جست‌وجوی استاد و پژوهشگر

Shrikanth Narayananمشاهده پروفایل

استاد

• Human-centered signal processing
• Machine intelligence
• Affective computing
• +۱۰ مورد دیگر

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC), with appointments spanning Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery. He serves as Research Director of the Information Sciences Institute and Director of the Ming Hsieh Institute. PhD in Electrical Engineering (UCLA, 1995) Engineer and MS in Electrical Engineering (UCLA, 1992 and 1990) BE in Electrical Engineering (Anna University, India, 1988) His interdisciplinary research focuses on human-centered signal processing and machine intelligence , addressing societal challenges in health, education, defense, and media arts. Key areas include: Behavioral signal processing Affective computing Multimodal signal processing Computational speech science Biomedical applications Scientific Awards : IEEE James L. Flanagan Speech and Audio Processing Award (2025) Edward J. McCluskey Technical Achievement Award (2024) ISCA Medal for Scientific Achievement (2023) Claude Shannon-Harry Nyquist Technical Achievement Award (2023) ACM ICMI Sustained Accomplishment Award (2020) USC Distinguished Faculty Service Award With over 1,000 publications and 19 patents , his work has been commercialized through startups like Behavioral Signals Technologies and Lyssn . He leads transformative university initiatives and has served in editorial roles for top journals including Computer Speech and Language and IEEE Transactions on Affective Computing .

Thomas Hermannمشاهده پروفایل

پژوهشگر

• Auditory Data Science
• Interactive Sonification
• Smart Environments
• +۵ مورد دیگر

Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.

Federico Visiمشاهده پروفایل

استاد مهمان

• gesture in music
• motion-sensing technologies
• interactive machine learning
• +۴ مورد دیگر

Federico Visi (he/they) serves as Guest Faculty in the Sound Studies and Sonic Arts Master's program at Berlin University of the Arts. Based in Berlin, Germany, Visi is a researcher, composer, and performer whose work bridges technology and artistic expression through innovative musical interfaces and performance systems. Visi completed doctoral research on instrumental music and body movement at the Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, UK. They have held postdoctoral positions at Luleå University of Technology (Sweden) and Goldsmiths, University of London (UK), establishing a foundation for their interdisciplinary approach to music technology. Visi's research focuses on gesture in music, motion-sensing technologies, interactive machine learning, and embodied interaction. Their work explores how body movement and physiological signals can be harnessed in electronic music creation through both theoretical frameworks and practical implementations. They have developed novel instruments like the Sophtar and biosignal-based performance systems that translate physiological data into sound. Recent scholarly output reveals a trajectory toward increasingly sophisticated integration of machine learning in musical contexts, with significant contributions spanning computer music, psychology, and human-computer interaction domains. Visi's publications demonstrate how algorithmic processes can be embedded in musical instruments to create new forms of musical expression and co-creativity between humans and machines. As an active member of the research community, Visi co-edited a thematic issue of Organised Sound on 'Embedding Algorithms in Music and Sound Art' and organized the Embedding Algorithms Workshop at Berlin University of the Arts. They contribute to the Wilding AI collective that explores creative applications of AI in spatial audio environments. Visi releases music under the moniker AQAXA, combining conventional electronic music production with machine learning exploration of personal sonic memories. Their work has been supported by the European Research Council, Swedish Research Council, Boström Fund, and Helge Ax:son Johnsons foundation.

Maria Schweigelمشاهده پروفایل

استاد

• Artificial Intelligence Algorithms
• Optimization Techniques
• Software Development
• +۴ مورد دیگر

