جست‌وجوی استاد و پژوهشگر

Rex Yingمشاهده پروفایل

استادیار

• Graph Neural Networks
• Geometric Representation Learning
• Explainable AI
• +۳ مورد دیگر

Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.

Jonathan D Victorمشاهده پروفایل

استاد

• Computational Neuroscience
• Neurophysiology
• Sensory Physiology
• +۳ مورد دیگر

Jonathan D Victor is Professor of Computational Neuroscience in Computational Biomedicine at the Institute for Computational Biomedicine, Professor of Neuroscience at the Brain and Mind Research Institute, and Fred Plum Professor of Neurology within the Department of Neurology at Weill Cornell Medical College (Cornell University). He serves as an Attending Neurologist at NewYork-Presbyterian Hospital and directs the Division of Systems Neurology and Neuroscience. His educational background includes: B.A. from Harvard University (1973) Ph.D. in Neurophysiology from The Rockefeller University (1979) M.D. from Cornell University Medical College (1980) Victor's research integrates computational neuroscience with clinical neurology, focusing on neural coding in sensory systems (vision, taste, smell), disorders of consciousness, and eye movement dynamics. He employs information theory, statistical modeling, and electrophysiology to decode how the brain processes natural stimuli and recovers from injury, with emphasis on perceptual spaces and neural network signatures. Analysis of his 2023-2025 publications reveals three dominant trends: computational modeling of sensory-motor transformations in natural behaviors (e.g., Drosophila locomotion), geometric frameworks for perceptual spaces (visual texture, depth processing), and EEG-based biomarkers for traumatic brain injury recovery (alpha coherence, thalamic stimulation). No scientific awards were explicitly documented in the source material. He secures major NIH/Foundation grants as Principal Investigator or Co-Investigator, including National Eye Institute projects on fixational eye movements (2022-2026), James S. McDonnell Foundation's Covid-19 consciousness recovery consortium (2020-2025), and National Cancer Institute's NeuroNex olfactory behavior study (2020-2026). These fund translational work bridging neural coding theory with clinical applications. Victor leads the Division of Systems Neurology and Neuroscience, leveraging collaborations with Weill Cornell's Brain and Mind Research Institute and Institute for Computational Biomedicine. His external relationships include consulting for Elsevier and Springer Science and Business Media on scientific publishing and data analysis.

Travis Mitchellمشاهده پروفایل

مدرس

• Computational fluid dynamics
• Lattice Boltzmann methods
• Multiphase fluid dynamics
• +۳ مورد دیگر

Dr. Travis Mitchell is a Lecturer at the School of Mechanical and Mining Engineering , The University of Queensland , and an affiliate of the Centre for Multiscale Energy Systems . He holds a PhD in Multiphase Computational Fluid Dynamics and dual degrees in Mechanical Engineering (BE Hons) and Mathematics (BSc). Education: PhD in Multiphase Computational Fluid Dynamics, The University of Queensland BE (Hons) in Mechanical Engineering, The University of Queensland BSc in Mathematics, The University of Queensland Research Interests focus on numerical modeling of multiphase fluid dynamics in porous media , with applications spanning CO2 electrolysis , hydrogen production via methane pyrolysis , biomedical fluid-structure interaction , and geomechanical fracture analysis . His methodological expertise includes Lattice Boltzmann techniques and high-performance computing . Recent Work Trends encompass multiphase transport in fractured media , gas diffusion electrode optimization , fiber-based air filter design , and thermocapillary flow modeling , reflecting his interdisciplinary impact in energy, health, and resource engineering. Scientific Recognition includes the ICMMES-CSRC Award for multiphase lattice Boltzmann research and an EAIT Citation for Excellence in Student Learning (2023) . Teaching Portfolio includes coordination of MECH2700: Computational Engineering and Data Analysis and lectures in MECH3780: Computational Mechanics and MECH6480: Computational Fluid Dynamics .

Lukasz Laniewski-Wollkمشاهده پروفایل

پژوهشگر ارشد

• Computational Fluid Dynamics
• Lattice Boltzmann Methods
• Aerodynamic Optimization
• +۳ مورد دیگر

Dr. Lukasz Laniewski-Wollk serves as an Honorary Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, where he conducts advanced research in computational fluid dynamics and optimization methodologies. His work bridges theoretical development with practical applications in energy systems and resource extraction. His primary research domains encompass: Computational Fluid Dynamics Lattice Boltzmann Methods Aerodynamic Optimization Multiphase Flow Fracture Mechanics Particle Transport Analysis of his 2020-2025 publications reveals a concentrated effort on developing high-fidelity computational frameworks for complex fluid phenomena, particularly in multiphase systems and fractured media. His methodologies demonstrate strong interdisciplinary integration between fluid mechanics, materials science, and petroleum engineering, with significant contributions to lattice Boltzmann variants for non-Newtonian fluids and fracture permeability quantification. Collaborative patterns indicate sustained partnerships with UQ's computational mechanics group and international energy research consortia.

