جست‌وجوی استاد و پژوهشگر

Kwabena Boahenمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Neuroscience
• Artificial Intelligence
• +۴ مورد دیگر

Kwabena Boahen is a Professor of Bioengineering and Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. He is affiliated with the Wu Tsai Neurosciences Institute, the Bio-X Institute, and the System X Alliance. His research bridges neurobiology, computer science, and electronics, focusing on neuromorphic engineering to model brain functions. He founded the Brains in Silicon Lab, developing silicon chips that emulate neural behavior efficiently. Notable contributions include the Neurogrid platform and the Braindrop architecture. Boahen holds multiple awards, including the Packard Fellowship and NIH Pioneer Award, and has led interdisciplinary research initiatives like the Brainstorm Project. He advises numerous graduate students and teaches courses on neuromorphics and bioengineering systems. Education: PhD in Computation and Neural Systems from Caltech (1997). Prior to Stanford, he was at the University of Pennsylvania (1997–2005), holding the Skirkanich Term Junior Chair. His work spans over 100 publications, emphasizing energy-efficient neural models and silicon implementations of brain-inspired systems. Research interests include neuromorphic engineering, neural network design, and the intersection of neuroscience with artificial intelligence. Recent work explores dendrocentric learning and embodied AI frameworks. He has pioneered methods for scaling neural simulations to functional brain sizes while maintaining biophysical accuracy. His publications highlight advancements in neuromorphic hardware, computational neuroscience, and interdisciplinary applications. Awards reflect recognition for innovations in neurogrid technology and contributions to neuroAI. Advising spans bioengineering, neuroscience, and biomedical data science PhD programs.

• Computer Graphics
• 3D Modeling
• Computer Animation
• +۷ مورد دیگر

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique (Institut Polytechnique de Paris), where she serves as Dean of the Master of Science and Technology program and leads the STREAM research team at LIX Laboratory (CNRS/IP Paris). Her research bridges computer graphics, HCI, and AI, focusing on expressive 3D modeling, animation, and virtual environments. Education: 1987: M.Sc. in Computer Science, École Normale Supérieure & University Paris XI 1990: Ph.D. in Computer Graphics, University Paris XI 1995: Habilitation in Computer Science, Institut National Polytechnique de Grenoble Research Interests: She pioneers user-centered tools for 3D content creation, combining gesture-based interfaces (sketching, sculpting) with knowledge-enhanced procedural models. Key areas include implicit surfaces, physical simulation, AI-driven animation, and accessible tools for non-experts. Her work enables intuitive design of complex shapes, terrains, and dynamic scenes. Publication Trends: Recent work (2023–2025) emphasizes AI-integrated graphics, including controllable natural phenomena (volcanoes, erosion), sketch-based bio-visualization, reinforcement learning for animation, and real-time character control. Cross-disciplinary applications span geology, biology, and cinematography. Awards & Honors: ACM Steven A. Coons Award (2023) French Academy of Sciences (2020) ACM SIGGRAPH Academy (2019) ERC Advanced Grant EXPRESSIVE (2012–2017) CNRS Silver Medal (2012) Leadership & Grants: She founded research teams (STREAM, Imagine, Evasion) and secured major funding: ERC Advanced Grant, Google-École Polytechnique AI Chair (2018–2021), Horizon 2020 project CLIPE (2020–2024), and Hi!Paris Creative AI Fellowship (2021–2024). She mentors researchers in geometric modeling, animation, and AI. Labs & Teams: Directs the Modeling, Simulation & Learning pole at LIX (2019–2023). Founded STREAM (Structure Representation for Animation/Modeling) and Imagine (Inria/Grenoble teams), advancing real-time graphics and procedural content.

