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Yao Wen ChangView profile

Professor

• Electronic Design Automation (EDA)
• Integrated Circuit Physical Design
• Heterogeneous Integration
• +3 more

Yao Wen Chang is a Professor at National Taiwan University (NTU) , with joint appointments in the Graduate Institute of Electronics Engineering , Department of Electrical Engineering , and Department of Computer Science and Information Engineering . His research focuses on Electronic Design Automation (EDA) , particularly physical design, 2.5D/3D heterogeneous integration, design for manufacturing/reliability, EDA for AI, and analog circuit automation. He leads the EDA Lab , contributing to both academic and industrial advancements in nanometer ICs and semiconductor technologies. Education : Ph.D., Department of Computer Sciences, The University of Texas at Austin (1996) M.S., Department of Computer Sciences, The University of Texas at Austin (1993) B.S., Department of Computer Science and Information Engineering, National Taiwan University (1988) Research Trends : His recent work bridges AI and EDA, emphasizing co-design across board, package, and chip domains. He also explores design automation for analog circuits and combinatorial optimization challenges in nanometer-scale ICs. Scientific Awards : Recipient of the TECO Award (2017) NSC Distinguished Research Award (2011) NTU Distinguished Teaching Award (2020, 2013) Ministry of Science and Technology Academic Award (2022) Chinese Institute of Electrical Engineering Medal (2023) Labs & Teams : He directs the EDA Lab at NTU, which develops cutting-edge tools and methodologies for semiconductor design. The lab’s work includes open-source code and participation in international design contests.

Xiaoxuan YangView profile

Assistant Professor

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +3 more

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

• Antenna Design
• Wireless Communications
• Millimeter-Wave Technologies
• +4 more

Chinthana Panagamuwa is a Senior Lecturer in Wireless Communications at Loughborough University, where he holds a PhD in Electronic and Electrical Engineering. His academic journey includes a first-class honours degree from the same institution, followed by post-doctoral research on radio-frequency antenna interactions with the human body. He has served as Director of Undergraduate Studies for Electrical programmes for seven years and organized the Loughborough Antennas and Propagation Conference (LAPC) from 2013 to 2016. Chinthana has graduated four PhD students and acted as an external examiner for institutions like Queen Mary University of London and the University of Bristol. His research focuses on advanced antenna systems, including millimeter-wave communications, beam steering technologies, and bioelectromagnetics. Key contributions include reconfigurable antennas for 5G/6G networks, dielectric lens design, and implanted antenna applications in medical monitoring. His work spans theoretical innovations to practical implementations, leveraging 3D-printing and electromagnetic simulations. Awardees include MIEEE (Member of IEEE). His publications demonstrate expertise in antenna design, RF engineering, and biomedical RF applications. Beyond academia, he has contributed to industry-relevant solutions like low-SAR antennas for wearable devices and MIMO systems for smartphones. Chinthana’s research emphasizes interdisciplinary collaboration, addressing challenges in both communication infrastructure and healthcare. His recent trends include exploring 6G beamforming techniques, dielectric lens optimization for millimeter-wave systems, and advancing antenna reconfigurability for dynamic environments.

Bulent SarliogluView profile

Associate Professor

• Electric Machines
• Power Electronics
• Drives
• +5 more

Bulent Sarlioglu is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison, with additional affiliations in Mechanical Engineering. His research focuses on advancing electric machines, power electronics, and drive systems for applications in hybrid/electric vehicles, renewable energy, aerospace, and naval systems. He specializes in optimizing efficiency, power density, and thermal management through innovative material usage and topology design. Education: PhD 1999, University of Wisconsin-Madison MS 1992, University of Missouri-Columbia BS 1990, Istanbul Technical University Research interests include: Electric machines and drives Current-source inverters (CSIs) High-power-density integrated systems Thermal management via additive manufacturing Fault-tolerant modular designs Wide bandgap semiconductor applications His work emphasizes practical implementation, with contributions to cooling technologies (e.g., in-core cooling for YASA motors) and high-speed propulsion systems. Over 20+ awards highlight his impact, including the IEEE PELS Outstanding Achievement Award (2024) and NSF CAREER Award (2016). Teaching includes advanced research supervision (ECE/ME 790-990 courses) and circuit analysis at both undergraduate and graduate levels.

