Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .
Adrian Vulpe-Grigorasi is a Junior Researcher at the Center for Digital Health and Social Innovation , affiliated with the Institute of Health Sciences at FH Steyr (FHSTP.ac.at). He holds BEng and MEng degrees and specializes in interdisciplinary research at the intersection of machine learning, cognitive science, and biomedical engineering. His research focuses on: Cognitive load assessment using VR systems and biosensors Development of multimodal machine learning frameworks for health monitoring Applications of GANs in ECG analysis and synthetic data generation Energy systems optimization through data-driven approaches Key projects include: Realistic clinical XR training systems Attention performance classification via eye tracking Smart grid forecasting with GAN data augmentation Adrian has published in conferences such as IEEE Informatics, CGI, and ACM MUM. His work bridges theoretical machine learning advancements with practical healthcare and energy applications.
Professor Ilias Maglogiannis is a leading academic in Computational Biomedicine at the University of Piraeus , directing its Computational Biomedicine Laboratory. He holds a PhD from the National Technical University of Athens and has held faculty positions at the University of the Aegean and University of Thessaly before joining the University of Piraeus in 2013. He has served as Dean and Department Chair, leading large-scale EU projects like AI4WORK and MELIORA . His research focuses on AI in Healthcare , including medical imaging, wearable devices, and telemedicine systems. He has published over 400 papers (h-index 46), three books, and serves on editorial boards of journals like IEEE JBHI and Personal and Ubiquitous Computing . Key Awards: Fellow of EAMBES, Senior IEEE Member Leadership: IFIP WG12.5 President (AI Applications) Current Projects: MedSecurance (IoMT security), E-Prevention (mental health monitoring) His teaching includes courses on Pattern Recognition , Telemedicine , and Digital Image Processing . He actively promotes AI ethics and human-centric digital twin technologies in healthcare and education sectors.
Kimiko Ryokai is an Assistant Professor at the UC Berkeley School of Information and the Berkeley Center for New Media. Her research focuses on tangible embodied computing and human-computer interaction (HCI), particularly in educational technology, mental health, and creativity support systems. She has been funded by NSF, Google, and Nokia, and her work has been published in top venues like CHI, SIGGRAPH, and CSCL. Ryokai holds MS and PhD degrees from MIT’s Media Arts & Sciences program and previously worked at IDEO as an interaction designer. Education: M.S. (1999), Ph.D. (2005) in Media Arts & Sciences, MIT Grants: Over $600,000 in funding from NSF, Google, and Nokia for projects like mobile AR learning tools and laughter analysis Research Interests Ryokai investigates how physical and digital systems can enhance learning, creativity, and social interaction. Key projects include: MathMarks : AR tool for math education through environmental exploration GreenHat : Exploring nature via expert-guided AR EnergyBugs : Wearables for children to learn energy harvesting Recent Contributions Recent work emphasizes embodied learning (e.g., Balance Board Math), affective computing (laughter visualization), and tangible interaction design. Her research bridges theory and practice, often involving interdisciplinary teams across education, engineering, and art. Awards & Recognition Best Paper Honorable Mention (ACM CHI 2013, 2011) IDSA Gold Award for Industrial Design (2005) Distinguished Mentor Award (UC Berkeley 2010) Teaching & Mentorship Ryokai teaches courses like Design of Tangible User Interfaces and has advised over 30 graduate students. Notable advisees include Laura Devendorf (PhD 2014) and Daniela Rosner (PhD 2012, now at University of Washington).
