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Kalle ÅströmView profile

Professor

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +5 more

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.

• Mathematical Data Science
• Machine Learning
• Compressed Sensing
• +5 more

Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.

• Cybersecurity
• Privacy
• Industrial Networks
• +7 more

Martin Henze is a tenure-track Assistant Professor at RWTH Aachen University's Department of Computer Science, where he leads the Security and Privacy in Industrial Cooperation (SPICe) research group. Additionally, he co-leads the Secure Production & Energy Networks research group at the Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE in Bonn, Germany. His work bridges academic research with practical industrial security applications, focusing on critical infrastructure protection. Dr. Henze's research interests center on technical security and privacy aspects of industrial networks and data sharing, with special emphasis on energy and production sectors. His work spans industrial intrusion detection, 5G security for industrial applications, IoT security in constrained environments, and blockchain security. He develops practical security solutions that balance protection needs with the resource constraints and operational requirements of industrial systems, particularly focusing on making security both effective and comprehensible for operators. His recent publications demonstrate a strong focus on industrial security challenges, with particular emphasis on intrusion detection systems that maintain operator control, TLS optimization for resource-constrained industrial IoT, 5G security for production systems, and novel approaches to securing legacy industrial protocols. His work consistently addresses the tension between security requirements and operational constraints in industrial settings. Nachwuchsförderpreis Verbraucherforschung NRW Borchers-Plakette ICT Young Researcher Award Dr. Henze actively contributes to the academic community through service on numerous prestigious program committees including ACM CCS, IEEE S&P, NDSS, and USENIX Security. His teaching portfolio includes graduate courses on Industrial Data Security, Industrial Network Security, and specialized seminars on 5G/6G Security and IoT Security. His research is highly collaborative, frequently involving partnerships across institutions and with industry to address real-world security challenges in critical infrastructure. He heads the SPICe research group at RWTH Aachen, which focuses on developing practical security and privacy solutions for industrial cooperation scenarios. The group's work emphasizes creating security mechanisms that are not only technically sound but also comprehensible and usable by industrial operators, recognizing that the human element is critical in maintaining security in complex industrial environments.

• Computer Architecture
• Embedded Systems
• Machine Learning Accelerators
• +5 more

Marina Zapater Sancho is a researcher at the Embedded Systems Laboratory (ESL) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL) . She specializes in computer architecture, with a focus on energy-efficient systems, AI accelerators, and memory-centric computing paradigms. Research Interests: Compute-Near-Memory (CnM) : Pioneering architectures like SideDRAM and processing-near-bank designs to reduce energy consumption and latency in DRAM systems. AI Accelerators : Frameworks such as LIONHEART for analog-digital hybrid systems, and Gem5-AcceSys for exploring interconnects in ML accelerators. System Simulation : Contributions to RISC-V full-system simulation validation (gXR5) and component-level calibration methodologies. Her work bridges software and hardware, addressing challenges in heterogeneous systems, thermal management in 2.5D/3D packages, and virtual memory optimization for cache-intensive workloads. Recent trends emphasize energy-proportional computing and edge AI deployment . Grants & Collaborations: Funded by EU H2020 programs and the ACCESS-AI Chip Center (Hong Kong), her research is conducted within the ESL team led by Prof. David Atienza Alonso.

