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MATTHEW DZIECIUCHView profile

Researcher

• Ocean Acoustic Tomography
• Climate Monitoring via Ocean Acoustics
• Arctic Oceanography
• +3 more

Matthew Dzieciuch is a Researcher at the Institute of Geophysics and Planetary Physics, part of the Scripps Institution of Oceanography at the University of California San Diego. His work focuses on ocean acoustic tomography and climate monitoring through ocean acoustics. He holds degrees from the University of Michigan (B.S., M.S., PhD) and has contributed to major Arctic and global ocean acoustics research initiatives. Key research areas include acoustic propagation in polar regions, Arctic climate change impacts, and the integration of acoustic networks into ocean observing systems. He has led and participated in large-scale experiments like CANAPE (Canada Basin Acoustic Propagation Experiment) and CAATEX (Coordinated Arctic Acoustic Thermometry Experiment), focusing on tomographic arrays, glider-based acoustics, and long-range acoustic measurements. His publications emphasize acoustic travel-time analysis, sea ice dynamics, and the interplay between environmental conditions and sound transmission. Dzieciuch collaborates with institutions globally to advance acoustic-based oceanography and climate monitoring, leveraging autonomous platforms like gliders and buoyancy-driven vehicles.

Luana RuizView profile

Assistant Professor

• Large-scale Graph Machine Learning
• Graph Neural Networks
• Signal Processing
• +6 more

Luana Ruiz is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science and Artificial Intelligence Institute (DSAI). She earned her Ph.D. in Electrical Engineering from the University of Pennsylvania (2022), and dual B.Sc. and M.Eng. degrees in Electrical Engineering from the University of São Paulo (Brazil) and École Supérieure d’Electricité (France, now CentraleSupélec) in 2017. Her research focuses on machine learning, signal processing, and network science, particularly scalable algorithms for non-Euclidean domains like graphs and data manifolds. She has pioneered work on graph neural networks (GNNs), graph sampling, and theoretical limits of transferability and generalization in graph-based learning. Notably, she has developed methods for efficient GNN training on large graphs and stability analysis of neural networks on manifolds. Affiliations: Johns Hopkins University, MINDS, DSAI Education: Ph.D., University of Pennsylvania (2022); B.Sc./M.Eng., University of São Paulo & CentraleSupélec (2017) Her research interests include large-scale graph machine learning, manifold learning, physics-informed ML, and combinatorial optimization. She has received awards such as the iREDEFINE Fellowship (2019), MIT EECS Rising Star (2021), and Best Student Paper Awards at EUSIPCO (2019, 2021). Her work bridges theoretical foundations and practical applications, with contributions to GNN architecture design, signal processing on graphs, and stability guarantees for neural networks on manifolds. Recent publications highlight advances in graph sampling for scalable GNNs, stability of manifold neural networks, and theoretical analysis of GNN expressivity. She collaborates with institutions like MIT and the Simons Institute, and her interdisciplinary approach addresses challenges in graph data analysis and AI efficiency. She advises on grants related to graph signal processing and has contributed to the development of graphon-based frameworks for large-scale graph analysis. Awards: Eiffel Excellence Scholarship (2013–2015), Best Paper Awards at EUSIPCO (2019, 2021) Grants/Advising: METEOR and FODSI postdoctoral fellowships, Google Research Fellowships

Ulrich ParlitzView profile

Adjunct Professor

• Nonlinear Dynamics
• Chaos Theory
• Biomedical Physics
• +7 more

Ulrich Parlitz is an Adjunct Professor of Physics at Georg-August-University Göttingen and a Scientist leading the Biomedical Physics Group at the Max Planck Institute for Dynamics and Self-Organization. His research focuses on nonlinear dynamics, chaos theory, and biomedical applications, particularly in cardiac dynamics and excitable media. He has held visiting positions at institutions like UC San Diego and the Santa Fe Institute. Education: 1987 PhD in Physics, Georg-August-University Göttingen 1984 Diploma in Physics, Georg-August-University Göttingen Research Interests: Analysis of nonlinear systems (neurons, lasers, oscillators) Bifurcation and chaos phenomena Data-based modeling and synchronization control Wave dynamics in excitable media (e.g., cardiac arrhythmias) Fractal dimension estimation and reservoir computing Labs/Teams: Leads the Biomedical Physics Group at MPI-DS and contributes to the IMPRS Program in Physics of Biological and Complex Systems.