Professor Maria Schweigel Prof. Maria Schweigel is a Professor at the Department of Autonomous Systems, Schmalkalden University of Applied Sciences. Her teaching focuses on automation control, electronic control systems, robotics, and embedded systems. She leads the research group 'Eingebettete Diagnosesysteme' (Embedded Diagnostic Systems) and specializes in artificial intelligence algorithms, optimization using genetic and evolutionary methods, and biosignal analysis for anesthesia and sleep studies. Her research spans robotics, embedded systems, image processing, and software development with tools like C/C++, MATLAB/Simulink, and LabView. Key projects include real-time EEG classification for anesthesia monitoring, low-SNR biomedical signal extraction, and SVM-based classification on resource-constrained platforms. She has contributed to international conferences like ECT and IWK TU Ilmenau, focusing on interdisciplinary applications in medical technology and autonomous systems. No scientific awards were explicitly listed in the provided materials. Her advising and grants are not detailed here, but her extensive publication record (2009–2016) demonstrates active involvement in collaborative research with colleagues like Wenzel, Walther, and Baumgart-Schmitt. She co-developed tools such as the Multi-EEG-Viewer and explored wireless biomedical signal transmission for relaxation control systems.

• Computer Science
• Machine Learning
• IoT
• +۷ مورد دیگر

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

• Low-Voltage Analog Circuits
• Biomedical Signal Processing
• Operational Transconductance Amplifiers
• +۴ مورد دیگر

Fabian Khateb is a prominent researcher in low-voltage analog circuit design, with over 100 publications indexed in DBLP from 2010 to 2025. He collaborates extensively with Montree Kumngern and Tomasz Kulej, focusing on operational transconductance amplifiers (OTAs), current conveyors, and their applications in biomedical signal processing. His work emphasizes ultra-low power consumption (nanowatt to microwatt ranges) and sub-1V operation for portable/wearable health devices. Research Interests: Low-voltage analog IC design Biomedical signal conditioning Current-mode circuits OTA-based filters and oscillators CMOS technology optimization Publication Trends: His recent IEEE Access papers (2024-2025) explore mixed-mode filters, shadow oscillators, and bilinear filter designs. Earlier works in Circuits Syst. Signal Process. and Sensors address rail-to-rail amplifiers, rectifiers, and memristor emulators for adaptive systems.

• Blockchain Technology
• Edge Computing
• Federated Learning
• +۴ مورد دیگر

Pubudu N. Pathirana is a Professor at the University of Melbourne's College of Engineering, specializing in Blockchain Technology, Edge Computing, Federated Learning, and Machine Learning for Healthcare . His work bridges theoretical advancements with practical applications in smart cities, cyber-physical systems, and medical diagnostics. Education : PhD in Electrical Engineering, Monash University His research focuses on privacy-preserving machine learning and blockchain integration with edge networks , demonstrated through collaborations with Dinh C. Nguyen, Minh K. Quan, and Bipasha Kashyap. Key contributions include quantum-enhanced models for acoustic classification and federated learning frameworks for IoT environments. Recent publications (2025) highlight advancements in spatio-temporal traffic modeling , adaptive privacy clipping , and secure client aggregation . Earlier work (2021-2024) explored applications in Covid-19 detection , Friedreich Ataxia severity classification , and entropy-based cerebellar ataxia analysis . He mentors students like Ali Reza Sattarzadeh and Kanishka Ranaweera, with grants supporting projects on blockchain-enhanced federated learning and quantum-inspired edge computing . His lab collaborates with institutions such as Monash University and the Florey Institute of Neuroscience.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Jong-Hwan Kim is a Professor at KAIST's College of Engineering, specialized in robotics and artificial intelligence. His research focuses on neural networks, autonomous systems, computer vision, and human-robot interaction. He has published extensively in top-tier conferences like CVPR, ICRA, and IEEE Transactions. Key contributions include work on developmental learning networks, episodic memory models, and AI applications in robotics and healthcare. He co-chaired the RiTA conference series and has collaborated with institutions globally. His research bridges theoretical advancements with practical applications in robotics, medical diagnostics, and multimodal AI. Affiliations: KAIST (main), Seoul National University (PhD 1987) Research Highlights: Visual odometry, gesture recognition, emotion modeling, and robotics task intelligence Recent projects include text recognition via finger movement, Alzheimer's disease classification using EEG-FNIRS fusion, and multimodal emotion recognition systems. His work emphasizes interdisciplinary approaches, integrating robotics, computer vision, and cognitive science.