Ørjan Grøttem MartinsenView profile

Professor

• Bioimpedance
• Biomedical Physics
• Electronics and Instrumentation
• +3 more

Ørjan Grøttem Martinsen is a Professor of Electronics at the Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo. He also holds a temporary research position at the Medical Technology Business Area of Oslo University Hospital. With over three decades of experience, he has established himself as a leading expert in bioimpedance research and applications. Education: High-voltage engineer degree (1983) Cand. scient. in electronics/measurement technology (1990) Dr. scient. with thesis on skin's electrical properties (1995) Professor Martinsen's research centers on bioimpedance—the passive electrical properties of biological tissues that vary with anatomy and physiology. His work spans diverse applications including medical diagnostics (skin cancer detection), food quality assessment (fresh vs. thawed fish), skin condition monitoring (moisture levels), and stress level evaluation. His research bridges physics, engineering, and medical applications, creating practical diagnostic tools from fundamental electrical principles. He has pioneered methods to characterize tissue properties through impedance measurements, with particular focus on electrodermal activity and skin impedance. His recent publications (2022-2025) demonstrate a strong interdisciplinary approach combining bioimpedance with machine learning, robotics, and advanced signal processing. The work spans from fundamental biophysics (GABA detection, tissue characterization) to practical applications (dental anxiety assessment, ADHD treatment evaluation). Key trends include integration of AI with bioimpedance measurements, development of novel sensor systems, and expansion into new application areas like optogenetics and micro-robotics. Awards and Recognition: IEEE Senior Member (2006) CLABIO Award (2012) Fellow at Institute of Physics (FInstP) (2015) Dr. Honoris Causa, Tallinn University of Technology (2018) UiO Innovation Award (2019) Member of Norwegian Academy of Technical Sciences (2021) Professor Martinsen has served as Editor-in-Chief of the Journal of Electrical Bioimpedance since 2010 and was President of the International Society for Electrical Bioimpedance (2010-2016). His research has attracted significant funding, enabling collaborations across engineering, medical, and biological disciplines. He has supervised numerous students and researchers in the Bioimpedance Group at UiO, fostering a strong research environment that bridges theoretical and applied work. His work is conducted primarily through the Oslo Bioimpedance Group and Sensorama SmartSense research teams, which focus on developing innovative measurement techniques and applications of bioimpedance technology. These groups maintain strong collaborations with medical institutions and industry partners to translate research findings into practical healthcare solutions.

Dibyendu MukherjeeView profile

Associate Professor

• Digital Signal Processing
• Wireless Communications
• Embedded Systems
• +3 more

Dibyendu Mukherjee is an Associate Professor at the University of Miami , affiliated with the College of Engineering . His research focuses on areas including digital signal processing, wireless communications, and embedded systems, with applications in machine learning and IoT technologies. Contact: Email: dxm1936@miami.edu ORCiD: 0000-0002-9232-6774 Research Focus: Digital Signal Processing Wireless Communications Embedded Systems Machine Learning Applications Network Security Internet of Things (IoT)

Kaiwen ChenView profile

Assistant Professor

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +6 more

Dr. Kaiwen Chen serves as an Assistant Professor in the Department of Civil, Construction and Environmental Engineering at The University of Alabama's College of Engineering, where she is affiliated with the Center for Sustainable Infrastructure. Her research integrates drone robotics, sensor technologies, and Artificial Intelligence to revolutionize building diagnostics and performance simulation. Her academic credentials include: Ph.D. in Environmental Design and Planning from Virginia Polytechnic Institute and State University (2020) M.Sc. in Management in Science and Technology from Southeast University (2016) B.S. in Construction Project Management from Southeast University (2013) Dr. Chen's research program focuses on innovations in the AECO field, with core expertise in drone-based imaging systems, 2D/3D data processing, infrared thermography, high-performance computing, and building energy modeling. Her work bridges advanced computational techniques with practical infrastructure challenges, particularly in building envelope diagnostics and pavement inspection. Analysis of her 15 most recent publications (2024-2025) reveals dual research thrusts: primary focus on AI-driven construction applications (digital twins, thermal anomaly detection, UAV-based surveys) and significant contributions to wireless power transfer systems. This interdisciplinary scope demonstrates exceptional versatility in applying cutting-edge computational methods to both civil infrastructure and electrical engineering challenges. Her scientific recognition includes: Runner-Up for 5th Annual ASCE VIMS Datathon Competition (2024) Virginia Tech Outstanding Dissertation Award (2020) ASCE i3CE Best Paper Award (2019) Dr. Chen leads externally funded research initiatives including a US Department of Energy project on aerial intelligence for building envelope diagnostics and a Georgia Department of Transportation project on drone-assisted pavement inspection. These grants demonstrate her ability to secure competitive funding for high-impact infrastructure research. As an active contributor to the Center for Sustainable Infrastructure, she advances research in sustainable infrastructure systems through the integration of drone technologies, AI analytics, and digital twin methodologies for comprehensive infrastructure assessment and management.

