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Ricardo ZednikView profile

Professor

• Piezoelectricity
• Fracture Mechanics
• Microelectronic Mechanical Systems (MEMS)
• +4 more

Ricardo Zednik is a Professor at the Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS) in Montreal. Holding degrees from Rice University (BA, BS) and Stanford University (MS, PhD), he specializes in piezoelectric materials, fracture mechanics, and microelectronic systems. His research focuses on sensors, innovative materials, and health technologies. Fields of Interest: Piezoelectricity, Fracture Mechanics, MEMS, Smart Materials, Crystallography With over 36 peer-reviewed publications and extensive supervision of graduate research (including 15+ co-directed theses and projects since 2016), Zednik contributes to applied research in materials science and biomedical engineering. He collaborates with LaCIME and PULÉTS laboratories on cutting-edge projects involving ultrasonic transducers, flexible sensors, and high-temperature material characterization. Current courses include Materials Technology (MEC200) and advanced research topics in Functional and Smart Materials (SYS877). His students explore applications like terahertz quality control, piezoelectric earcanal sensors, and Kirigami techniques for wearable electronics.

• Wireless Communications
• Statistical Signal Processing
• Estimation and Equalization
• +3 more

Cihan Tepedelenlioglu is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, statistical signal processing, and renewable energy systems, with a focus on photovoltaic array monitoring, fault detection, and optimization. PhD, MS, and BS in Electrical Engineering from University of Minnesota, University of Virginia, and Florida Institute of Technology 2001 NSF CAREER Award recipient Research interests span wireless communications , graph signal processing , stochastic optimization , and machine learning applications to solar energy systems . Key projects include quantum machine learning for PV topology optimization, consensus algorithms for distributed networks, and real-time fault detection using neural networks. Recent articles emphasize machine learning in energy systems (2023-2025), with 12 publications on photovoltaic monitoring and 3 on consensus algorithms. Earlier work focused on channel estimation in OFDM systems and fading models in wireless communications. Scientific awards : NSF CAREER Award (2001) Major grants include NSF funding for networked solar array management (2013-2016), nonlinear distributed consensus (2013-2016), and statistical processing of solar data (2009-2012). Teaching roles include EEE 350 Random Signal Analysis and graduate research supervision in signal processing and wireless communications. Collaborates extensively with Andreas Spanias, Mahesh Banavar, and other researchers on cyber-physical systems for energy applications.

Paolo PrandoniView profile

Lecturer

• Signal Processing
• Audio Processing
• Image Processing
• +5 more

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

Taein KwonView profile

Research Fellow

• Egocentric Vision
• Action Recognition
• Contextual AI
• +6 more

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

• Telecommunications
• Communication Systems
• Satellite Communications
• +7 more

Roberto Garello is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . He specializes in Communication Systems , Satellite Networks , and Channel Coding , with a focus on 5G/6G Technologies and Non-Terrestrial Networks . His work aligns with the School of Master’s Programmes and Lifelong Learning . Research Interests: Satellite communications systems, Direct-to-Satellite IoT constellations, Mega-constellation services in space, and physical layer advancements for 5G/6G. Teaching: Offers courses like Information Theory for Data Science , Communication and Network Systems , and Space Exploration and Resources , while supervising Applied Signal Processing Laboratory . Projects: Leads initiatives such as DitDSSS (satellite localization), RESTART (future telecommunications), and TESL@ (ICT energy efficiency). Scientific Awards: Received Best Paper Awards at CTRQ 2010 and COCORA 2013. Students: Supervises PhD candidates including Alessandro Compagnoni, Agbotiname Lucky Imoize, and Riccardo Tuninato, focusing on topics like Wireless Communication, Machine Learning, and Non-Terrestrial Networks. Publications: His recent work explores OTFS vs. OFDM, spectrum sensing algorithms, MIMO with cylindrical arrays, and 5G NTN synchronization, reflecting trends in satellite IoT and machine learning integration.