Christian TetzlaffView profile

Professor

• Computational Neuroscience
• Synaptic Physiology
• Neuromorphic Engineering
• +4 more

Prof. Christian Tetzlaff holds a W2 Professorship at the Department for Neuro- and Sensory Physiology, University Medical Center Göttingen. He leads the Computational Synaptic Physiology group, focusing on integrating computational methods to understand neural systems' principles and translating these into technologies like robotics and neuromorphic engineering. His work spans scales from synaptic protein interactions to large neural networks, leveraging disciplines such as nonlinear dynamics and machine learning. Educational Background: 2009: Diploma in Physics, Georg-August University Göttingen 2013: PhD in Physics (Dr.rer.nat.), Georg-August University Göttingen Research Leadership & Grants: Principal Investigator in EU Horizon 2020 Projects (Plan4Act, ADOPD, Human Brain Project's FIPPA) Lead in DFG-funded SFB 1286 (Quantitative Synaptology) INRC member (Intel Neuromorphic Research Community) BMBF-funded KISSKI project (2022–present) Research Interests: His lab explores synaptic mechanisms underlying learning, memory consolidation via synaptic tagging-and-capture, and neuromorphic hardware implementations. Recent work emphasizes calcium signaling in synapses, memristive devices for synaptic working memory, and robust trajectory generation using neuromorphic chips like Loihi. Affiliations: Co-coordinator of Plan4Act (2017–2021) and active in the Göttingen Graduate Center for Neurosciences (GGNB). Collaborates globally with institutions like the Weizmann Institute and Columbia University. Laboratory: Visit tetzlab.com for lab activities and open-source tools like Plastic Arbor .

• Silicon Devices
• Wide Bandgap Semiconductors
• High-Frequency Electronics
• +3 more

Mikael Östling is a Professor at KTH Royal Institute of Technology, holding a position in the Division of Electronics and Embedded Systems within the School of Information and Communication Technology. He earned his MSc (1980) and PhD (1983) in engineering physics from Uppsala University. Since 1984, he has been a faculty member at KTH, serving as Deputy President (2017–2022), Dean of the School of ICT (2004–2012), and Head of the Department of Microelectronics and Information Technology (2000–2004). He has held visiting roles at Stanford University and the University of Florida. His research focuses on silicon/silicon germanium devices, wide bandgap semiconductors (e.g., silicon carbide), and high-power/high-frequency applications. He has authored over 600 papers, 10+ book chapters, and a textbook. Key achievements include co-founding TranSiC (2005), securing the ERC Advanced Investigator Grant (2009), and serving as Editor-in-Chief of IEEE Journal of Electron Devices Society (2016–2019). He is an IEEE and ECS Fellow. Östling has supervised 50 PhD theses and contributed to innovations like high-temperature silicon carbide circuits and graphene-based sensors. His work spans academic leadership, industry collaboration, and global research advisory roles in EU frameworks and the European Research Council.

Ali Wanis Ali ElshaariView profile

Associate Professor

• Quantum Photonics
• Topological Photonics
• Quantum Integrated Photonics
• +7 more