David Gruberمشاهده پروفایل

استاد

• Marine Biology
• Animal Communication
• Biofluorescence
• +۱۲ مورد دیگر

David Gruber is a Distinguished Professor of Biology and Environmental Sciences at the City University of New York, with affiliations at Baruch College and the CUNY Graduate Center. He is also the Founder and President of Project CETI, a nonprofit interdisciplinary initiative focused on decoding sperm whale communication using advanced machine learning and robotics. His work bridges marine biology, climate science, animal communication, and bio-inspired technology. Education and Professional Background: Ph.D. in Biology (specific institution not mentioned in text) Postdoctoral training likely in marine molecular biology or biophysics (inferred from research) National Geographic Explorer and Emerging Explorer (2014) Principal Investigator, Project CETI (Audacious Project, 2020) Research Interests: David Gruber's research is deeply interdisciplinary, focusing on the intersection of marine life and technology. He investigates biofluorescence and bioluminescence across diverse species—from corals and eels to sharks and sea turtles—discovering over 200 glowing organisms. His lab explores the molecular basis of these phenomena, identifying novel fluorescent proteins with potential biomedical applications. A major focus is animal communication , particularly in sperm whales, where he applies AI and natural language processing to decode vocalizations. He also studies coral resilience to climate change and deep-sea ecosystems. Recent Research Trends: Gruber’s recent publications reveal a strong shift toward integrating artificial intelligence with marine bioacoustics. His team uses convolutional neural networks to classify sperm whale clicks and identify individuals with high accuracy. There is a growing emphasis on unsupervised machine translation to interpret non-human communication, suggesting a future where interspecies dialogue may be possible. His work increasingly combines soft robotics, genomics, and real-time underwater sensing to enable non-invasive study of fragile deep-sea life. Scientific Awards and Recognition: National Geographic Emerging Explorer (2014) Multiple National Geographic Grants (2010–2020) Audacious Project Funding for Project CETI (2020) Leader in the global movement for ocean conservation and interspecies communication Advising and Grants: As a principal investigator and professor, Gruber mentors students and early-career scientists in marine biology, genomics, and bio-inspired technology. He has secured significant funding from National Geographic and other sources to support deep-sea exploration, coral research, and whale communication studies. His grants emphasize innovation in wildlife technology , including the development of gentle, on-whale sensors and soft robotic samplers. These projects often involve collaboration with engineers, computer scientists, and conservationists, reflecting his commitment to interdisciplinary science. Labs and Teams: Gruber leads a multidisciplinary research group that collaborates with the Harvard Microrobotics Laboratory, the Dominica Sperm Whale Project (led by Shane Gero), and institutions worldwide. His lab develops cutting-edge tools such as the 'shark-eye camera' and soft robotic grippers for delicate marine organisms. Project CETI brings together over 50 scientists from more than 15 institutions, forming one of the largest collaborative efforts in cetacean communication research.

• Computational Intelligence
• Serious Games
• Machine Learning in Games
• +۵ مورد دیگر

Darryl Charles is a Professor of Artificial Intelligence and Games at the School of Arts & Humanities , Ulster University, specializing in computational intelligence applied to serious games, virtual reality, and connected health. He has over 80 peer-reviewed publications and authored the book Biologically Inspired Artificial Intelligence for Computer Games . Member of Computer Science Research Institute Fellow of the Higher Education Academy Active in SDGs related to health and education Research Interests: His work spans intelligent interactive storytelling , machine learning in games , user modeling , cloud computing , and connected health , with recent focus on natural user interfaces for rehabilitation and learning motivation. Scientific Awards: Catalyst Invent Finalist (2020) iCURE Business Accelerator (2020) Intertrade Ireland Seedcorn Finalist (2018/2019) ICVRAT/ICDVRAT Prizes (2014-2016) Academic Leadership: Served as External Examiner at Glasgow School of Art (2019-2023), University of West of Scotland (2008-2014), and other institutions.