• Artificial intelligence
• Biomedical signal processing
• Human movement analysis
• +12 more

Marco Ghislieri is an Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab and teaches in the Biomedical Engineering program, including courses like Neuroengineering and Design of Programmable Biomedical Devices . His research spans Artificial Intelligence, Biomedical Signal Processing, Neuroscience, and Rehabilitation Engineering . PhD in Bioengineering and Medical-Surgical Sciences (2017-2021) at Politecnico di Torino Thesis: Muscle Synergy Assessment during Cyclic and Non-Cyclic Movements His research focuses on muscle synergy analysis in Parkinson’s Disease (PD) patients post- Deep Brain Stimulation (DBS) , AI-driven gait analysis for fall prevention, and wearable sensor applications for stress-cognitive decline monitoring. He leads the S-CoDe and OMNIA-PARK projects, and contributes to PRIN as a team member. Recent publications highlight advancements in machine learning for intraoperative DBS targeting , statistical gait analysis , and neurorehabilitation tools . He serves as Associate Editor for Scientific Reports and Applied Bionics and Biomechanics , and Guest Editor for Frontiers in Neural Circuits . Awards include the Carlo J. De Luca Award (2022) , GNB Doctoral Award (2022) , and the Best Poster Award at M. Grattarola Summer School (2022) . He supervises Fabrizio Sciscenti (PhD candidate) and collaborates on neuroengineering and biomedical device design courses. His work addresses Goal 3 (Good Health) and Goal 4 (Quality Education) of the UN SDGs.

Ethan WilliamsView profile

Assistant Professor

• Seismology
• Oceanography
• Acoustic Monitoring
• +3 more

Ethan Williams is an Assistant Professor at the University of California, Santa Cruz, Department of Earth & Planetary Sciences. His research focuses on distributed acoustic sensing (DAS) applications in seismology, oceanography, and environmental monitoring, particularly studying wave dynamics, subduction geohazards, and ocean-solid Earth interactions. Ph.D. in Geophysics, California Institute of Technology (2023) M.S. in Geophysics, California Institute of Technology (2019) B.S. in Geophysics and B.A. in Music, Stanford University (2017) Research Interests: Williams specializes in DAS technology, subduction zone geohazards, surface wave dynamics, and ocean-solid Earth coupling. His work bridges seismic monitoring, marine geophysics, and environmental sensing. Publication Trends: His recent articles emphasize DAS innovation for offshore seismic monitoring, wave propagation analysis, and integrating fiber-optic data with conventional measurements. Themes include earthquake detection, climate change impacts, and multi-scale ocean dynamics. Scientific Awards: 2022 SSA Student Presentation Award for 'Continuous seismic monitoring of a building over 20 years' Advising & Collaboration: Williams collaborates with institutions like the University of Washington and Caltech, working on NSF-funded projects such as the Ocean Observatories Initiative. He invites prospective students/postdocs to contact him via email.

• Wireless Sensing
• Radar Technology
• Machine Learning
• +7 more

Kevin Chetty is a Professor of Wireless Sensing at University College London (UCL), leading the Urban Wireless Sensing Lab within the Department of Security and Crime Science. His work bridges radar technology, machine learning, and healthcare applications, with a focus on passive sensing systems. Education: PhD in Medical Ultrasound Physics (Imperial College London, 2004-2007), MRes in Image and X-Ray Physics (King's College London, 2003), BSc in Physics (King's College London, 1999) Research spans radar micro-Doppler signature analysis for human behavior classification, software-defined radar development, and integrated communication-sensing systems, with applications in security, healthcare, and smart environments. Recent work emphasizes privacy-preserving technologies and edge processing for real-time operations. Scientific awards include the 2022 IET Radar Systems Best Paper Runner-Up, 2022 IEEE Radar Conference 2nd Place, and 2015 National Instruments Engineering Impact Award. He has received funding from government and industry sectors in telecommunications, IoT, security, and healthcare. Teaching roles: Programme Convener for MSc Crime Science and IEP Minor in Crime and Security Engineering; Module Convener for Security Technologies and Crime Mapping & Spatial Analysis Consultancy: Huawei Technologies (2020-2022), Metropolitan Police Service (2019)