Ali W. Elshaari is an Associate Professor at the Royal Institute of Technology (KTH) in Stockholm, Sweden. He holds a B.S. in Electrical Engineering from the University of Benghazi (2007) and a Ph.D. in photonics from the Rochester Institute of Technology (2011). His postdoctoral research at TU Delft’s Kavli Institute of Nanoscience focused on quantum transport. Currently, he leads the Quantum Nano Photonics Group, pioneering work in topological and quantum integrated photonics to develop high-performance circuits for communication, sensing, and metrology. His research spans hybrid quantum photonics, strain-tunable systems, and superconducting detectors, with applications in quantum communication and quantum materials characterization. Elshaari's research interests include integrating single-photon emitters into CMOS-compatible platforms, exploring quantum phenomena like entanglement and coherence, and developing advanced photonic materials (e.g., hexagonal boron nitride and Cu₂O). He has contributed to on-chip single-photon generation/filtering, strain-tunable photonic circuits, and slow-wave superconducting detectors. His work bridges experimental and theoretical approaches, leveraging imaging techniques and phase retrieval algorithms. Elshaari is an editorial board member for Nature Portfolio - Scientific Reports , Wiley Advanced Quantum Technologies , and EPJ Quantum Technology . He teaches courses in quantum technology, electromagnetism, and optical physics. His funding includes grants from the Wallenberg Foundation, Swedish Research Council, Vinnova, and the European Research Council. His lab actively recruits students for bachelor’s and master’s projects in quantum photonics and nanophotonics.

Juergen PfingstnerView profile

Researcher

• Particle Accelerator Technology
• Free Electron Lasers
• CLIC (Compact Linear Collider)
• +5 more

Juergen Pfingstner is a Post-doctoral Fellow at the University of Oslo (since 2015) within the Department of Physics. His research focuses on advanced accelerator technologies, particularly at CERN's CLIC Test Facility (ATF2). Key areas include emittance preservation, ground motion mitigation, and free-electron laser (FEL) design. He holds a PhD from the Vienna University of Technology (2013) and a Master's in Electrical Engineering from Graz University of Technology (2008), specializing in control engineering and electromagnetic field computation. His academic journey includes a postdoctoral stint at CERN (2012–2014), where he investigated ground motion effects on CLIC performance. Collaborations span institutions like KEK (ATF2 facility) and the X-band FEL collaboration. Research interests bridge particle accelerator physics, control systems, and high-frequency radiation technologies. Publications emphasize CLIC final focus systems, wakefield suppression, and plasma wakefield acceleration. His work addresses both theoretical and experimental challenges in next-generation collider design, including THz radiation facilities and feedback control methodologies.

Yichuang SunView profile

Professor

• Wireless Communications
• 5G/6G Technologies
• RF Circuits and Systems
• +7 more

Yichuang Sun is a Professor of Communications and Electronics at the University of Hertfordshire, leading the Communications and Intelligent Systems Research Group and the Electrical and Electronic Engineering Department. He holds a PhD from the University of York (1996) and has over 450 peer-reviewed publications, 5 authored books, and 40+ supervised PhD students. His research focuses on wireless communications (5G/6G, RIS-aided systems), RF/microwave circuits, neuromorphic computing, and machine learning applications. He serves as Editor or Guest Editor for 12+ IEEE/IET journals and chairs technical committees for IEEE conferences. Notably ranked in the World's Top 2% Scientists since 2019, he also advises global institutions like Oxford and the Royal Academy of Engineering. Education: PhD in Electronics, University of York, UK (1996) Research Interests: Wireless/Mobile Communications and Networks RF and Microelectronic Circuits Neuromorphic Computing and Machine Learning Memristor-based Systems Secure and Energy-Efficient Communications Grants & Projects (selected): Advanced mmWave MIMO 4D Radar for Traffic Detection (2024–2027) Wireless Power Transfer for Implantable Medical Devices (2023–2025) RFID System for Sample Management (2021–present) Labs/Teams: Leads the Communications and Intelligent Systems Research Group, collaborating on 6G, neuromorphic hardware, and IoT systems.