Natalia AlguacilView profile

Professor

• Power Systems
• Optimization
• Smart Grids
• +3 more

Natalia Alguacil is a Full Professor at the University of Castilla-La Mancha, affiliated with the Higher Technical School of Industrial Engineering in Ciudad Real, Spain. Her research focuses on power systems optimization, smart grid technologies, and renewable energy integration, with an emphasis on robustness, demand response mechanisms, and decentralized optimization frameworks. She has contributed significantly to topics such as transmission network expansion, energy storage systems, and electricity market design through advanced methodologies like bilevel programming and ADMM-based approaches. Her work addresses critical challenges in modern grid operation, including renewable integration uncertainties, fair prosumer incentives, and grid resilience. Key contributions include frameworks for optimal residential demand response coordination and robust scheduling under wind energy variability. Her research bridges theoretical advancements with practical implementation, influencing both academic discourse and industry practices. Natalia has authored over 40 peer-reviewed articles, many published in top journals like IEEE Transactions on Smart Grid and Power Systems. She has led projects on transmission tariff allocation, grid observability restoration, and defense planning against deliberate outages. Her educational efforts include pioneering problem-based learning curricula in engineering education.

• Electronic Design Automation
• Computer-Aided Design
• Analog/Mixed-Signal Circuit Design
• +7 more

David Z. Pan is a Professor at the University of Texas at Austin, where he leads a prominent research group specializing in Electronic Design Automation (EDA) and Computer-Aided Design for Integrated Circuits. His extensive publication record spanning from 1997 to the present demonstrates his leadership in advancing the field of electronic design. Dr. Pan's research focuses on solving fundamental challenges in analog/mixed-signal circuit design automation, physical design methodologies, and the integration of machine learning techniques with traditional EDA problems. His work bridges theoretical advances with practical applications in semiconductor design, with particular emphasis on photonic computing, quantum circuit design, and FPGA optimization. His research has evolved from traditional layout and placement algorithms to incorporate cutting-edge AI and machine learning approaches for next-generation design automation. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with EDA, including the use of large language models for circuit design automation, reinforcement learning for placement optimization, and deep learning for various aspects of the design flow. His work consistently addresses critical industry challenges while pushing the boundaries of what's possible in electronic design. Dr. Pan has advised numerous graduate students who have become significant contributors to the field, with many continuing their research careers in academia and industry. His research group has developed several influential tools and methodologies that have been adopted by both academic and industrial researchers. He actively contributes to major conferences in the field including ICCAD, DAC, ASP-DAC, and ISPD, often presenting invited talks that shape the future direction of EDA research. His work on open-source EDA tools has been particularly impactful, promoting accessibility and reproducibility in electronic design research.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +7 more

Michael S. Horn is a Professor in the Department of Computer Science and the Learning Sciences at Northwestern University's School of Education and Social Policy, with additional affiliation at Tufts University's Computer Science department. His interdisciplinary work bridges Human-Computer Interaction, Learning Sciences, and Educational Technology, focusing on creating innovative learning experiences across formal classrooms, museums, and informal settings. His research encompasses: Design of tangible and embodied learning interfaces Computational thinking and programming education Museum and informal learning environments Integration of music and coding in educational contexts Application of artificial intelligence in educational settings Children's understanding of complex scientific concepts Horn's recent work demonstrates a significant shift toward exploring the intersection of artificial intelligence and education, particularly how Large Language Models can support learning processes and research methodologies. His publications reveal consistent attention to making abstract computational concepts accessible through concrete, embodied interactions that connect learners with meaningful content. His notable scientific contributions include: Development of the NetLogo programming environment for agent-based modeling in education Creation of museum exhibits like DeepTree and FloTree for visualizing evolutionary concepts Innovation in music+code learning environments such as TunePad Research on children's understanding of electricity through augmented circuit exhibits Pioneering work on room-scale augmented reality for computational modeling Horn has mentored numerous graduate students who have become productive researchers in learning sciences and educational technology. His collaborative network is extensive, with particularly strong ties to Uri Wilensky (31 joint publications), Chia Shen, and other leaders in the field. His work consistently demonstrates how well-designed technological interventions can transform how learners engage with complex concepts across multiple domains.

