faculty of Information Technology and Communication Sciences. His primary affiliation is the Computing Sciences department, where he leads the Audio Research Group . His work focuses on computational analysis of audio signals, machine listening, and acoustic scene understanding. Research Interests: Audio signal processing, content analysis of audio, sound source separation, acoustic scene classification, speech processing (including noise-robust ASR and speaker recognition), and machine learning techniques such as deep neural networks and statistical modeling. His group develops methods for sound event detection, localization, and tracking in complex acoustic environments. Key Contributions: Leading the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges, developing open-source datasets like TUT Acoustic Scenes and STARSS, and advancing techniques in audio captioning, privacy-preserving audio processing, and multimodal learning. Notable Projects: Creation of the Audio Research Group , development of the Clotho audio captioning dataset, and pioneering work in zero-shot audio classification using semantic embeddings. His research bridges signal processing fundamentals with modern machine learning paradigms.

• Artificial Intelligence
• Robotics
• Computer Vision
• +۴ مورد دیگر

Hulya Yalcin is an Assistant Professor in the Department of Mechanical Engineering at Istanbul Technical University. Her research focuses on artificial intelligence applications in robotics, computer vision, and precision agriculture. She leads projects in musculoskeletal modeling, plant phenology monitoring, and assistive technologies for elderly care. Her work contributes to UN Sustainable Development Goals related to innovation, health, and sustainable agriculture. Key projects include using deep learning for crop yield estimation and developing exergaming systems to improve elderly health. She has authored 52 research outputs and secured funding for initiatives like AISENSE (EU-funded exergames) and plant classification via computer vision. Publications span robotics control, medical engineering, and agricultural informatics. Notable contributions include knee orthosis gait learning via deep reinforcement learning and low-resource Turkish speech recognition improvements. As Principal Investigator, she manages projects on plant classification using CNNs, drone-based depth mapping, and multimodal assisted living systems. Her research bridges AI with practical applications in healthcare, agriculture, and robotics.

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis and Artificial Intelligence
• +۳ مورد دیگر

Dr. Jungeun (Jenny) Won is an Assistant Professor in the Department of Biomedical Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. Her research focuses on optical imaging, biomedical device instrumentation, medical image analysis with artificial intelligence, and optical coherence tomography (OCT). She leads the Translational Biophotonics Laboratory, advancing non-invasive diagnostic tools for ophthalmology and infectious diseases. Her work combines cutting-edge imaging techniques with clinical applications, such as analyzing retinal pathologies and middle ear biofilms. Dr. Won’s research emphasizes high-resolution OCT advancements for studying age-related macular degeneration (AMD), diabetic retinopathy, and biofilm dynamics. Her innovations include automated motion correction algorithms, variable interscan time analysis (VISTA), and multimodal microscopy platforms. These tools enhance diagnostic precision and enable longitudinal tracking of disease progression. Her articles highlight trends in OCT-based vascular imaging, capillary blood flow quantification, and therapeutic interventions for otitis media. She explores interdisciplinary solutions at the intersection of engineering and medicine, contributing to both fundamental science and clinical translation. Her lab’s work is disseminated through top-tier journals and has implications for improving patient care in ophthalmology and infectious disease management.

Matthew Stoneمشاهده پروفایل

استاد

• Natural Language Generation
• Computational Semantics
• Knowledge Representation
• +۵ مورد دیگر

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
• +۷ مورد دیگر

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)
• +۳ مورد دیگر

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 Lavanمشاهده پروفایل

مدرس

• Voice Perception
• Identity Perception
• Person Perception
• +۳ مورد دیگر

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.

• Big data
• Data science
• Data analytics
• +۱۲ مورد دیگر

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 Bethiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Signal Processing
• +۷ مورد دیگر

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 Ongمشاهده پروفایل

استاد

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

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 Zhouمشاهده پروفایل

دانشیار

• Health Informatics
• Medical Image Analysis
• Machine Learning
• +۳ مورد دیگر

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 Alexanderssonمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Affective Computing
• Natural Language Processing
• +۴ مورد دیگر

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.