Fadi AL-TURJMANView profile

Professor

• Artificial Intelligence of Things (AIoT)
• Wireless Sensor Networks
• Blockchain Applications
• +7 more

Prof. Dr. Fadi AL-TURJMAN serves as the founding Dean of the Faculty of AI and Informatics at Near East University (NEU), Cyprus. He holds multiple leadership roles including Head of the Software Engineering Department and Director of the AI and Robotics Institute and the International Research Center for AI and IoT. With a PhD in Computer Science from Queen’s University (2011), he specializes in AIoT systems, wireless networks, and blockchain integration. Affiliation: Near East University Leadership: Founding Dean for AI and Informatics, Director of AI & Robotics Institute Research Focus: His work bridges Artificial Intelligence of Things (AIoT) , Blockchain Applications , and Smart Networking . He explores cybersecurity frameworks for smart cities, quantum state optimization techniques, and novel AI-driven solutions for healthcare, agriculture, and energy systems. Key Article Trends: Recent publications emphasize Transformer models for environmental monitoring, blockchain-enabled security protocols , and evolutionary algorithms for resource optimization. His research spans interdisciplinary domains including medical diagnostics, vehicular networks, and sustainable infrastructure. Scientific Awards: Lifetime Golden Award of Dr. Suat Gunsel (2022) Multiple Best Research Awards at International Venues Labs & Teams: Directs the International Research Center for AI and IoT at NEU, leading multidisciplinary teams in developing advanced networking technologies and AI-driven solutions for global value chain applications.

Joshua MarshallView profile

Associate Professor

• Robotics
• Field Robotics
• Mobile Robotics
• +5 more

Joshua Marshall is an Associate Professor in the Department of Electrical and Computer Engineering at Queen's University, cross-appointed to Mechanical and Materials Engineering. He serves as Interim Director of Ingenuity Labs Research Institute and leads the Offroad Robotics research group (formerly Mining Systems Laboratory). Previously, he held positions at Carleton University and MDA, Inc. Education: PhD in Electrical and Computer Engineering, University of Toronto (specializing in systems control) Research Interests: Dr. Marshall specializes in field robotics for mining, space, and industrial applications. His work integrates control systems engineering , localization and mapping , and mechatronics to develop autonomous solutions for harsh environments. Key focus areas include mobile robot navigation, sensor fusion, and real-world deployment challenges in underground and extraterrestrial settings. Publication Trends: His 2022-2025 publications reveal dominant themes in autonomous industrial vehicle control (motor graders, excavators), marine robotics (uncrewed surface vessels), and terrain/material classification . Work consistently combines model predictive control with machine learning (Gaussian processes, reinforcement learning) for dynamic environment adaptation. Multi-robot systems and simulation-to-real transfer represent emerging research directions. Professional Recognition: Senior Member of IEEE Associate Editor, IEEE Control Systems Society Conference Editorial Board Senior Editor, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Research Impact: Dr. Marshall's Offroad Robotics group develops technologies featured in the Canada Science and Technology Museum's 'From Earth to Us' exhibit. His work bridges academic research with industry applications through collaborations with MDA and international institutions like Örebro University, though specific grant details are not provided in source materials. Leadership: He directs both the Offroad Robotics research group and Ingenuity Labs Research Institute, fostering interdisciplinary innovation in robotics, autonomous systems, and intelligent technologies for real-world implementation in mining, space exploration, and environmental monitoring.

• Advanced Device Technologies
• Materials Science
• Nanoengineering
• +4 more

Olin Hartin serves as a Professor of Practice in Arizona State University's School of Electrical, Computer and Energy Engineering, leveraging over 30 years of Fortune 500 industry experience in science and technology. Based at the Tempe campus (GWC 340, Mailcode 5706), he maintains an active research profile with 25 patents and more than 60 scholarly publications. His academic credentials include: Ph.D. in Electrical Engineering M.S. in Electrical Engineering M.S. in Physics B.S. in Physics Hartin's research centers on advanced device technologies and materials, with demonstrated expertise in nanoengineering, RF circuit design, and semiconductor physics. His work bridges theoretical modeling with practical fabrication challenges, particularly in gallium nitride transistor development and electromagnetic compatibility. Recent projects address thermal management in high-power devices and noise isolation techniques for mixed-signal integrated circuits, driven by industry applications in wireless infrastructure. Analysis of his publication history reveals sustained focus on GaN HEMTs since 2010, with increasing emphasis on machine learning applications for FPGA deployment. His work consistently targets real-world implementation challenges, reflecting his industry background through patents in antenna design, ESD protection, and RF component optimization. Professional recognition includes: Senior Member of IEEE In teaching, Hartin supervises senior design laboratories (EEE 488/489) and instructs core courses including Hardware Design Language/Programming Logic (EEE 333), Circuits II (EEE 334), and Machine Learning with FPGA Deployment (EEE 405). His industry perspective enriches curriculum development, though specific grant funding details are not publicly documented. While no dedicated research lab is specified, his patent portfolio indicates ongoing collaboration with semiconductor industry partners.