Dylan EdwardsView profile

Adjunct Professor

• Neuroplasticity
• Motor Rehabilitation
• Non-Invasive Brain Stimulation
• +4 more

Dylan Edwards, Ph.D., P.T., is an Adjunct Professor of Neuroscience at Edith Cowan University’s School of Medical and Health Sciences. He holds leadership roles as Director of the NeuroRehabilitation and Robotics Laboratory and former Director of the Moss Rehabilitation Research Institute (MRRI) in Philadelphia. His expertise spans neuroplasticity, motor rehabilitation post-neurological injury, non-invasive brain stimulation (TMS/tDCS), and robotics. Edwards has pioneered combinatorial therapies (robotics + neuromodulation) for stroke and spinal cord injury recovery. He has over 100 publications and serves on editorial boards and national committees, including the NIH advisory panels. Education: Ph.D. Clinical Neurophysiology (2005, UWA), B.S. Physical Therapy (2001, Curtin), B.App.Sci. Exercise Physiology (1994, Edith Cowan) Affiliations: Burke Neurological Institute, Harvard Medical School, MossRehab Research Interests: Edwards focuses on enhancing functional recovery through advanced technologies. His work combines robotics (e.g., exoskeletons, humanoid robots) with neuromodulation techniques to optimize neuroplasticity. Key areas include stroke aphasia recovery via rTMS, spinal cord injury regeneration using MAP2K signaling, and telerehabilitation scalability. Recent Articles: His 2025 work explores tDCS dose-response in motor learning, while 2023 studies highlight robotic exoskeleton efficacy in spinal cord injury. Key themes include optimizing neuromodulation timing, predicting stroke recovery trajectories, and advancing clinical trial methodologies. Awards: 2018 Clinical Award (NY Neuromodulation), 2007 Science & Innovation Awards (WA Govt), Whitfield Fellowship (2004) Grants/Leadership: NIH/NINDS reviewer, Strategic Planning Committee (ASNR), Scientific Advisor (Massachusetts General Hospital) Labs/Teams: Heads the MRRI’s Human Motor Recovery Lab and collaborates with MIT’s robotic lab. Develops standardized protocols for TMS/tDCS training (IFCN guidelines) and advises FDA on rehabilitation robotics.

Seiichi TakamatsuView profile

Professor

• Hybrid Electronics
• MEMS Technologies
• Wearable Technology
• +3 more

Seiichi Takamatsu is a Professor at the School of Systems Science and Industrial Engineering, Binghamton University, since 2024. He holds BE, ME, and PhD degrees in Mechanical Informatics from the University of Tokyo (2003–2009). Previously, he was a researcher at AIST (2009–2016) and an Associate Professor at the University of Tokyo. His international experience includes visiting roles at Cornell University (2005–2006) and Ecole Nationale Supérieure des Mines de Saint-Étienne (2014–2015). His research focuses on hybrid electronics, wearable MEMS technologies, and electronic textiles. Key projects include flexible sensors for robotics, biomedical applications, and structural health monitoring. He has published over 120 papers and received prestigious awards like the Satomi Prize and Young Investigator Award. Recent work emphasizes stretchable electronics, cryogenic sensors, and non-contact vital sign monitoring. His articles span applications in livestock health, wearable keyboards, and textile-based ultrasound probes. He has pioneered sensor arrays for structural monitoring and rumen microbial fuel cells. Awards: Excellent Researcher Award (University of Tokyo), Satomi Prize, Young Investigator Award (Japan Institute of Electronics Packaging) Key Labs: Formerly at AIST's Research Center for Ubiquitous MEMS and Micro Engineering Future Work: Expanding into meter-scale electronic textiles and AI-driven health monitoring systems.