• Machine Learning
• Artificial Intelligence
• Neuroscience
• +۱۲ مورد دیگر

Maia Fraser is an Associate Professor in the Department of Mathematics & Statistics at the University of Ottawa, with a cross-appointment in the School of Electrical Engineering and Computer Science. She is also a member of the Brain and Mind Research Institute, reflecting her interdisciplinary research bridging mathematics, machine learning, and neuroscience. Her research focuses on the theoretical foundations of machine learning, especially interactions with neuroscience, and she actively explores how biological learning can inspire AI algorithms. She also contributes to contact and symplectic geometry and computational geometry. Her work increasingly addresses the societal implications of AI, including its role in mathematical discovery and AI safety. Her recent publications reveal a strong trend toward integrating reinforcement learning with temporal and spatial hierarchies, drawing from both neuroscience and geometry. She investigates regret bounds in learning, temporal abstraction, and large-scale geometric invariants, demonstrating a unique blend of mathematical rigor and AI innovation. Her work often involves interdisciplinary collaboration, particularly with neuroscientists and mathematicians. Guest editor, Bulletin of the AMS special issue on 'Will machines change mathematics?' Co-applicant on Canada-UK AI Initiative grant on modeling the self Co-organizer, 2022 Fields Medal Symposium in honor of Akshay Venkatesh Co-moderator, Panel Discussion at Forward from the Fields Medal (FFFM) 2024 Fraser leads the NSERC-CREATE-funded INTER-MATH-AI training program and is involved in the Major Thematic Program on the Mathematics of Neuroscience at the Fields Institute (2025). She has advised students and researchers across disciplines and is currently writing a book on conceptual tools for understanding AI from a societal systems perspective. Her teaching includes courses in mathematical machine learning, differential geometry, topology, and linear algebra. She maintains active research collaborations and participates in key seminars and initiatives in machine learning, symplectic geometry, and AI ethics.

• Field robotics
• Robot olfaction
• Multi-robot systems
• +۶ مورد دیگر

Lino Marques is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, with a Senior Researcher position at the Institute for Systems and Robotics (ISR-UC) . He leads the Field Robotics group and has supervised 8 PhD students and over 60 MSc students. 1994–Present: Faculty at University of Coimbra 2005: PhD in Electrical and Computer Engineering 2023: Habilitation in Robotics His research focuses on mobile robot olfaction , multi-robot systems , sensor data fusion , and hazardous environment robotics . Recent projects emphasize UV-C disinfection robots , precision agriculture , and energy-efficient path planning . Key applications include pollutant monitoring , maritime surveillance , and mine detection . He has participated in major projects like FP7-TIRAMISU (mine removal) and FP6-GUARDIANS (scent-based navigation). His editorial roles include Editor-in-Chief for Mobile Robots and Multi-Robot Systems in the International Journal of Advanced Robotic Systems. With an h-index of 33 , he has published 16 book chapters, over 40 journal articles, and 150+ conference papers. His work spans robotic clusters , wireless sensor networks , and cyber-physical systems .

Feng Zhuمشاهده پروفایل

عضو هیئت علمی

• Impact Mechanics
• Trauma Biomechanics
• Injury Prevention
• +۱۶ مورد دیگر

Feng Zhu is an Associate Research Professor at the Hopkins Extreme Materials Institute (HEMI) and holds an adjunct appointment in the Department of Mechanical Engineering at Johns Hopkins University. His research spans impact mechanics, trauma biomechanics, injury prevention, and AI-driven mechanical design, with applications in transportation safety and protective systems. B.Eng., Central South University, China M.Phil., University of Hong Kong Ph.D., Swinburne University of Technology, Australia His research focuses on impact mechanics , particularly energy absorption in composite and architectured materials under extreme loading. He investigates trauma biomechanics , including injury mechanisms from blast and impact, using human body digital twins and high-strain-rate tissue properties. A key innovation is his work on AI-driven mechanical design , applying machine learning and data mining to optimize energy-absorbing structures, battery safety, and vehicle crashworthiness. His work bridges computational modeling, experimental validation, and real-world safety applications. The recent publications highlight a strong trend in integrating machine learning with finite element modeling to enhance the design of protective systems, particularly in lithium-ion battery safety , helmet padding optimization , and bio-inspired energy absorbers . Topics span from blast injury mitigation to autonomous vehicle safety, reflecting interdisciplinary collaboration and practical impact. Fellow, SAE International SAE Forest R. McFarland Award, 2019 Feng Zhu has secured research funding from government agencies and defense and automotive industries. He has advised students in biomechanics and mechanical design, though specific names are not listed. His editorial roles include Associate Editor for Frontiers in Future Transportation and SAE International Journal of Electrified Vehicles , and editorial board membership in Energies , International Journal of Applied Mechanics , and others. He has reviewed for over 50 journals across biomechanics, materials science, and mechanical engineering. He is affiliated with HEMI and the Ralph S. O’Connor Sustainable Energy Institute at Johns Hopkins, collaborating with teams focused on extreme materials, energy systems, and safety technologies. His lab integrates computational modeling, experimental testing, and AI to advance protective systems for humans and vehicles.