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +5 more

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Jihye ParkView profile

Associate Professor

• Remote Sensing
• Geodesy
• Geophysics
• +3 more

Jihye Park is an Associate Professor in the Department of Civil and Construction Engineering at Oregon State University's College of Engineering. She specializes in GNSS (Global Navigation Satellite System) positioning/navigation and remote sensing applications, focusing on enhancing system performance in harsh environments and leveraging GNSS for atmospheric monitoring. Her research includes detecting ionospheric disturbances caused by earthquakes, volcanic eruptions, and artificial explosions, as well as developing algorithms for real-time anomaly detection and navigation system improvements. Dr. Park holds a Ph.D. in Geodetic Science and Surveying from The Ohio State University (2012), an M.S. in Geodetic Science and Surveying from the same institution (2010), and prior degrees in Geoinformatics from The University of Seoul (B.S. 2004, M.S. 2006). Her research interests span GNSS remote sensing applications such as monitoring ionospheric anomalies, hurricane-induced rainfall trajectories, and coastal hazards. She also explores GNSS-Reflectometry (GNSS-R) for sea level changes, storm surge monitoring, and climate-related phenomena. Her work emphasizes real-time data processing, machine learning integration, and interdisciplinary applications in disaster response and geophysical monitoring. Dr. Park has contributed to advancements in GNSS accuracy through projects like JPL's GUARDIAN system and studies on the Galileo High Accuracy Service. Her recent work highlights automated detection systems and deep learning approaches for TID (Traveling Ionospheric Disturbance) identification, reflecting her commitment to bridging geomatics with environmental and disaster resilience challenges.

Zijian DiaoView profile

Associate Professor

• Quantum Computation
• Quantum Information Theory
• Natural Language Processing
• +7 more

Zijian Diao is an Associate Professor in the Department of Mathematics at Ohio University's College of Arts and Sciences, where he contributes to both teaching and research. He is based at the Eastern Campus in Shannon Hall and teaches a wide range of undergraduate mathematics courses. Education: Ph.D. in Mathematics, Texas A&M University, 2001 M.S. in Computer Science, University of Illinois, 2003 B.S. in Automation, University of Science and Technology of China, 1996 His research spans interdisciplinary areas with a strong focus on quantum computation and information theory , where he has made contributions to Grover's algorithm, quantum counting, and quantum circuit design. He has also worked in natural language processing , particularly in multilingual speech-to-speech translation systems like MARS. Additional interests include control theory , partial differential equations , and mathematical modeling . His recent and ongoing publications reflect a blend of theoretical mathematics and applied computational science. The works trend toward quantum algorithms and foundational mathematical proofs, with earlier contributions in NLP and control systems. His research bridges pure mathematics with practical implementations in quantum computing and language technologies. Scientific Awards: Ohio University Regional Higher Education Outstanding Professor Dr. Diao advises undergraduate and graduate students in mathematics and related fields, though specific student names are not listed. He has been involved in research projects supported by academic grants, particularly in quantum computing and interdisciplinary technology development. His curriculum vitae indicates sustained scholarly activity across multiple domains. He is associated with research groups and collaborations in quantum computing, having co-authored works with G. Chen, C. Huang, and others. His work in NLP was part of larger team efforts involving researchers from industry and academia, suggesting active participation in collaborative technical teams.

• Structural Dynamics
• Vibrations
• Acoustics
• +8 more

Jonathan Blotter is a Professor in the Department of Mechanical Engineering at the College of Engineering, Brigham Young University (BYU). His research and teaching focus on structural dynamics, vibrations, acoustics, and experimental mechanics, with extensive use of laser-based measurement techniques. Education: Doctor of Philosophy, Mechanical Engineering, Virginia Polytechnic Institute and State University, 1996 Master of Science, Mechanical Engineering, Utah State University, 1993 Bachelor of Science, Mechanical Engineering, Utah State University, 1991 His research interests include structural dynamics, vibrations, acoustics, noise and vibration control, solid mechanics, electro-optic holography, and scanning laser Doppler vibrometry. His work bridges experimental techniques with computational modeling to analyze structural energy flow and dynamic behavior. He has made significant contributions to the understanding of power flow in plates and beams, nearfield acoustic holography, and flow-induced vibrations. The most recent articles reflect a consistent focus on experimental acoustics and vibration measurement. Key themes include noise control using microphone arrays, sound quality assessment in consumer products, optical interferometry for deformation measurement, and fluid-structure interaction in piping systems. These works span disciplines of mechanical engineering, acoustics, optics, and thermal sciences, with subfields such as vibroacoustics, metrology, and non-destructive testing. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Dr. Blotter has advised numerous graduate students, including 14 Master’s students and one Ph.D. student. His advising spans experimental and computational projects in vibrations, acoustics, and mechanical testing. While specific grant details are not listed, his publication record suggests sustained research activity supported by academic and possibly industrial or federal funding, particularly in experimental mechanics and acoustics. Labs and Research Teams: Though not explicitly named, Dr. Blotter's work implies leadership in an experimental mechanics and acoustics laboratory at BYU. His collaborations with students and co-authors like Sommerfeldt, West, and Kimber suggest active research teams focused on laser-based measurement, noise control, and structural dynamics.