Ernesto Elias Vidal RosasView profile

Lecturer

• Neuroimaging
• Brain-computer interfaces
• Medical optics imaging
• +2 more

Dr. Ernesto Elias Vidal Rosas is a Lecturer at the School of Electronics and Computer Science, University of Southampton. Previously, he was a Postdoctoral Researcher at UCL Medical Physics and Biomedical Engineering Department. He specializes in neuroimaging and optical technologies with extensive expertise in near-infrared spectroscopy (NIRS), diffuse optical tomography (DOT), and wearable brain imaging systems. He is currently accepting PhD students. Research Focus: Dr. Vidal-Rosas leads research in neuroimaging methodologies and brain-computer interfaces. His work emphasizes wearable technologies for clinical and home-based monitoring, with applications spanning from neonatal care to motor rehabilitation. Key research domains include: Development of high-density diffuse optical tomography (HD-DOT) systems Integration of EEG and fNIRS for brain-computer interfaces Real-time processing algorithms for neurofeedback applications Non-invasive monitoring of therapeutic responses in oncology He is currently involved in the Horizon Europe PUREMIND project focused on preventing mental illness through multimodal biomarker analysis. Academic Activities: Actively advises PhD candidates in Computer Science and teaches undergraduate courses including Healthcare Technology Design (ELEC2231) and Digital Control System Design (ELEC3206). His pedagogical research includes developing virtual tools for engineering education. Affiliations: Member of the Digital Health and Biomedical Engineering research group and the Institute for Life Sciences at Southampton.

Jean-François FrigonView profile

Professor

• Wireless Communications
• Signal Processing
• Adaptive Systems
• +3 more

Jean-François Frigon is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal, specializing in wireless communication systems and signal processing. His research spans adaptive systems, MIMO technologies, and energy-harvesting networks. Research Focus: Wireless communications, signal processing algorithms, and MIMO systems Collaborations: LORLAB, IVADO, POLY-GRAMES, GERAD Recent Publications: 15+ articles on topics including regenerative satellite payloads, machine learning for beamforming, and mmWave system optimization. His work emphasizes AI integration in 5G/6G and sustainable wireless solutions. Awards: Recipient of NSERC Discovery Grant funding in 2022. Collaborated with Ericsson on AI-driven 5G sustainability projects. Lab Affiliations: Active in LORLAB (Laboratoire de recherche en radiocommunications) and POLY-GRAMES microwave research center.

Aaron YoungView profile

Associate Professor

• Human Augmentation
• Wearable Robotics
• Assistive Devices
• +7 more

Aaron Young is an Associate Professor in the Woodruff School of Mechanical Engineering at Georgia Institute of Technology and a program faculty member in the Biomedical Engineering School. He serves as Director of the Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab, focusing on robotic human augmentation through advanced control systems for prosthetics and exoskeletons. Education: Postdoctoral Fellow, University of Michigan (2014-2016) Ph.D., Northwestern University (2014) M.S., Northwestern University (2011) B.S., Purdue University (2009) Dr. Young's research addresses clinically viable control systems for wearable robotic devices, emphasizing intent recognition , EMG signal processing , and machine learning integration. His work targets mobility impairments from stroke, amputation, cerebral palsy, and neurological injuries, aiming to reduce metabolic costs, restore natural biomechanics, and enhance community ambulation. Key innovations include data-driven control frameworks , biomechanical terrain adaptation , and anthropometry-based personalization . His recent publications highlight advancements in deep learning for real-time biomechanics , EMG-informed joint estimation , and adaptive assistance systems . The EPIC Lab's facilities feature a terrain park with force plates , motion capture systems , and HumoTech simulation platforms for device testing. Scientific Awards: New Faces of Engineering (IEEE USA, 2017) Military Health System Team Award (2015) NSF Graduate Fellowship (2010) NDSEG Fellowship (2010) IEEE EMBC 3rd Place (2013) Projects include NSF-funded hip exoskeletons for stroke survivors, DoD-powered prostheses for amputees, and Pediatric knee exoskeletons for cerebral palsy. The lab cultivates interdisciplinary expertise in robotics , biomedical engineering , and human-machine interaction .

Sean AndersonView profile

Senior Lecturer

• Bioinspired Robotics
• Adaptive and Optimal Control
• System Identification
• +3 more

Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.

