جستجوی استاد راهنمای دانشگاهی

Valkyrie Arline Savage is a Tenure Track Assistant Professor in the Human-Centred Computing section of the Department of Computer Science (DIKU) at the University of Copenhagen's Faculty of Science. Her research bridges the gap between physical input devices and digital systems, with a focus on creating interfaces that can be custom-designed, fabricated, or simply picked up to solve specific user problems in particular contexts. Dr. Savage studies physical input devices (like mice, game controllers, or surgical tools) as the ultimate bridge between humans and computers. Her work pushes for deeply custom interfaces that fit specific user needs for particular times, places, and tasks. She is fascinated by the interaction between sensing (which takes physical information and digitizes it) and fabrication (which takes digital information and physicalizes it). Her methodology centers on systems research, where project outputs are functional, novel systems that users can interact with directly, with contributions often coming from the algorithms required to make these prototypes work. Dr. Savage's recent publications demonstrate a strong research trajectory in innovative fabrication techniques that integrate electronics with traditional manufacturing processes. Her work spans 3D printing innovations, laser cutting applications, textile integration, and novel sensing mechanisms. Key themes across her research include democratizing fabrication technologies, combining digital and craft-based methods, and developing responsive input devices that adapt to specific user requirements. Her publications in top-tier venues like CHI, UIST, and TEI showcase her contributions to computational fabrication and human-computer interaction. Dr. Savage actively works with students in her fabrication lab, which is equipped with 3D printers, lasercutters, electronics tools, a CNC mill, and other fabrication equipment. Her advising focuses on interdisciplinary projects that explore the boundaries between technology and craft, including 3D printing combined with crochet, underwater 3D printing applications, and lasercut slot-together circuits. She has published 22 research outputs including journal articles, conference proceedings, a PhD thesis, and a patent, demonstrating consistent productivity in her field. Her research has practical applications across multiple domains including maritime automation, medical interfaces, and general human-computer interaction. The citations and downloads of her work indicate growing impact in her field, with applications spanning from specialized industrial contexts to everyday user interfaces. Dr. Savage's work represents a significant contribution to making fabrication technologies more accessible and integrated for creating custom interactive systems.

• Human-Computer Interaction
• Physical Input Devices
• Sensing Technologies
• +۵ مورد دیگر

Lingjun Li is a Professor of Pharmaceutical Sciences and Chemistry at the University of Wisconsin–Madison , where she leads a multidisciplinary research program at the intersection of analytical chemistry, neuroscience, and biomedical discovery. Her work is centered in the School of Pharmacy and spans the Department of Pharmaceutical Sciences and Department of Chemistry . Education: B.E., 1992 – Beijing Polytechnic University (China) Ph.D., 2000 – University of Illinois at Urbana-Champaign Postdoctoral Fellow, 2000–2002 – Pacific Northwest National Laboratory, Brandeis University, and University of Illinois at Urbana-Champaign Research Focus: Professor Li’s laboratory pioneers bioanalytical mass spectrometry and microseparation technologies to decode the neuropeptidome —the complex landscape of signaling peptides in the nervous system. By integrating chemical labeling , capillary electrophoresis , nanoLC , and tandem MS , her team uncovers novel neuropeptides in crustacean models and translates these findings to mammalian systems, including Alzheimer’s disease biomarker discovery , neuroprotective astrocyte secretomes , and zebrafish cardiotoxicity . Publication Trends: From 2023–2025, her group has published extensively on neuropeptide identification , post-translational modifications , mass spectrometry imaging , and quantitative proteomics , with a strong emphasis on methodological innovation (e.g., EndoGenius, TEMI, DiLeu labeling) and disease-relevant applications in cancer, neurodegeneration, and metabolic disorders. Scientific Awards: While specific awards are not listed in the provided text, her prolific publication record and leadership in analytical chemistry underscore significant recognition within the field. Collaborations & Funding: Her research is bolstered by interdisciplinary collaborations across neurobiology, bioinformatics, and clinical sciences, supported by grants that drive technological advancement and biological discovery. Laboratory & Team: The Li Group operates a state-of-the-art facility for mass spectrometry and microfluidics , fostering a collaborative environment for graduate students, postdocs, and visiting scholars.