• Computational Intelligence
• Optimization Algorithms
• Educational Technology
• +۳ مورد دیگر

Zervoudakis Konstantinos is a Researcher at the School of Production Engineering and Management, Technical University of Crete. His work focuses on computational intelligence, optimization algorithms, and their applications in education and product design. He holds a fixed-term research position and is based in Office G3.0.01, Building G3. Research Interests: His primary areas include developing nature-inspired optimization algorithms (e.g., flying fox, mayfly, and bees algorithms), applying computational methods to educational challenges like student psychological fitness assessment and group formation, and optimizing product line design using metaheuristics like Tabu Search and Differential Evolution. He also investigates the impact of ICT on education and special education teacher efficacy. Publications Trends: Recent works emphasize hybrid optimization algorithms for real-world problems (maintenance scheduling, product design), AI-driven educational tools for mental health assessment and learning grouping, and algorithmic solutions for multi-objective decision-making. His research bridges computational innovation with practical applications in education and engineering. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No student advisees listed. No grants disclosed in the data.

• First principle kinetics
• Molecular dynamics simulations
• Nanoporous materials
• +۷ مورد دیگر

Veronique Van Speybroeck is a Full Professor at Ghent University within the Faculty of Engineering and Architecture, a position she has held since October 2012. She also serves as Research Professor at Ghent University since October 2007 and is the Head of the Center for Molecular Modeling (CMM), which she co-founded in 2000. The CMM brings together approximately 40 researchers from the faculties of Science and Engineering and Architecture to model molecules, materials, and processes at the nanoscale. Van Speybroeck graduated as an engineer in physics from Ghent University in 1997 and obtained her Ph.D. in 2001 on theoretical simulations of chemical reactions. After her Ph.D., she received a postdoctoral fellowship from the National Fund for Scientific Research Flanders and conducted research at various foreign institutes. Her research focuses on first principle kinetics and molecular dynamics simulations of complex chemical transformations in nanoporous materials, particularly zeolites, Metal-Organic Frameworks (MOFs), and Covalent Organic Frameworks (COFs). She has built substantial expertise in modeling nanoporous materials for catalysis and adsorption, with applications inspired by and performed in close collaboration with experimental groups. Her work strongly emphasizes modeling realistic materials and processes under operating conditions. Analysis of her recent publications reveals a strong focus on computational modeling of reticular materials, with particular emphasis on machine learning potentials for accurate simulations, understanding confined water behavior in zeolites, and developing photocatalytic frameworks for environmental applications. Her research increasingly integrates advanced computational techniques with experimental validation to tackle complex problems in materials science and catalysis. Dr. Karl Wamsler innovation award (2023) Francqui prize in exact sciences (2024) ERC Starting grant (2010) ERC Consolidator grant (2015) ERC Advanced grant (2025) Full member of the Royal (Flemish) Academy for Science and the Arts of Belgium (KVAB) Van Speybroeck leads a research team comprising approximately 5 postdoctoral researchers, 13 PhD students, and 5-10 Master's students annually. Her research has been supported by multiple European Research Council grants, including an ERC Starting grant in 2010, an ERC Consolidator grant in 2015, and an ERC Advanced grant in 2025. She also participates in various activities to enhance the impact of science on society, having served on the STEM-platform from 2012-2017 to promote Science, Technology, Engineering, and Mathematics training and careers. The Center for Molecular Modeling (CMM), which Van Speybroeck heads, is a multidisciplinary research group composed of about 40 researchers. The CMM's mission is to model molecules, materials, and processes at the nanoscale by bringing together physicists, chemists, and (bio-)engineers. This collaborative approach is central to the CMM's scientific excellence in molecular modeling.