Yuling Yan serves as Professor and Founding Chair of the Department of Bioengineering at Santa Clara University's School of Engineering, holding the Phil and Bobbie Sanfilippo Professorship. She maintains an affiliation as Consulting Faculty at Stanford University's Department of Otolaryngology. Her academic journey includes faculty positions at the University of Hawaii-Manoa (2002-2005), University of Wisconsin-Madison, and University of the Ryukyus (1997-1999), with postdoctoral training at McGill University and Max Planck Institute for Biochemistry. Professor Yan's research focuses on AI-driven medical diagnostics, specializing in machine learning applications for early disease detection including vocal pathologies, cardiac arrhythmia, skin cancer, brain aneurysms, and breast cancer. Her work integrates biomedical imaging (MRA, ultrasound, CT), biosignal analysis, and fluorescence microscopy with molecular switch probes. Current projects involve collaboration with radiologists at Santa Clara Valley Hospital to develop deep learning systems for medical image analysis. Her publication trends reveal an evolution from foundational work in vocal fold dynamics and optical probe development (2011-2015) toward AI/ML applications in medical diagnostics (2017-present), with increasing emphasis on deep neural networks for cancer detection and cardiac monitoring. Key contributions include novel methodologies for high-speed laryngeal imaging analysis and optical lock-in detection microscopy. Active research funding includes NIH Multi-Investigator Awards for high-contrast single-molecule imaging and NSF grants for laryngeal imaging tools development. She teaches graduate courses including Machine Learning for Biomedical Applications and Deep Learning for Medical Image Analysis, alongside undergraduate bioengineering fundamentals.
Peter Washington is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF), affiliated with the School of Medicine and the Division of Clinical Informatics and Digital Transformation (DoC-IT). He leads the UCSF TECH Lab (Technologies Empowering Consumer Health), focusing on human-centered AI for digital health applications. Education: PhD in Bioengineering, Stanford University (2022) MS in Computer Science, Stanford University (2018) BA in Computer Science, Rice University (2015) His research centers on consumer digital health informatics, leveraging data from wearables, smartphones, and specialized health devices to develop digital diagnostics, therapeutics, and monitoring tools. Key areas include autism and ADHD screening, mental health, substance use, chronic disease management, and global digital health. His work emphasizes ethical AI, crowdsourcing, and personalized machine learning. The recent publications highlight a strong trend in applying AI to real-world health challenges, particularly through passive sensing, social media data, and multimodal inputs. His research spans digital phenotyping, remote monitoring, and bias mitigation in clinical AI, with a focus on implementation and equity. Scientific Awards: NIH New Innovator Award (2023–2028) Stanford Interdisciplinary Graduate Fellowship (2020–2022) Dr. Washington teaches Epi 233 – Introduction to Clinical Artificial Intelligence – and mentors UCSF students, staff, residents, and fellows in research projects. His lab is funded by the NIH and NSF, including as Principal Investigator on grants focused on personalized machine learning and crowd-powered diagnostics for neurodevelopmental conditions. He is actively involved in global health equity initiatives through the Institute for Global Health Sciences at UCSF. Labs and Teams: UCSF TECH Lab (Technologies Empowering Consumer Health) Collaborator with Wall Lab and other interdisciplinary teams
George P. Kafentzis is a Lecturer in the Computer Science Department at the University of Crete, where he teaches Physics for Engineers (CS-112), Digital Signal Processing (CS-370), and Signals and Systems (CS-215). He is a core member of the Speech Signal Processing Lab within the Multimedia Informatics Labs, focusing on advanced signal processing methodologies. His educational background includes a Ph.D. in Signal Processing and Telecommunications from MATISSE Doctoral School (University of Rennes 1) and a Ph.D. in Computer Science and Engineering from the University of Crete (2014), a Master of Science in Computer Science (2010), and a Bachelor's degree in Computer Science (2008), all from the University of Crete. Research interests span speech, audio, and biosignal processing with emphasis on sinusoidal modeling, emotion recognition from speech, deep learning applications, pathological speech analysis, and music signal processing. His work bridges theoretical signal processing with clinical and engineering applications, particularly in non-invasive vocal fold pathology detection through glottal analysis. Recent publications demonstrate a strategic pivot toward cough sound analysis for respiratory diagnostics using AI, while maintaining core expertise in adaptive sinusoidal models for speech transformations. Publication trends reveal an evolution from fundamental speech modeling (2010-2016) toward applied health informatics (2021-present), with increasing focus on real-world diagnostic systems leveraging cough acoustics. Over 50% of recent work integrates deep learning with traditional signal processing for medical applications, particularly in low-resource settings. Graduate student Scholarship - Institute of Computer Science, FO.R.T.H. (2008-2010) Undergraduate Scholarship - Institute of Computer Science, FO.R.T.H. (2007-2008) As an active industry collaborator, Kafentzis has served as Signal Processing Engineer at Hyfe AI (2022-2025) and contractor for VoiceSignals and Toshiba Research Europe. His teaching portfolio includes a widely adopted textbook Continuous and Discrete Time Signal Processing (2019), which integrates MATLAB implementations with theoretical foundations. Current research leverages his signal processing expertise in cough monitoring systems validated through multicenter clinical trials. He leads projects in the Speech Signal Processing Lab including Novel Deep Learning Architectures for Automatic Speech Recognition and Speech Emotion Recognition and Visualization Techniques, with recent work extending to Greek-language pathological speech analysis and respiratory health monitoring systems.