• Bioanalytical Chemistry
• Mass Spectrometry
• Neuropeptidomics
• +۵ مورد دیگر

Dr. Antreas Kalli serves as Associate Professor at the School of Medicine, University of Leeds, within the Faculty of Medicine and Health. He leads a computational research group focused on membrane protein dynamics, utilizing multi-scale molecular simulations to investigate protein function in native-like lipid environments. His work is closely affiliated with the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) and the British Heart Foundation Cardiovascular Research Centre. His academic background includes a DPhil in Structural Biology from the University of Oxford (2012) and a BSc in Physics from the University of Cyprus (2008). Prior to his current position, he conducted postdoctoral research in the Sansom group at Oxford, specializing in computational methodologies for membrane protein analysis. Research centers on membrane proteins—critical components in cellular signaling, transport, and mechanosensation—using coarse-grained and all-atom molecular dynamics simulations. Key themes include Piezo1 channel mechanotransduction, T-cell receptor signaling, membrane transporter function (particularly Band 3 anion exchanger), and peripheral membrane protein-lipid interactions. His group develops advanced computational approaches to simulate protein behavior in asymmetric lipid bilayers that mimic native cellular environments. Analysis of recent publications (2021-2025) reveals consistent focus on mechanosensitive channels (especially Piezo1), immune receptor dynamics, and lipid-regulated protein function. His work demonstrates how specific lipid interactions govern channel activation, transporter mechanisms, and signaling complex assembly, with direct implications for cardiovascular disease and immune disorders. Scientific awards: No specific honors or fellowships were documented in the provided materials. Dr. Kalli supervises PhD researchers Jiehan Chong and Dheeraj Prakaash, while offering undergraduate and postgraduate projects in membrane protein simulation. His research is institutionally supported through LICAMM and the Multidisciplinary Cardiovascular Research Centre, with teaching responsibilities spanning undergraduate and postgraduate programs in Medicine and Biological Sciences. His laboratory operates within the LIGHT Building (Room 6.30), collaborating with cardiovascular research teams to investigate force-sensing mechanisms in endothelial cells and red blood cells. The group specializes in constructing complex asymmetric membrane models to study lipid-protein co-regulation, with particular emphasis on how membrane composition modulates Piezo1 function in vascular physiology.

• membrane proteins
• molecular dynamics simulations
• molecular modelling
• +۵ مورد دیگر

Dr. Chenfan Zhang is a researcher focused on High Voltage CMOS (HV-CMOS) pixel sensors and radiation-tolerant detector technology. Their work addresses challenges in sensor design for high-radiation environments, particularly in particle physics experiments. Research Interests: Zhang's research spans semiconductor device engineering, radiation physics, and instrumentation. Key contributions include optimizing backside-biased CMOS sensors, time walk compensation circuits, and radiation tolerance simulations. Publications: Recent work includes the RD50-MPW and UKRI-MPW series of monolithic pixel chips (2024-2025), emphasizing high granularity and radiation durability. Studies involve collaboration with institutions like CERN (RD50 project) and UKRI.

• Microelectronics
• Semiconductor Devices
• Radiation Physics
• +۳ مورد دیگر

Professor Yuri Pashkin, a Fellow of the Institute of Physics (FInstP) and the Higher Education Academy (FHEA), holds the Chair of Experimental Condensed Matter Physics at the Department of Physics, Lancaster University . His research focuses on quantum computing, quantum metrology, and nanoelectromechanical systems using superconducting and hybrid nanocircuits. He collaborates with the Ultra Low Temperature (ULT) group and international theorists, and leads projects at the Quantum Technology Centre. Research Themes : Quantum computing, superconducting nanocircuits, Coulomb blockade devices, nanomechanical resonators, quantum information processing. Methodology : Nanofabrication in cleanrooms, mK-temperature measurements, dilution refrigeration, microwave engineering. Recent Publications highlight advancements in axion dark matter detection, superconducting parametric amplifiers, and nanomechanical resonator design. His scientific awards include the Royal Society Wolfson Research Merit Award and the Japan Society of Applied Physics Superconductors Division Best Paper Award. Quantum sensing for hidden sector research (QSHS) Josephson junction-based quantum devices Nanoelectromechanical systems for cryogenic applications Professional Activities encompass peer-review roles for journals like Nature Communications and Applied Physics Letters , and service on EPSRC panels. He actively supervises PhD students in experimental quantum technologies.

• Quantum Computing
• Quantum Metrology
• Nanoelectromechanical Systems
• +۷ مورد دیگر

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.

• Electronic Design Automation (EDA)
• Integrated Circuit Physical Design
• Heterogeneous Integration
• +۳ مورد دیگر

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

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +۳ مورد دیگر

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.

• Antenna Design
• Wireless Communications
• Millimeter-Wave Technologies
• +۴ مورد دیگر

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.

• Electric Machines
• Power Electronics
• Drives
• +۵ مورد دیگر

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.

• Power Systems
• Optimization
• Smart Grids
• +۳ مورد دیگر

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.

• Electronic Design Automation
• Computer-Aided Design
• Analog/Mixed-Signal Circuit Design
• +۷ مورد دیگر

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.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +۷ مورد دیگر

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.

• Neuroplasticity
• Motor Rehabilitation
• Non-Invasive Brain Stimulation
• +۴ مورد دیگر

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.

• Hybrid Electronics
• MEMS Technologies
• Wearable Technology
• +۳ مورد دیگر

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.

• Bio-inspired Computing
• Applied Neuroinformatics
• Machine Learning
• +۷ مورد دیگر