Thomas OttView profile

Professor

• Bio-inspired Computing
• Applied Neuroinformatics
• Machine Learning
• +7 more

Prof. Thomas Ott is the Director of the Institute of Computational Life Sciences at the ZHAW School of Life Sciences and Facility Management. His research focuses on Bio-inspired Computing, Applied Neuroinformatics (including Pattern Recognition and Machine Learning), Modelling of Complex Systems, and Forecast Methodologies for Traffic/Logistics. He leads projects in AI-driven health systems, ecological sustainability, and industrial analytics. Ott has collaborated on over 50 peer-reviewed publications, with recent work emphasizing neuromorphic computing, predictive analytics in healthcare, and chaos-based network analysis. Education & Affiliations: His academic background includes prior roles at institutions like the University of Zurich and ETH Zurich. He serves on boards of Prognosix AG, Life Sciences Zurich Business Network, and Biotechnet Switzerland. Research Interests: Ott’s work bridges computational methods with real-world applications, such as AI for agriculture (fruit classification), predictive maintenance in healthcare systems, and traffic flow optimization. His team develops hybrid human-machine learning frameworks for complex decision-making processes. Projects & Grants: Key projects include 'Shapescience – AI for Morphologically-Based Fruit Recognition', 'Predictive Analytics for Hospital Supply Chains', and 'Smartstones - AI for Plant Breeding'. These reflect his focus on applied AI solutions for industry and public health. Labs & Teams: He oversees interdisciplinary teams at ZHAW’s Computational Life Sciences lab, integrating computer science, biology, and engineering for innovative problem-solving.

Elles A.L. RaaijmakersView profile

Researcher

• Electrical Engineering
• Gamification in Education
• Medical Technology
• +3 more

Elles A.L. Raaijmakers is a Postdoc in the Electrical Engineering department at Eindhoven University of Technology (TU/e). She is affiliated with the Electromagnetics for Care + Cure Research Lab (EM4Care+Cure) and the RF Sensing & Communication Lab. Her academic background includes a Master’s degree in Electrical Engineering from TU/e (2015) and a part-time PhD at the University of Amsterdam (2023). She also contributed as an illustrator for faculty and university magazines, and published a comic book to translate research findings for public access. Her research focuses on gamification in education, particularly developing games for electromagnetism and integrated circuit design. She explores innovative methods to simplify complex scientific concepts using storytelling and interactive tools. Additionally, her work intersects with neuroscience and medical technology, investigating electric field effects on neurons and brain stimulation setups. Key contributions include the Ripples educational game (2025) and studies on electric field distributions in brain stimulation (2024). Her 2023 thesis, Nudging Neurons , examines neuronal responses to weak electric fields. She has also developed the Calima Calcium Imaging Tool for brain-on-chip cultures (2021). Awards: Best Poster Presentation (2015), New Investigator Travel Award (2016), TU/e Best Graduation Project in Electrical Engineering (2016) Labs: EM4Care+Cure Lab, RF Sensing & Communication Lab, Eindhoven MedTech Innovation Center Public Engagement: Workshops for gifted youth, media contributions explaining chip design and brain stimulation

Fangzhou ZhaoView profile

Assistant Professor

• Power Electronics
• Renewable Energy Systems
• Grid Integration
• +4 more

Fangzhou Zhao is an Assistant Professor at the Department of Energy Technology, Aalborg University. His research focuses on advancing power electronics technologies, particularly in grid integration, renewable energy systems, and converter control strategies. He specializes in addressing challenges related to harmonic distortion, grid stability, and energy storage systems, with applications in offshore wind farms and battery energy storage. He has participated in several funded projects, including the MERGE project (EUDP-funded, 2020–2023) and the OWF System Integration Enhancement project (SHELL-funded, 2021–2022). Additionally, he supervised the PhD project 'Wide Output Voltage Universal Fast Charger' (2023–2026) and co-supervised the 'MODELING AND CONTROL OF SCALABLE VERSATILE GRID EMULATION SYSTEM' (2020–2024). His work emphasizes practical solutions for high-power systems, such as DC fast chargers and modular multilevel converters. Research interests include grid-forming converter control, power hardware design for grid emulators, and mitigating transformer saturation in converter systems. His recent publications highlight innovations in harmonic mitigation, voltage stability, and adaptive strategies for light-load efficiency in resonant converters. In advising, he supervises Li Z., a PhD student. While no scientific awards are explicitly listed, his contributions to power electronics and grid systems have garnered significant citations. Zhao’s involvement in interdisciplinary teams underscores his role in bridging theoretical research with industrial applications in renewable energy infrastructure.