• Soft Robotics
• Robotics Control Systems
• Adaptive Control
• +۷ مورد دیگر

Hamed RAHIMI NOHOOJI is a Postdoctoral researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the Automation department under Prof. Holger Voos's research group. He holds a Ph.D. from Curtin University (Australia, 2018) and has held research positions at the National University of Singapore, UC Louvain, University of Pisa, and the University of Birmingham. His research focuses on soft robotics , adaptive control systems , and human-robot interaction , with notable contributions to projects like the EU H2020 CYBERLEGs Plus Plus initiative and Singapore's A*STAR-funded soft gripper development. His work spans neuroadaptive control , reinforcement learning , and fault-tolerant systems , with applications in space robotics, wind turbine control, and collaborative robotics. With over 900 citations and an H-index of 18 (Google Scholar, 2023), his publications appear in top journals like Mechanical Systems and Signal Processing (IF 8.934) and Neurocomputing (IF 5.779). He has authored four Springer book chapters and served as a guest editor for journals including Frontiers in Robotics and AI and IEEE Transactions on Industrial Electronics . Research interests include: Soft Robotics : Design of compliant actuators, jamming grippers, and topology-optimized soft mechanisms Control Systems : Barrier Lyapunov functions, Nussbaum gain techniques, and neuroadaptive methods Human-Machine Collaboration : Adaptive trajectory optimization for safe human-robot interaction Space Applications : Soft robotics for extraterrestrial missions and compliant systems for space environments His recent articles emphasize constrained control systems , reinforcement learning for robotics , and lightweight gripper design for aerial platforms . His work often bridges theoretical control principles with practical robotic implementations in dynamic environments. Scientific achievements include a Student Travel Award at the 2016 Australasian Conf on Robotics and Automation. He has edited topical collections on Human-Robot Interaction and Soft Robotics , reflecting his leadership in shaping the field's research directions. Labs/Teams: Member of the Automation & Robotics Research Group at SnT, collaborating with Prof. Holger Voos and international partners on EU-funded projects.

Pamela Abshireمشاهده پروفایل

استاد

• VLSI Circuit Design
• Bioengineering
• Information Theory
• +۷ مورد دیگر

Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. She holds the rank of Fischell Institute Fellow and is affiliated with the Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. Her work bridges VLSI circuit design and bioengineering, focusing on performance-resource tradeoffs in natural/engineered systems. Education: B.S. Physics (Caltech, 1992), M.S. and Ph.D. in Electrical Engineering (Johns Hopkins University, 1997 and 2001). Pre-UMD career included R&D roles at Medtronic (1992-1995). Research focuses on CMOS biosensors, low-power microsystems, and bio-inspired designs for applications like cell-based sensing, robotics, and medical devices. Notable projects include nose-on-a-chip odor detection systems, ant-like microrobots, and lab-on-CMOS platforms for real-time cell monitoring. Awards include IEEE Fellow (2018), NSF CAREER Award (2003), and 2021 University Distinguished Scholar-Teacher honor. Active in academic leadership roles including ADVANCE Professor (2020-2021) and editorial work for IEEE Transactions on Circuits and Systems. Grants include NSF funding for olfactory sensing, AFOSR bio-inspired flight tech, and DARPA CogniSense initiatives. Her Integrated Biomorphic Information Systems Lab collaborates on semiconductor innovation through partnerships like the Mid-Atlantic Semiconductor Collaborative. Labs/Teams: Leads the Integrated Biomorphic Information Systems Lab and contributes to Microelectronics at Maryland group. Co-develops biohybrid systems integrating CMOS, MEMS, and biological components.

Uta Bohnebeckمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr.-Ing. Uta Bohnebeck is a faculty member at Bremen University of Applied Sciences under Faculty 4 . Her research focuses on interdisciplinary applications of data science and artificial intelligence, particularly in additive manufacturing, bio-inspired systems, and sustainable energy solutions. Current projects include Federated learning for additive manufacturing and quality enhancement (2025-2026) and Development of a data science analysis infrastructure (2020-2021). She has been involved in initiatives like the Science Year on AI and FH-Impuls collaborations. Her work intersects with the CINNAMON research group, though no specific awards, students, or publications are detailed in the provided text.