Ralf Seepoldمشاهده پروفایل

استاد

• Biomedical Engineering
• Biosignal Processing & Analytics
• Sleep Disorders and Sleep Medicine
• +۷ مورد دیگر

Prof. Ralf Seepold is a Professor in the Faculty of Computer Science at Konstanz University of Applied Sciences, leading the Professorship of Ubiquitous Computing. He serves as the contact person for the Automotive and Information Technology degree programs and the newly established Intelligent Mobility Systems program since 2022. His primary affiliations include the Department of Ubiquitous Computing, focusing on interdisciplinary research in healthcare technology and smart systems. Prof. Seepold's research expertise spans biomedical engineering, biosignal processing (ECG, ballistocardiogram), sleep medicine (sleep disorders, sleep apnea detection), telemedicine, and ambient assisted living solutions. His work integrates wearable sensors, machine learning, and IoT technologies to develop non-invasive healthcare systems for home and clinical environments. Notable contributions include: Development of smart sleep monitoring systems using accelerometer-based sensors AI-driven diagnostics for sleep disorders and stress analysis Hybrid energy harvesting systems for medical wearables Conceptual frameworks for E-health platforms and digital CBT-I tools His academic memberships include IEEE Senior Member (Engineering in Medicine and Biology Society), German Society for Sleep Research & Medicine (DGSM), and Smart Home & Living Baden-Württemberg.

Stefan Endlerمشاهده پروفایل

پژوهشگر

• Sports Informatics
• Modeling and Simulations
• Software Engineering
• +۳ مورد دیگر

Dr. Stefan Endler is an Academic Councillor at the Institute of Computer Science, Johannes Gutenberg University Mainz, where he has held various roles including Research Associate and Academic Council member since 2006. His work bridges computer science and sports science, focusing on sports informatics, modeling/simulations for training impact analysis, and software engineering (design patterns). Educational Background: Bachelor of Science (2006): Model-based training planning with GPS data at Johannes Gutenberg University Mainz Bachelor of Science (2008): Attacker models and attack detection in mobile ad-hoc networks at TU Darmstadt Doctorate (2013): Adaptation of the PerPot metamodel for endurance-oriented running optimization at Mainz University Research Interests: Development of simulation models for athletic performance optimization Application of design patterns in software engineering for sports technology Integration of sensor data (e.g., smartwatches) for real-time training analysis Teaching Contributions: Extensive teaching experience across 15+ years, including courses on software development, design patterns, and computer engineering. Notable roles include lecture leadership for Design Patterns (2014–2021) and programming fundamentals. Awards: 2018 Teaching Award from Johannes Gutenberg University recognizing excellence in academic instruction. Advisory Work: Supervised 45+ theses (2014–2020) across computer science and sports science disciplines. Maintained active involvement in thesis supervision and academic council governance. Collaborative Efforts: Cross-disciplinary work with the Institute of Sports Science, contributing to biomechanical studies and training methodology research.

Cornelia Herbertمشاهده پروفایل

استاد

• Affective Neurolinguistics
• Language, Self, Emotion and Motivation
• Physical Activity and Exercise
• +۶ مورد دیگر

