جست‌وجوی استاد و پژوهشگر

Daniel W. Blissمشاهده پروفایل

استاد

• Radar
• Wireless Communications
• Precision Positioning
• +۷ مورد دیگر

Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.

Frank L. Hammond IIIمشاهده پروفایل

استادیار

• Adaptive Robotic Manipulation
• Soft Robotics
• Wearable Robotics
• +۵ مورد دیگر

Frank L. Hammond III serves as Assistant Professor at Georgia Tech's Woodruff School of Mechanical Engineering since April 2015, directing the Adaptation Robotic Manipulation (ARM) Laboratory. A Carnegie Mellon PhD graduate, he previously held postdoctoral positions at MIT and Harvard as a Ford Fellow. His interdisciplinary work bridges mechanical engineering, biomedical applications, and computational design. Education Ph.D. in Mechanical Engineering, Carnegie Mellon University M.S. in Mechanical Engineering, University of Pennsylvania M.S. in Electrical Engineering, University of Pennsylvania B.S. in Electrical Engineering & Biomedical Engineering, Drexel University Hammond's research pioneers adaptive robotic manipulation (ARM) systems that operate in unstructured human environments through bioinspired computational design. His lab develops xenomorphic (non-biomorphic) robots using soft pneumatic actuation, flexible electronics, and machine learning to achieve biological-level versatility. Key application domains include wearable human augmentation devices , haptic-enabled surgical teleoperation , and autonomous soft platforms for medical and industrial use. The ARM methodology integrates empirical biomechanics characterization with simulation-driven optimization and rapid prototyping. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Medical rehabilitation breakthroughs through intention-driven exoskeletons with soft bioelectronics, (2) Novel locomotion strategies for soft robots in complex environments (sand, water, cluttered spaces), and (3) Advanced haptic feedback systems leveraging multimodal sensory substitution for proprioceptive restoration. These works consistently bridge biomechanics, control theory, and human factors. Awards Ford Postdoctoral Research Fellowship at Harvard School of Engineering Hammond actively mentors graduate researchers including PhD candidates Lucas Tiziani (soft actuators) and Bangyuan Liu (earthworm robotics), and Master's student Alex Hart (pediatric haptics). His lab secures research funding for projects like tunable mechanical interfaces for neuropathy treatment and cognition-focused wearable devices, with strong industry and clinical partnerships evident in co-authored medical device publications. The ARM Lab maintains robust collaborations across Georgia Tech's robotics, neuroscience, and biomedical engineering communities. The Adaptation Robotic Manipulation Laboratory operates from Whitaker Building Room 4102, housing specialized facilities for soft robot fabrication (3D printing, shape deposition manufacturing) and biomechanics testing. Current projects include pediatric haptic feedback displays, biomimetic swimming robots, and kirigami-skinned earthworm robots for subsurface locomotion. The lab emphasizes translational research with multiple pending medical device patents and active participation in K-12 STEM outreach programs.

Massimo Mischiمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

• Phonetics
• Conversation Analysis
• Prosody
• +۵ مورد دیگر

Mattias Heldner is Professor of Phonetics and Head of Department at the Department of Linguistics, Stockholm University. He also serves as Director of the Phonetics Laboratory where he has developed advanced recording facilities for breathing movements and voice quality dynamics in conversation. Dr. Heldner received his PhD in Phonetics from Umeå University in 2002, completed a postdoc at TeliaSonera Sweden in 2005, became Docent in Speech Communication at KTH Royal Institute of Technology in 2007, and was appointed Professor in Phonetics at Stockholm University in 2011. His research focuses on communicative behavior relevant for turn-taking in conversation and signaling of prosodic functions such as prominence and boundaries. His work spans multiple dimensions of speech communication, including: Respiratory patterns and their role in conversation flow Voice quality dynamics as turn-taking cues Prosodic functions in spontaneous speech Physiological constraints in verbal communication Acoustic properties of child speech development Neural correlates of lexical stress Dr. Heldner's recent publications demonstrate how respiratory signals provide crucial information for predicting speech activity and managing turn-taking. His research combines physiological measurements with acoustic analysis to uncover hidden aspects of conversational organization, particularly the relationship between breathing patterns and speech timing. As an educator, Dr. Heldner has taught phonetics courses for Speech and Language Pathology students at Karolinska Institutet, University of Gothenburg, and Umeå University. He currently supervises one PhD student and has successfully supervised two PhD theses to completion. Dr. Heldner has held several Swedish research grants and served as one of the Technical Program Chairs for Interspeech 2017 in Stockholm. His Phonetics Laboratory has developed innovative systems like RespTrack for measuring respiratory movements during speech with high experimental control.