Chiara Brombin serves as Associate Professor of Statistics (SECS-S/01) at Vita-Salute San Raffaele University's Faculty of Psychology and contributes to the University Center for Statistics for Biomedical Sciences (CUSSB). Her academic career at the institution spans from Research Fellow (2010-2013) through fixed-term researcher positions (2013-2021) to her current role, demonstrating sustained institutional engagement and scholarly progression. Education: PhD in Statistical Sciences (2009), University of Padua Bachelor's Degree in Statistical and Economic Sciences (2005), University of Padua Research Focus: Dr. Brombin specializes in shape analysis, permutation testing, and advanced multivariate modeling with biomedical applications. Her work develops computational frameworks integrating facial expressions/biosignals (FIRB 2012 project) and applies joint latent class models to clinical subgroups. Recent publications emphasize statistical innovation in gene therapy efficacy, cancer treatment optimization, and pandemic response analytics through rigorous longitudinal/survival modeling. Publication Trends: Her 2024-2025 output in Nature, Science Translational Medicine, and specialized biostatistics journals reveals three converging themes: (1) network-based approaches for psychophysiological healthcare data, (2) joint modeling of longitudinal biomarkers with survival outcomes in immunology/oncology, and (3) shape analysis applications in genomic editing safety assessment. These works consistently integrate Bayesian networks with permutation-based validation. Scientific Recognition: Futuro in Ricerca 2012 award (MIUR) for emotion interpretation research Academic Leadership: Dr. Brombin has coordinated doctoral committees for Cognitive and Behavioral Sciences (2022-2024 cycles) and secured FIRB project funding as national coordinator. Her teaching portfolio spans undergraduate statistics methodology to graduate advanced modeling, with current responsibility for five courses including Multidimensional Data Analysis and Advanced Modeling in Psychology. She maintains active collaboration with CUSSB research teams on gene therapy and cancer imaging projects. Research Infrastructure: As core faculty in CUSSB, she leads statistical development for interdisciplinary teams in hematopoietic stem cell research and prostate cancer radiotherapy trials, applying shape analysis to [11C]-choline PET/CT imaging data and developing open-source tools for joint model implementation.