• Nonlinear optics
• Fiber optic sensors
• Optical neural networks
• +۴ مورد دیگر

José Rodrigo Paredes Miguel is a Predoctoral Researcher at the Technological Research Institute (IIT) of the Higher Technical School of Engineering, Comillas Pontifical University, Spain, specializing in Artificial Intelligence since his March 2025 appointment. He earned a Bachelor's degree in Communications and Electronics Engineering and a Master's in Electrical Engineering from the University of Guanajuato, Mexico, with thesis work on optical locomotor control systems and neural network-based material segmentation. His research integrates nonlinear optics, computer vision, and generative models to develop bio-inspired AI solutions, particularly for photonics and electrical fault diagnosis. Recent work demonstrates expertise in optical neural networks and deep learning for precision manufacturing applications. His 2025 publication in Advanced Photonics Research establishes a framework for using deep neural networks to optimize femtosecond laser parameters in molybdenum oxide synthesis, reflecting his focus on AI-driven photonics innovation. He contributes to the PREDICT-MAINTENANCE project (2024-2027) funded by the Union Internationale des Chemins de Fer, developing data-driven maintenance protocols for railway electrical infrastructure through machine learning approaches.

• Evolutionary Algorithms
• Machine Learning
• Optimization
• +۷ مورد دیگر

Giovanni Iacca is an Associate Professor at the University of Trento's Department of Information Engineering and Computer Science (DISI), where he serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. He leads the Distributed Intelligence and Optimization Lab (DIOL) and teaches courses including Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, and Optimization Techniques across multiple academic programs. PhD in Computer Science, University of Jyväskylä, Finland (2011) MSc in Computer Engineering, Technical University of Bari, Italy (2006) Professor Iacca's research focuses on the intersection of evolutionary computation, machine learning, and optimization with applications in distributed systems and robotics. His work spans from theoretical foundations of memetic computing and multi-objective optimization to practical implementations in soft robotics, embedded systems, and healthcare applications. Recent efforts emphasize interpretable AI, particularly in reinforcement learning contexts, where his team develops methods to make decision processes transparent while maintaining performance. His research bridges the gap between fundamental algorithmic development and real-world engineering challenges, with over 15 years of industrial experience in optimization applied to engineering, logistics, and scheduling. Analysis of his recent publications reveals a strong trend toward interpretable AI systems, particularly in reinforcement learning contexts, with significant contributions to federated learning optimization, evolutionary neural architecture search, and applications in healthcare scheduling. His work consistently combines evolutionary algorithms with modern machine learning techniques to solve complex optimization problems across diverse domains including soft robotics, batteryless edge computing, and supply chain management. Scientific Awards: EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) Professor Iacca actively supervises a large research group with numerous PhD students across multiple doctoral programs, including Information Engineering and Computer Science, Industrial Innovation, and the National PhD in Artificial Intelligence for Society. His lab has secured significant research funding through collaborations with industry partners and international research consortia. Recent grants support work on interpretable reinforcement learning, federated optimization, and applications of evolutionary computation in healthcare and robotics. He has also been appointed to editorial roles for prestigious journals including IEEE Transactions on Evolutionary Computation and Evolutionary Intelligence. The Distributed Intelligence and Optimization Lab (DIOL) under Professor Iacca's leadership comprises over 30 researchers including postdocs, PhD students, and master's students. The lab maintains strong international collaborations and has developed specialized expertise in evolutionary computation, interpretable AI, and optimization for embedded systems. Current projects include work on the EIC Pathfinder Challenge "Awareness Inside," development of methods for batteryless edge intelligence, and applications of evolutionary algorithms to healthcare scheduling problems.

• Biomedical Engineering
• Wearable Systems
• Neural Networks
• +۷ مورد دیگر

Paolo Motto Ros is a Researcher specializing in biomedical engineering, wearable systems, and low-power electronics. He has extensive experience in event-driven signal processing , functional electrical stimulation , and biocompatible sensor design , with a focus on human-machine interfaces and implantable devices. His research interests include: Biomedical instrumentation Wireless power/data transmission Surface electromyography (sEMG) Low-complexity embedded systems Plant impedance monitoring Neuroprosthetics Recent publications highlight collaborations with institutions on piezoelectric skin sensors , CMOS neural implant circuits , and plant health monitoring systems . His work spans applications in healthcare, robotics, and environmental technology.