Youngmoo KimView profile

Professor

• Machine Learning
• Audio Signal Processing
• Human-Computer Interaction
• +5 more

Youngmoo Kim is Professor of Electrical and Computer Engineering at Drexel University, where he directs the Expressive and Creative Interactive Technologies (ExCITe) Center. His leadership extends to the Music & Entertainment Technology Laboratory (MET-lab), focusing on machine listening, robotics for expressive interaction, and STEM education through music technology. Education: PhD, Media Arts & Sciences, MIT (2003) MS, Electrical Engineering, Stanford University MA, Music, Stanford University BS, Engineering, Swarthmore College BA, Music, Swarthmore College Research Focus: Kim's interdisciplinary work bridges audio signal processing, machine learning for music information retrieval, human-robot interaction, and acoustic synthesis. His lab develops novel interfaces including brain-controlled robotics, augmented instruments, and AI-driven music systems, while championing K-12 STEAM education through technological innovation. Publication Trends: Recent articles demonstrate strong convergence of deep learning with music technology, particularly in neural audio synthesis, instrument recognition, and robotic control systems. Emerging themes include differentiable physical modeling of instruments, neuroadaptive interfaces using fNIRS/tDCS, and computational approaches to creative collaboration. Awards & Honors: 2021 College of Engineering Inclusive Excellence Award 2013 Apple Distinguished Educator 2012 Lindback Foundation Distinguished Teaching Award 2012 Philadelphia Geek Awards Scientist of the Year 2007 NSF CAREER Award Leadership & Funding: Kim has secured research support from the National Science Foundation and Knight Foundation. His ExCITe Center serves as an interdisciplinary hub for creative technology development, while MET-lab advances audio-machine intelligence through collaborations across engineering, computer science, and performing arts.

Samir ChatterjeeView profile

Professor

• Design Science Research
• Health Informatics
• Machine Learning in Healthcare
• +5 more

Samir Chatterjee is Professor and Fletcher Jones Chair of Design, Technology & Management at Claremont Graduate University's Center for Information Systems & Technology (CISAT). He joined CGU in July 2001 and founded IDEA Labs (formerly Network Convergence Laboratory), which conducts cutting-edge research at the intersection of technology and healthcare. His educational background includes a PhD and MS in Computer Science from the University of Central Florida and a BE in Electronics and Telecommunications Engineering from Jadavpur University, India. He also holds an executive education certificate on Leadership in Digital Health from Harvard Medical School. Dr. Chatterjee's research spans design science methodology, health informatics, and persuasive technologies, with particular focus on machine learning applications for healthcare, chronic disease management, and telemedicine. His work has significantly influenced both academic research and practical healthcare technology development. His publications demonstrate consistent focus on design science methodology and healthcare technology applications, showing evolution from network engineering to healthcare informatics. Recent work emphasizes machine learning approaches to solve healthcare problems including cancer caregiver burden, stroke prediction, and COPD management. Distinguished lifetime achievement award for contributions to Design Science Research (2015) Schoeller Senior Fellow 2017 Mahatma Gandhi Pravasi Samman (2017) Best Paper Award at DESRIST 2016 Dr. Chatterjee actively mentors doctoral students through IDEA Labs, with 4-8 students typically working on dissertation research. His research has been funded by prestigious organizations including the National Science Foundation, National Institutes of Health, and Internet2. He also co-founded VoiceCore Technologies Inc. (2001) and DCL Health (2013), a wireless healthcare startup. As Founding Director of IDEA Labs, he leads a multidisciplinary team working on projects related to human flourishing, cancer caregiver support, stroke prediction, and COPD mHealth applications. The lab maintains strong connections with the Southern California venture capital and entrepreneurial startup community.