Stefan Kragh NielsenView profile

Professor

• Plasma Physics
• Fusion Energy
• Collective Thomson Scattering
• +7 more

Stefan Kragh Nielsen is a Professor and Section Leader in the Department of Physics at the Technical University of Denmark (DTU), specializing in Plasma Physics and Fusion Energy. He is actively involved in experimental and theoretical research related to fusion plasma diagnostics, particularly collective Thomson scattering and microwave-based measurements in tokamak devices such as ASDEX Upgrade and Wendelstein 7-X. His research interests include: Plasma Physics and Fusion Energy Collective Thomson Scattering Fast Ion Dynamics Electron Cyclotron Resonance Heating Parametric Instabilities Microwave Diagnostics The recent publications highlight a strong focus on advanced diagnostics, nonlinear wave interactions, and fast ion behavior in fusion plasmas. His work spans theoretical modeling, experimental validation, and instrumentation development, particularly in high-frequency microwave systems for continuous plasma monitoring. Trends show increasing emphasis on reduced modeling techniques and real-time diagnostic capabilities for next-generation fusion reactors. Scientific contributions include: Development of ultrafast digitizers for microwave diagnostics Commissioning of 174 GHz CTS systems at W7-X Modeling of metaplectic geometrical optics for plasma waves Investigation of parametric decay in gyrotron beams He actively supervises multiple PhD students on topics such as non-linear processes in electron Bernstein wave heating, ion dynamics via CTS, and parametric decay instabilities in spherical tokamaks. His projects are well-funded and aligned with international fusion research goals. He has collaborated extensively with major fusion facilities including ASDEX Upgrade, Wendelstein 7-X, and JET. No formal awards are listed in the provided text. He leads a research team focused on advancing plasma diagnostic capabilities for future fusion reactors.

Jie DingView profile

Associate Professor

• Artificial Intelligence
• Statistics
• Scientific Computing
• +6 more

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.

• Battery Management Systems
• Electric Vehicles
• Lithium-ion Batteries
• +14 more

Professor Weixiang Shen is a full Professor in the Department of Engineering Technologies at the School of Engineering, Swinburne University of Technology, Melbourne, Australia. He holds a PhD in Electrical Engineering and has extensive academic experience across China, Germany, Singapore, and Malaysia. Bachelor of Engineering (BEng), Electrical Engineering Master of Engineering (MEng), Electrical Engineering Doctor of Philosophy (PhD), Electrical Engineering His research focuses on battery management systems for electric vehicles and renewable energy integration. Key areas include battery capacity estimation, charging strategies, fault diagnosis, thermal and mechanical management, and grid integration of EVs. He employs advanced techniques such as physics-informed machine learning, electrochemical modeling, and multidimensional sensing to enhance battery safety and performance. His recent publications (2024–2025) reveal a strong trend toward intelligent battery health monitoring, degradation modeling, and innovative thermal management using origami-inspired designs. These works appear in top journals like Applied Energy , IEEE Transactions , and Journal of Energy Chemistry . Associate Editor, IET Power Electronics Editorial Board Member, Journal of Energy Storage Founding Board Member, Vehicles Guest Editor, Applied Energy , Journal of Cleaner Production , Energy General Chair, ICEEE2018 Program Chair, CoEEPE2023 Professor Shen actively supervises PhD and Master’s students in areas such as battery fault diagnosis, energy storage, and control systems. His research is supported by grants from the Australian Research Council, Department of Industry, Science and Resources, and industry partners. He leads projects on intelligent energy management, battery testing, and EV control systems. His lab explores cutting-edge topics including solid-state batteries, multidimensional sensing, and origami-based thermal solutions for battery packs.