Cornelia Herbert is a Full Professor and Head of the Department of Applied Emotion and Motivation Psychology at the Institute of Psychology and Education, Ulm University, Germany. She directs the Sports-Physio-Lab, Brain-Lab, and Mobile-Lab, and serves as Women’s Representative for the Faculty of Engineering, Computer Science and Psychology. Her institutional roles include membership in the Medical Ethics Committee, Committee of Good Scientific Practice, Center for Trauma Research, and supervisory boards for Health Promotion in Students and Education 'Leitbild Lehre' at Ulm University. Her research integrates Affective Neurolinguistics, Language, Self, Emotion and Motivation, Physical Activity and Exercise, and Mental Health and Well-Being, with specialized focus on Cognitive Vulnerability, Depression, Schizophrenia, Body Image, and Eating Disorders. Utilizing advanced methodologies including EEG, fNIRS, fMRI, psychophysiology (ECG, ICG, fEMG), mobile biosignal assessment, and Character Computing, she investigates neural and behavioral mechanisms linking linguistic self-representations, bodily states, and emotional processing to develop interventions for mental health and well-being. Her 2015-2020 publications demonstrate a clear trajectory from foundational affective neurolinguistics toward interdisciplinary integration with computer science, particularly in Character Computing. Key themes include neural correlates of emotion-language interactions, computational modeling of psychological profiles, and cross-cultural affective processing. Her work spans experimental psychology, clinical applications, and computational approaches, with increasing emphasis on translating theoretical insights into health-promoting technologies and behavioral interventions. Professor Herbert leads multiple externally funded projects including DFG grants on cognitive vulnerability and the bodily self, NSC-funded 'Social Grammar', DAAD/BMBF collaborations on Cross-Cultural Character Computing, and industry partnerships like 'Oral Health and Behavior Regulation'. Her applied research program features RCT studies (Fit & Well), population-level physical activity investigations (E-Move), and community initiatives including regular workshops for the elderly and student health checks through Open-Lab-Days. She maintains three specialized laboratories supporting her research ecosystem: the Sports-Physio-Lab for movement analysis, Brain-Lab for neuroimaging studies, and Mobile-Lab for field-based biosignal assessment. These facilities enable investigations into neural markers of self-paced movement, emotion regulation during exercise, and real-world applications of her theoretical frameworks across clinical and healthy populations.

• Internet of Things
• Assisted Living Environments
• Smart Sensors
• +۱۲ مورد دیگر

Mariana Catela Jacob Rodrigues is an Assistant Professor at Iscte – University Institute of Lisbon, affiliated with the Department of Applied Digital Technologies and the Institute of Telecommunications - IUL, where she conducts research in the Instrumentation and Measurements Group. She holds a PhD in Information Technologies and Systems from ISCTE, completed in 2024, following a Master’s and Bachelor’s in Telecommunications and Computer Engineering at the same institution. Her research interests include: Internet of Things (IoT) Ambient Assisted Living (AAL) Smart Sensors and Wearable Technologies Cardiorespiratory and Environmental Monitoring Artificial Intelligence in Healthcare Virtual Reality for Rehabilitation Her scientific work focuses on developing intelligent, sensor-driven systems for health and environmental monitoring, particularly in indoor and elderly care settings. She utilizes wireless sensor networks, wearable platforms, and physiological signal analysis (e.g., HRV) to assess autonomic nervous system responses under various stimuli such as music, lighting, and environmental conditions. Her recent publications highlight trends in non-invasive monitoring, fall detection, precision agriculture, and stress assessment using innovative IoT and VR-based tools. Her scientific contributions have been recognized with several awards, including: Best Student Paper Award at IEEE MeMeA 2022 Best Student Paper Award at ISSI 2019 ISTA Top Talent 2018–2019 (awarded in 2020) Mariana Rodrigues actively mentors and collaborates on research projects, frequently co-authoring with senior researchers such as Octavian Postolache and Francisco Cercas. She has served as a member and chair in organizing committees for multiple IEEE-sponsored summer schools and symposia on sensing and IoT, including the Smart Systems for Ambient Assisted Living series and the International Symposium on Sensing and Instrumentation. She is also involved in grant-funded projects related to smart healthcare and environmental monitoring, though specific grants are not detailed in the provided text. She leads key IEEE initiatives at ISCTE, including: Chair of IEEE Women in Engineering (WIE) Affinity Group Chair of IEEE Instrumentation and Measurement Society Chapter