Pedram JohariView profile

Researcher

• Intra-body Communications
• Wireless Networking
• Internet of Medical Things
• +4 more

Pedram Johari serves as a Principal Research Scientist at Northeastern University's Institute for Wireless Internet of Things, working under Prof. Tommaso Melodia in the Department of Electrical and Computer Engineering within the College of Engineering. His research focuses on next-generation wireless communications with particular emphasis on medical applications and nanoscale networks. Dr. Johari earned his Ph.D. in Electrical Engineering from the University at Buffalo, State University of New York in 2018 under the supervision of Prof. Josep M. Jornet. Prior to joining Northeastern, he served as CTO of an IoT-tech startup in New York (2018-2019) and held academic positions at University at Buffalo as Adjunct Instructor and Research Assistant Professor (by courtesy appointment). His research spans four interconnected domains: Intra-body Communications and Networking focusing on nanoscale electromagnetic and optical communications within biological tissues; Internet of Medical Things developing wireless systems for healthcare applications; Low Power Wireless IoT creating energy-efficient communication protocols; and Vehicular Communications advancing cooperative driving systems. His work bridges theoretical modeling with practical implementation, often incorporating AI techniques for network optimization. Analysis of his recent publications reveals a strong trend toward digital twin technology for wireless networks, AI-enabled communication systems, and medical applications of nanoscale communications. His research increasingly integrates machine learning with traditional communication theory, particularly in Open RAN systems and seizure prediction technologies, demonstrating cross-disciplinary impact across engineering, computer science, and biomedical fields. Best Paper Award at IEEE Global Communications Conference (GLOBECOM) Best Short Paper Award at IEEE Vehicular Networking Conference (VNC) Best Presentation Award at IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS) Dr. Johari actively contributes to academic service as a reviewer for numerous prestigious journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Internet of Things Journal. He has served on technical program committees for major conferences including IEEE/ACM CHASE and IEEE SECON. His teaching experience includes courses in programming, circuit analysis, and digital principles at University at Buffalo. As a key member of the Wireless Networks and Embedded Systems Lab at Northeastern University, Dr. Johari contributes to the Colosseum wireless network emulator project - recognized as the world's largest wireless network emulator. His work with the Institute for Wireless Internet of Things involves collaboration with industry partners to translate research into practical wireless communication solutions.

Kimin LeeView profile

Assistant Professor

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +9 more

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

Taekwang JangView profile

Associate Professor

• Analog and Mixed-Signal Circuits
• Energy-Efficient Systems
• Wireless Sensor Networks
• +3 more

Prof. Taekwang Jang is an Associate Professor at the Department of Information Technology and Electrical Engineering, ETH Zürich. He leads the Energy Efficient Circuits and IoT Systems Group, focusing on analog and mixed-signal circuits for energy-constrained applications such as wireless sensor nodes and biomedical electronics. His research includes sensor interfaces, energy harvesters, power converters, and communication systems. He holds 15 patents and has authored over 80 peer-reviewed publications. Key awards include the 2024 IEEE Solid-State Circuits Society New Frontier Award and the SNSF Starting Grant. Educations: B.S. and M.S. in Electrical Engineering, KAIST (2006, 2008) Ph.D. in Electrical Engineering, University of Michigan (2017) Affiliations: Chair of IEEE Solid-State Circuits Society, Switzerland Chapter Associate Editor for Journal of Solid-State Circuits (JSSC) Research Interests: His work spans energy-efficient integrated circuits, biomedical interfaces, and IoT systems. Notable contributions include low-power keyword spotting ICs, ultra-low-noise amplifiers, and neural stimulation systems. He emphasizes practical applications in healthcare and wearable devices. Awards: 2024 IEEE Solid-State Circuits Society Distinguished Lecturer 2022 IEEE ISSCC Jan Van Vessem Award 2009 IEEE CAS Guillemin-Cauer Best Paper Award Advising & Grants: Supervises a team of researchers and has secured grants including the SNSF Starting Grant. His lab collaborates with institutions like the Competence Center for Rehabilitation Engineering and Science. Labs & Teams: Leads the Energy-Efficient Circuits and Intelligent Systems group at ETH Zurich, focusing on interdisciplinary projects at the intersection of circuits, systems, and biomedical engineering.