Sudip VhaduriView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Federated Machine Learning
• +8 more

Sudip Vhaduri is an Assistant Professor in the School of Applied and Creative Computing at Purdue University, where he serves as Director of the mobile Artificial Intelligence (mAI) Laboratory. He holds appointments with several Purdue research centers including the Applied AI Research Center (AARC), Center for Education to Research in Information Assurance and Security (CERIAS), Purdue Institute of Inflammation, Immunology and Infectious Disease (PI4D), and the Institute for a Sustainable Future (ISF). Dr. Vhaduri received his educational training from: Ph.D. in Computer Science and Engineering from University of Notre Dame M.Sc. in Computer Science from University of Memphis B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology His research focuses on the intersection of artificial intelligence and mobile computing, with particular emphasis on machine learning, deep learning, and federated machine learning approaches. Dr. Vhaduri's work integrates three major application areas: health informatics leveraging smartphone and wearable sensing, continuous user authentication using physiological and behavioral biometrics, and reliable discovery of places of interest using alternative sensor data. His research has been featured in prestigious outlets such as Forbes Magazine. Dr. Vhaduri collaborates extensively with interdisciplinary researchers from medical and nursing schools at institutions including University of Cincinnati and Indiana University, as well as industrial research institutions like IBM Research. He is an active member of multiple IEEE societies including IEEE Computer Society, IEEE Signal Processing Society, IEEE Geoscience and Remote Sensing Society, and IEEE Engineering in Medicine and Biology Society. He teaches courses including Machine Learning for Smart Sensing (F'21-F'24), Data Fusion for Machine Learning (S'23-S'24), and Database Fundamentals (S'22-present). Dr. Vhaduri actively seeks highly motivated PhD students for research in ML/DL/FedML, IoT, and Mobile & Wearable Computing, with multiple funded positions available.

Raman KashyapView profile

Professor

• Optical Fibers
• Photonics
• Lasers
• +11 more

Raman Kashyap is a Full Professor at the Department of Electrical Engineering and Department of Engineering Physics at Polytechnique Montréal . He serves as a researcher at the Centre d’optique, photonique et laser (COPL) and a member of the Advanced Research Centre in Microwaves and Space Electronics (POLY-GRAMES) . His work spans multiple domains in photonics and laser technology. B.Sc. (King's College), Ph.D. (Essex) Research Interests : Professor Kashyap's research focuses on optical fibers , laser cooling , Bragg gratings , optoelectronics , nonlinear optics , and periodically poled crystals . His work explores stimulated Brillouin scattering , optical sensors , and material modification via lasers , contributing to advancements in quantum photonics and microwave engineering . Recent Research Trends : His latest publications emphasize Anti-Stokes fluorescence cooling in silica and oxide glasses, elastic optical network optimization , and femtosecond laser writing for photonic devices. These works bridge material science , quantum computing , and telecommunications , showcasing innovative applications in temperature sensing , optical delay systems , and 3D integrated optics . Scientific Awards : 2016 - Québec Science's 10 Discoveries of the Year 2014 - Royal Society of Canada (RSC) Fellow 2013 - SPIE Fellow 2012 - Killam Fellowship (Canada Council for the Arts) 2011 - Engineering Institute of Canada (EIC) Fellow 2010 - Institute of Physics Fellow 2004 - Optical Society of America (OSA) Fellow Academic Supervision : Professor Kashyap has supervised 36 students , including 24 Ph.D. candidates and 12 Master’s students . His supervised projects cover spherical Bragg resonators , laser-induced cooling , optical frequency domain reflectometry , and femtosecond laser writing . Labs & Collaborations : He leads research at the Fabulas laboratory and collaborates with the POLY-GRAMES center. His work involves partnerships with institutions like INRS and Québec Science , influencing quantum computing and optical fiber communication technologies.

• Algorithms
• Computational Geometry
• Wireless Sensor Networks
• +1 more

Professor Cem Evrendilek is a faculty member in the Department of Computer Engineering at Izmir University of Economics, Turkey, holding the rank of Professor with current active status. His institutional email is cem.evrendilek@ieu.edu.tr. His research spans algorithmic complexity and geometric computation, with primary focus areas including: Computational Geometry (specializing in orthogonal polygon covering problems) Wireless Sensor Network Localization (energy-efficient methods and trilateration) NP-hard Problem Analysis (proving complexity for geometric and network problems) Approximation Algorithms for combinatorial optimization Analysis of his 2008-2018 publications reveals consistent work on geometric covering problems and sensor network localization. Key trends include proving NP-completeness for orthogonal polygon covering variants, developing energy-efficient mobile beacon localization techniques, and analyzing trilateration with noisy measurements. His work frequently intersects computational geometry with practical wireless network applications, demonstrating strong theoretical foundations applied to real-world constraints. Collaborative patterns show frequent co-authorship with Hüseyin Akcan (on localization algorithms), Burkay Genç, and Brahim Hnich (on computational geometry problems).