Richard M. Sternمشاهده پروفایل

استاد

• Speech Recognition
• Audio Signal Processing
• Music Information Retrieval
• +۳ مورد دیگر

Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.

Cecilia Mascoloمشاهده پروفایل

استاد

• Mobile Systems
• Wearable Computing
• Machine Learning
• +۵ مورد دیگر

Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.

James W. Navaltaمشاهده پروفایل

دانشیار

• Exercise Physiology
• Outdoor Exercise Physiology
• Wearable Technology Validation
• +۳ مورد دیگر

Dr. James W. Navalta is an Associate Professor in the Department of Kinesiology and Nutrition Sciences at the University of Nevada, Las Vegas. His research focuses on physiological responses to outdoor exercise (hiking, trail running) and the validity/reliability of wearable technology. He earned his B.S. in Physical Education and Biology from Brigham Young University–Hawaii, M.S. in Kinesiology from UNLV, and Ph.D. in Exercise Physiology from Purdue University. Education: B.S. - Physical Education & Biology, Brigham Young University–Hawaii M.S. - Kinesiology, University of Nevada, Las Vegas Ph.D. - Exercise Physiology, Purdue University His research portfolio includes: Wearable technology validation for physiological measurements Comparative studies of indoor vs outdoor exercise environments Impact of gender-inclusive approaches on sports science Metabolic and cardiovascular responses to unconventional workouts Psychological benefits of nature immersion Recent publications demonstrate expertise in: Wearable device accuracy testing VO2max and lactate threshold validation Environmental influence on exercise physiology Methodological improvements in data collection Gender-inclusive research design Outdoor activity impact assessment As co-founder and executive editor of the International Journal of Exercise Science, he contributes significantly to academic discourse. He also serves on editorial boards for journals related to digital health and exercise technology.

• Biomedical Signal Processing
• Respiratory Monitoring
• Sleep Disorders
• +۴ مورد دیگر

Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.

Jana Kainerstorferمشاهده پروفایل

استاد

• Biomedical Optics
• Neurophotonics
• Medical Devices
• +۳ مورد دیگر

Jana Kainerstorfer is a Professor of Biomedical Engineering at Carnegie Mellon University (CMU), with courtesy appointments in the Neuroscience Institute and Electrical & Computer Engineering. She serves as Associate Department Head for Faculty and Graduate Affairs within the College of Engineering. Her research focuses on developing non-invasive optical imaging methods for disease detection and treatment monitoring, particularly in diffuse optical imaging. Key areas include cerebral hemodynamic monitoring in traumatic brain injury and handheld devices for breast cancer imaging. Dr. Kainerstorfer holds senior membership in the Optical Society of America and has received prestigious awards such as the NIH Trailblazer Award and AHA Scientist Development Grant. She leads the Biophotonics Lab, which bridges engineering and clinical applications, emphasizing translational research. Education: PhD from University of Vienna/NIH (2010), Postdoc at Tufts University Research Interests Her work revolves around biomedical optics , neurophotonics , and medical device innovation . Current projects include: Non-invasive cerebral hemodynamic monitoring Transabdominal fetal pulse oximetry Optical imaging in extreme environments (e.g., freediving physiology) Her lab develops tools like wearable NIRS for marine mammals and self-calibrating pulse oximetry algorithms. Research spans clinical translation and physiological mechanism discovery , with emphasis on microvascular imaging. Awards & Recognition NIH Trailblazer Award (2020) AHA Scientist Development Grant SPIE Fellow (2022) George Tallman Ladd Award (CMU) Lab & Collaborations The Biophotonics Lab collaborates with neurosurgery, oncology, and marine biology teams. Projects address clinical needs in neurocritical care and fetal monitoring, leveraging optical technologies for real-time diagnostics. Ongoing work includes: Optical assessment of cerebral metabolic rates Non-invasive intracranial pressure estimation Multi-modal EEG-NIRS fusion for neural source localization