MAJID SHOKOUFIView profile

Lecturer

• Biophotonics
• Microelectronics
• Internet of Things
• +4 more

Majid Shokoufi is a Lecturer in the School of Engineering Science at Simon Fraser University (SFU), part of the Faculty of Applied Sciences. He holds a Ph.D. in Mechatronic Systems Engineering from SFU, an M.Sc. in Electrical Engineering from Iran University of Science & Technology, and a B.Sc. in Electrical Engineering from Amir Kabir University of Technology. His research focuses on Biophotonics, Microelectronics, Internet of Things (IoT), and Wearable devices, with a strong emphasis on developing handheld optical imaging systems for medical applications like breast cancer assessment. Shokoufi teaches courses in Microelectronics, Digital Logic, and Embedded Systems. His work integrates biomedical optics with engineering solutions, exemplified by his contributions to portable diffuse optical breast scanning probes and deep learning-driven image reconstruction techniques. Recent research includes fusion-based imaging methods for tomogram-free diagnosis and multi-frequency analysis for enhanced diagnostic accuracy. His academic career reflects a commitment to advancing medical imaging technologies through interdisciplinary approaches, combining optical engineering with clinical applications. Though no specific awards are listed, his extensive publication record underscores his contributions to medical device innovation and diagnostic methodologies.

Karen S. WilcoxView profile

Professor

• Epilepsy Research
• Neuropharmacology
• Drug Development
• +3 more

Karen S. Wilcox, PhD, is a Professor at the University of Utah Health College of Pharmacy , specializing in epilepsy research and neuropharmacology. Her work focuses on developing novel antiseizure therapies, understanding mechanisms of epileptogenesis, and optimizing drug delivery systems. She leads research in viral-induced epilepsy models, neuroinflammation, and closed-loop stimulation techniques. Research Interests: Neuropharmacology of antiseizure drugs Epileptogenesis and neurodegenerative processes Drug delivery via neural implants and focused ultrasound Preclinical screening platforms for epilepsy therapies Impact of inflammation on seizure susceptibility Awards: Recipient of the NINDS Landis Award for Outstanding Mentorship (2022), recognizing her contributions to training the next generation of neuroscientists. Labs & Affiliations: Active member of the Skaggs Pharmacy Institute and affiliated with the Pharmtox research group. Collaborates with the NIH Epilepsy Therapy Screening Program to advance translational epilepsy research.

Richard MartinView profile

Professor

• Photonics
• Millimeter-Wave Imaging
• Low-Noise Amplifier Circuits
• +5 more

Richard Martin is a Professor of Practice in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a B.S. in Physics from Millersville University and advanced degrees (M.B.A., M.S.E.E., Ph.D.) from UD. His doctoral research at NASA’s Jet Propulsion Laboratory (JPL) focused on photonic devices under a NASA fellowship. With over 25 years of industrial experience, he specializes in low-noise amplifier circuits, photonics-based sensors, and embedded systems design. He transitioned to full-time faculty in 2017, teaching courses on microcontroller systems, analog circuits, and IoT devices. He also serves as an affiliated faculty member at UD’s Horn Entrepreneurship Program and as CTO of Resonate Forward LLC, a startup developing wearable devices for Parkinson’s Disease therapy. Education: B.S., Physics, Millersville University of Pennsylvania M.B.A., University of Delaware M.S.E.E., University of Delaware Ph.D., Electrical Engineering, University of Delaware Research Interests: Dr. Martin’s work spans millimeter-wave imaging systems, optically enabled sensors, embedded systems, and IoT device development. His recent projects include video-rate passive millimeter-wave imagers for degraded visual environment mitigation and polarization-sensitive imaging sensors. His research emphasizes practical applications in fields like aerospace, medical technology, and defense. Advising & Industry Engagement: As CTO of Resonate Forward LLC, he leads development of wearable therapeutic devices. His career includes roles at W.L. Gore & Associates and technology startups, reflecting a strong focus on bridging academic research with industrial innovation. Labs & Teams: Affiliated with the Horn Entrepreneurship Program, he collaborates on tech startups and entrepreneurship education. His research leverages advanced fabrication techniques in multilayer substrates (e.g., LCP) and optical upconversion methodologies for imaging systems.