Patrick Ganzer serves as an Assistant Professor in the Department of Biomedical Engineering within the College of Engineering at the University of Miami. His research focuses on developing closed-loop neuromodulation therapies, with particular emphasis on integrating machine learning approaches to decode biosignals and promote neuroplasticity for neurological recovery. Ganzer's research interests span multiple interconnected domains including closed-loop neuromodulation systems, machine learning applications for physiological signal processing, and neuroplasticity mechanisms following neurological injury. His laboratory investigates how reactive nerve stimulation can be precisely timed to disease events to optimize therapeutic outcomes, with applications in spinal cord injury, stroke recovery, and cardiovascular dysfunction. His recent publications reveal a strong focus on using artificial intelligence to detect and reverse physiological abnormalities, particularly in cardiovascular and neurological contexts. The research demonstrates how machine learning can decode sympathetic responses, optimize vagus nerve stimulation timing, and predict clinical outcomes in conditions like atrial fibrillation. Krishna Kumar New Investigator Award (2022) Ganzer leads the Ganzer-Kanumuri Neurotechnology Lab, mentoring multiple PhD students, postdoctoral fellows, and undergraduate researchers. His research is supported by significant NIH funding (R01 NS131493) and Florida Department of Health grants (COPBC), indicating strong institutional confidence in his research direction. Current projects explore how chronic stress alters supraspinal blood pressure control, the relationship between cardiovascular remodeling and deficits after spinal cord injury, and state-dependent vagus nerve stimulation approaches. The Ganzer-Kanumuri Neurotechnology Lab operates as a collaborative environment with Dr. Vivek V. Kanumuri as Co-Principal Investigator, focusing on closed-loop neuromodulation therapies that integrate disease event detection with precisely timed nerve stimulation to promote functional recovery.
Junho Park is an Assistant Professor at the University of Calgary, holding cross-appointment across three academic units: Schulich School of Engineering (Biomedical Engineering), Cumming School of Medicine (Community Health Sciences), and Faculty of Arts (Psychology). He also serves as Faculty Researcher at W21C Research and Innovation Centre, Member at O'Brien Institute for Public Health, and Child Health and Wellness Researcher at Alberta Children's Hospital Research Institute. PhD in Industrial and Systems Engineering (Texas A&M University) MSc in Industrial Engineering (Seoul National University) BSc in Computer and Industrial Engineering with Psychology minor (Yonsei University) His research focuses on Human-AI Interaction, Human Factors Engineering, and Ergonomics, with specific applications in biosignal processing for prosthetics, workload assessment in assistive technologies, and cognitive support systems for aging populations. Current projects include augmented reality biofeedback systems and autonomous vehicle interfaces for individuals with cognitive impairments. Recent publications demonstrate expertise in applying machine learning to biomedical engineering challenges and human factors assessments. The HERO Lab actively seeks postdoctoral researchers and graduate students in areas spanning human-AI interaction, ergonomics, and computational modeling. Scientific Awards Student Member with Honours, Human Factors and Ergonomics Society (2023) IEEE International Conference on Human-Machine Systems Travel Grant (2022) Institute for Applied Creativity Student Fellowship, TAMU (2022) Korean-American Scholarship Foundation (2021) HFES Best Student Paper Award (2021)
Rüştü Murat Demirer serves as an Assistant Professor in the Department of Electrical and Electronics Engineering at Işık University's Faculty of Engineering and Natural Sciences. His academic career spans decades with active teaching responsibilities including Biomedical Engineering courses such as Clinical Care Informatics, Biosignal Processing, and Medical Imaging since at least 2012 across multiple institutions including Işık University and Bahçeşehir University. His educational foundation includes: PhD in Biomedical Engineering from Boğaziçi University (1983-2002) MS in Energy from Istanbul Technical University (1980-1982) BS in Electronics and Communications Engineering from Kocaeli University (1976-1980) Dr. Demirer's research integrates Biomedical Engineering with cutting-edge computational neuroscience, focusing on Bioelectronics, Artificial Intelligence applications, and Neuroscience. He pioneers methodologies for analyzing brain dynamics through EEG/ECoG signal processing, entropy-based biomarker development, and machine learning algorithms for neurological and psychiatric conditions. His work bridges theoretical neuroscience with clinical applications in epilepsy, bipolar disorder, and brain-computer interfaces. Analysis of his publication trends reveals strong interdisciplinary convergence between neuroscience, biomedical engineering, and artificial intelligence. Key methodological themes include Hilbert transform applications, nonlinear dynamics in brain signals, entropy quantification for psychiatric diagnostics, and hybrid machine learning approaches for medical signal classification. This research trajectory demonstrates consistent innovation in translating complex brain signal analysis into clinically relevant diagnostic tools. Dr. Demirer has actively mentored 11 graduate students (10 Master's and 1 PhD) between 2013-2025. His advisees' research spans diverse applications including: Machine learning for cybersecurity threat detection Cryptocurrency market analysis using predictive modeling EEG/eye-tracking fusion for cognitive decision studies Medical diagnostics through convolutional neural networks Natural language processing for offensive language detection He maintains professional engagement as a member of the Chamber of Electrical Engineers (Elektrik Mühendisleri Odası) while teaching specialized courses across biomedical engineering, cybersecurity, and data science domains.
Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.
Pietro Arico is an Assistant Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome, and Chief Technology Officer at BrainSigns. He holds a PhD in Bioengineering from University of Bologna (2014) and an M.Sc. in Biomedical Engineering from Sapienza University of Rome (2010). Biomedical Engineering Education Expertise in Brain-Computer Interfaces (BCI) Specializes in biosignal processing and machine learning His research focuses on: Passive BCI Systems: Real-time assessment of mental states (workload, attention, stress) using EEG, ECG, and other biosignals in operational environments like driving and aviation. Signal Processing Innovation: Development of algorithms for artifact removal and feature extraction in non-laboratory settings. Hybrid BCI Applications: Integration of EEG and EMG for rehabilitation systems targeting post-stroke patients. Recent work trends emphasize: Real-time mental workload monitoring in clinical and automotive contexts. Artifact correction techniques for EEG data in naturalistic environments. Neurophysiological characterization of mixed reality embodiment. Scientific awards include: 2024 - MINDTOOTH paper selected for AIIC National Conference 2014 - Award 'Massimo Grattarola' for BCI research 2014 - Scientific Prize 'I Guidoniani' for mental load studies 2013 - Finalist for Scientific Award 'I Guidoniani' He teaches Biosignal Processing , Biological System Models , and Biosignal Processing and Electromagnetic Fields in Sapienza's MSc programs in Biomedical Engineering and Medicine and Surgery HT.
Clemens Brunner is a Researcher at the Institute of Psychology within the Faculty of Natural Sciences at the University of Graz. His work bridges electrical/biomedical engineering and cognitive neuroscience, specializing in the neural mechanisms of arithmetic processing and numerical cognition. He actively develops open-source neuroimaging tools used globally in EEG research and sleep analysis. His research focuses on EEG oscillations, biosignal processing, and machine learning applications in cognitive neuroscience. Key interests include arithmetic fact learning, neural correlates of numerical order processing, and non-invasive brain stimulation effects on mathematical cognition. His expertise spans Python programming, statistical analysis, and contributions to major scientific libraries like scikit-learn and SciPy. Analysis of his recent publications reveals consistent emphasis on electrophysiological signatures of arithmetic processing, with growing integration of computational methods and neuromodulation techniques. His work demonstrates strong methodological innovation through open-source software development for EEG analysis and sleep staging. Brunner contributes to the University of Graz's "Brain and behavior" research network, developing tools like MNELAB, SleepECG, and XDF.jl that enhance reproducibility in neuroscience. His collaborations extend to major projects including MNE-Python and BNCI Horizon 2020, advancing brain-computer interface methodologies and neuroimaging standards.
Daivaras Sokas is a researcher at the Institute of Biomedical Engineering, Kaunas University of Technology, affiliated with the Biosignal Analytics Laboratory. His primary responsibilities include conducting laboratory work and grading reports for the course 'Digital Signal Processing and Machine Learning.' He holds an ORCID ID (0000-0001-5723-3655). Research Interests: His work focuses on Digital signal processing Machine learning applications in biomedical contexts Electrical and electronic engineering (T001) Laboratory Affiliation: He is actively involved with the Biosignal Analytics Laboratory at KTU.