Abdulkadir C. Yucelمشاهده پروفایل

استادیار

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +۷ مورد دیگر

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

Riccardo Raheliمشاهده پروفایل

استاد

• Telecommunications
• Digital Signal Processing
• Wireless Communications
• +۵ مورد دیگر

Riccardo Raheli is a Full Professor at the University of Parma , Department of Engineering and Architecture, with a career spanning over three decades in Information and Communication Technologies (ICT). He has served as Chair of the Councils for Telecommunications and Communication Engineering programs, and as representative of the University of Parma in CNIT and its Members' Assembly. Education: Laurea in Electronic Engineering (University of Pisa, 1983), M.Sc. in Electrical and Computer Engineering (University of Massachusetts, 1986), Postgraduate Diploma (Scuola Superiore Sant'Anna, 1987) Key Roles: President of Degree Councils (2002-2018), CNIT Committee Member (2000-2005), Editorial Board member for IEEE Transactions, Springer and MDPI journals His research bridges telecommunications , digital signal processing , and healthcare applications , producing extensive international publications and industrial patents. He has co-authored monographs including Detection Algorithms for Wireless Communications (Wiley, 2004) and LDPC Coded Modulations (Springer, 2009). Recent article trends show interdisciplinary work in automotive stress monitoring (IoT/Matlab-based systems), video processing for healthcare (neonatal seizures, respiratory monitoring), and acoustic field control (microphone virtualization, personal sound zones). His work spans machine learning applications in automotive systems, stochastic acoustic modeling , and power-line communications . Scientific Leadership : Co-Chair for IEEE conferences (ICC 2010, GLOBECOM 2011, ISPLC 2020) Editorial roles in 7+ international journals Grants & Collaborations : Led industrial patents in communications systems Coordinated CNIT Technical Reports series (2025) He teaches Wireless Communications and Digital Signals Laboratory , emphasizing Matlab/Simulink proficiency. His laboratory sessions focus on practical implementation of signal processing algorithms, requiring full software installation on personal devices.

Lorenzo Pellisمشاهده پروفایل

پژوهشگر ارشد

• Infectious Disease Epidemiology
• Mathematical Modeling
• Epidemiology
• +۶ مورد دیگر