Simon M. Hofmannمشاهده پروفایل

پژوهشگر

• Neuroscience
• Deep Learning
• Cognitive Processes
• +۳ مورد دیگر

Simon M. Hofmann is a Doctoral Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences (Leipzig), affiliated with the Department of Neurology and the International Max Planck Research School NeuroCom. His work focuses on interpretable AI methodologies for neuroscience, recurrent neural networks modeling cognitive processes, and developing ecological research paradigms using naturalistic stimuli like immersive virtual reality. He holds a Master's in Brain and Cognitive Science from the University of Amsterdam and a Bachelor's in Philosophy/Psychology from TU Berlin. Hofmann has conducted visiting research at institutions including the Fraunhofer Heinrich Hertz Institute and the Donders Institute. Beyond academia, he has served as an organizer for the CBS CoCoNUT initiative and held roles in film festivals such as Berlinale. His research emphasizes explainable AI tools like the xai4mri Python toolbox and investigates brain-heart interactions in virtual environments. Education: 2015-2018: Research Master in Brain and Cognitive Science (University of Amsterdam) 2009-2013: Bachelor in Philosophy/Psychology (TU Berlin/Humboldt University) Key Research Groups: Methods and Development Group: Neural Data Science & Statistical Computing Research Group: Mind-Body-Emotion (Dept. of Neurology) His research interests span cognitive processes in complex environments, deep learning model interpretability, and the development of naturalistic experimental paradigms integrating multisensory stimuli. Hofmann's contributions include advancing EEG/fMRI integration techniques and studying emotional arousal through physiological signals in immersive VR settings. Publications highlight his work on explainable AI for neuroimaging, brain-age predictions via deep learning, and decoding neural correlates of emotional experiences. He actively promotes open science practices through tool development and interdisciplinary collaboration. Labs/Teams: Involved in the Methods and Development Group focusing on neural data science and statistical computing, with cross-group collaborations in computational neuroscience and neuroimaging.

Jan Eric Lenssenمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۴ مورد دیگر

Jan Eric Lenssen is a Senior Researcher at the Max Planck Institute for Informatics leading the Geometric Representation Learning Group , and a Founding Engineer at Kumo.ai . He holds a Dr.-Ing. in Computer Science from TU Dortmund University (2022) and has conducted research internships at Facebook Reality Labs and Nnaisense. Affiliations: ELLIS Unit Faculty Member and Saarland Informatics Campus Fellow Education: PhD under Prof. Heinrich Müller, TU Dortmund (2022) His research focuses on differentiable algorithms , neural fields , and graph-based methods for 3D perception and generative modeling. Key contributions include frameworks like SplineCNN , PyTorch Geometric , and methods for scalable 3D reconstruction, human-object interaction modeling, and relational deep learning. Publications span top venues ( CVPR, ECCV, ICCV, NeurIPS ) with highlights including Best Paper Honorable Mention (ECCV 2022) and Best Paper Award (BIOSIGNALS 2018) . He co-founded the RelBench benchmark for relational database learning and leads development of geometric deep learning tools. Key Labs/Teams: Geometric Representation Learning Group (MPI), Kumo.ai Research Team, and the Computer Vision and Machine Learning department at MPI.

• Rehabilitation Engineering
• Biomechanics
• Human-Computer Interaction
• +۶ مورد دیگر

Xianta Jiang is a research-focused academic at Simon Fraser University's School of Computing Science, specializing in rehabilitation engineering and biomedical computing. With a prolific publication record spanning from 2006 to 2025, Jiang has established expertise in force myography, prosthetic control systems, and human-computer interaction for medical applications. Jiang's research interests center on biomechanics, wearable sensor technologies, and surgical training systems. Their work bridges computer vision with rehabilitation engineering, particularly in facial image processing applications for medical contexts. Recent publications demonstrate a strong focus on diffusion models and generative AI techniques applied to medical image restoration and prosthetic control. The research trajectory shows a clear evolution from fundamental biomechanics research (early work on ankle joint power estimation and gait analysis) to more complex applications involving AI-driven prosthetic control and facial image processing. The 2023-2025 publications reveal a strategic pivot toward leveraging advanced computer vision techniques for medical applications. Jiang collaborates extensively with M. Stella Atkins, Bin Zheng, and Carlo Menon, forming a core research group at Simon Fraser University focused on biomedical computing applications. This collaborative network spans multiple publications in high-impact venues including IEEE Transactions, Sensors, and Frontiers journals.