Dr. Lorenzo Pellis is a Research Fellow at the University of Manchester, holding the Sir Henry Dale Fellowship, and a Visiting Fellow at the University of Warwick's Mathematics Institute and Zeeman Institute. He is also an Honorary Research Associate at the Medical Research Council (MRC) Centre for Outbreak Analysis and Modelling, within the Department of Infectious Disease Epidemiology at Imperial College London. His research bridges applied mathematics and epidemiology, focusing on developing models that inform public health decisions. He earned his Doctoral degree in Mathematical Biology from Imperial College London in 2009, with a dissertation titled Mathematical models for emerging infections in socially structured populations: the presence of households and other social structures II: Comparisons and implications for vaccination . Pellis's research interests include the development of novel deterministic and stochastic methods to model infection spread dynamics, particularly in human populations with complex social structures. He focuses on directly transmitted infections and the impact of co-infections on epidemiological and evolutionary outcomes. His work emphasizes multi-scale models integrating within-host and between-host processes, with applications to antimicrobial resistance, HIV-TB co-infections, and respiratory syncytial virus (RSV) transmission in Kenya. He also explores model comparison techniques to assess the utility of simple models in public health decision-making. His recent articles collectively explore mathematical modeling in infectious disease dynamics, with a focus on network-based approaches, multi-strain infections, and the integration of within-host and between-host processes. They highlight challenges in metapopulation and network models, as well as the evolutionary dynamics of HIV and TB co-infections. Sir Henry Dale Fellow , funded by the Wellcome Trust and Royal Society His grants include support for his Sir Henry Dale Fellowship, which funds research on co-infections and multi-scale models. He collaborates with Prof. Matt Keeling and Dr. Thomas House at Warwick and Prof. James Nokes on RSV studies in Kenya. His work also involves improving epidemic dynamics approximation methods on networks. He is affiliated with the applied Mathematics group at Manchester's School of Mathematics, the Zeeman Institute at Warwick, and the MRC Centre at Imperial College. His interdisciplinary collaborations span institutions and disciplines, including applied mathematics, epidemiology, and public health.

Melanie Schienleمشاهده پروفایل

استاد

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +۴ مورد دیگر

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

Michael Stevensمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Advanced Manufacturing
• Digital Manufacturing
• +۶ مورد دیگر

Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.

• Digital Signal Processing
• Communication Systems
• Biomedical Signal Processing
• +۶ مورد دیگر

Dr. Barry Cardiff is an Assistant Professor in the School of Electrical and Electronic Engineering at University College Dublin (UCD), where he has been a member of academic staff since September 2013. His career spans both industry and academia, with significant experience at Nokia Mobile Phone (UK) Ltd and Silicon & Software Systems (S3 group) before returning to complete his PhD at UCD. Education: B.Eng (1992), M.Eng.Sc. (1995), PhD (2011) from University College Dublin Professional Experience: Design Engineer at Nokia (1993-2001), Systems Architect at S3 group (2001-2007, 2011-2013) Current Position: Assistant Professor at UCD School of Electrical and Electronic Engineering Dr. Cardiff's research focuses on Digital Signal Processing applications in communication systems, with particular emphasis on theoretical analysis and practical implementation. His work bridges traditional communication theory with emerging biomedical applications, especially in wearable IoT sensors. He has made significant contributions to power/complexity reduction techniques in circuit design, specifically DSP algorithms for digitally assisted analog circuits. His research program addresses critical challenges in biomedical signal processing, sensor fusion, and efficient data transmission for healthcare applications. His recent publications demonstrate a strong trend toward biomedical applications of signal processing techniques, with a focus on ECG analysis, atrial fibrillation detection, and respiratory rate estimation using multimodal sensor fusion. The research shows a clear progression from traditional communication systems toward healthcare applications, with an emphasis on edge computing solutions that reduce power consumption in wearable devices. IEEE BioCas best paper award (2024) IEEE senior member since 2019 Active reviewer for multiple IEEE journals including Transactions on Biomedical Circuits and Systems, Circuits and Systems, and VLSI Systems Dr. Cardiff has supervised numerous research projects and has been instrumental in developing curriculum for digital communications, signal processing, and wireless systems. His teaching philosophy emphasizes open, friendly, and hands-on approaches that encourage independent thinking. He coordinates multiple modules including Communication Theory, Digital Electronics, DSP Technology, and Wireless Systems, demonstrating his commitment to both theoretical foundations and practical applications of electrical engineering principles. His research group works at the intersection of signal processing, machine learning, and biomedical engineering, developing innovative solutions for wearable healthcare monitoring. Current projects focus on event-driven processing architectures, decentralized classification systems, and signal quality-aware fusion techniques that enable robust performance in noisy real-world environments.