Pawel Danielewiczمشاهده پروفایل

استاد

• Nuclear Theory
• Heavy-Ion Collisions
• Symmetry Energy
• +۴ مورد دیگر

Pawel Danielewicz is a Professor in the Department of Physics & Astronomy at Michigan State University, affiliated with the National Superconducting Cyclotron Laboratory (NSCL). His research focuses on nuclear theory, particularly the dynamics of heavy-ion collisions and the equation of state for dense nuclear matter. Education: Ph.D., University of Warsaw (1981) His work explores high-pressure nuclear environments created in central heavy-ion reactions, using transport theory to model compression, thermalization, and decompression phases. Key areas include symmetry energy , isospin diffusion , and quantum transport with applications to neutron stars and ultracold quantum gases. Recent publications emphasize deblurring techniques for source function reconstruction, optical potentials for rare-isotope beams, and equation of state constraints from GW170817. Articles from 2025-2022 span topics like pion production , neutron skins , and nonequilibrium Green's functions . His theoretical frameworks connect heavy-ion collision observables to nuclear matter properties, with cross-disciplinary implications for early cosmology and atomic traps via nonequilibrium dynamics.

Yajing Liuمشاهده پروفایل

دانشیار

• Earthquake Seismology
• Fault Mechanics
• Seismic Hazard Assessment
• +۳ مورد دیگر

Yajing Liu is an Associate Professor and Canada Research Chair in Earthquake Seismology at McGill University's Department of Earth and Planetary Sciences. Her research focuses on earthquake mechanics, fault behavior, and anthropogenic seismicity, particularly induced by hydraulic fracturing. She combines geophysical observations with advanced numerical modeling to improve earthquake hazard prediction. Education: PhD (2007, Harvard University) and BSc (2001, Peking University). Professional experience includes roles at Woods Hole Oceanographic Institution and Princeton University. She leads the SCEC SEAS benchmark project, advancing community code verification for earthquake and aseismic slip simulations. Notable work includes discovering a novel type of slow-slip hydraulic fracturing-induced earthquakes in British Columbia and studying induced seismicity links to methane emissions. Research emphasizes subduction zones, fault geometry effects, and crustal stress dynamics. She has advised PhD student John Onwuemeka (JGR 2021) and secured NSERC strategic grants. Current projects explore seismic hazard mitigation in unconventional energy extraction and fault system interactions. Key awards: Canada Research Chair in Earthquake Seismology Labs/teams: SEAS community code project, McGill seismic hazard research group

• Laser Physics
• Optics and Laser Technology
• Scintillator Materials
• +۴ مورد دیگر

Emőke Lőrincz is an Honorary Professor in the Department of Atomic Physics at Budapest University of Technology and Economics (BME). Her research focuses on advanced optical and nuclear technologies, including scintillator materials for medical imaging (PET), laser physics, and holographic data storage. She has contributed to the development of high-resolution PET detectors, digital photon counter systems (SPADnet), and novel optical memory technologies using azobenzene polymers. Her work bridges fundamental material science with applied engineering solutions for biomedical and data storage applications. Key research areas include optimizing light-sharing modules for PET detectors, measuring optical properties of scintillator crystals, and advancing polarization-based holographic storage systems. Her interdisciplinary approach combines experimental validation with computational modeling to enhance detector performance and data density in optical systems. Publications span from 1985 to 2017, reflecting her long-term contributions to optics, nuclear instrumentation, and materials science. Notable collaborations include studies on LYSO:Ce scintillators, SiPM arrays, and phase-modulation techniques for holography. While no specific awards are listed, her extensive publication record underscores her influence in these fields. Her work often addresses practical challenges such as improving detector spatial resolution, optimizing light collection efficiency, and ensuring material durability in extreme conditions. This research has direct applications in medical imaging systems and next-generation optical storage technologies.

Daniel Matthesمشاهده پروفایل

استاد

• Partial Differential Equations
• Mathematical Analysis
• Gradient Flow Systems
• +۳ مورد دیگر

Daniel Matthes is a Professor of Dynamical Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. His research focuses on the qualitative theory of partial differential equation solutions, with particular expertise in gradient flow systems and kinetic equations. Matthes received his education at TU Berlin, where he completed his Diplom in Mathematics in 1999 and his PhD in 2003 with a thesis on Discrete Surfaces and Coordinate Systems: Approximation Theorems and Computation . He conducted postdoctoral research at the University of Mainz, the University of Pavia in Italy, and TU Vienna, before completing his Habilitation at TU Wien in 2010 with work on On the equilibration in certain kinetic and diffusion equations . Professor Matthes' research interests span the mathematical analysis of partial differential equations, particularly focusing on gradient flow structures, kinetic models, and their applications. His work bridges theoretical mathematics with practical applications in areas such as mathematical physics and economic modeling. He has made significant contributions to understanding the qualitative behavior of solutions to nonlinear PDEs, with particular emphasis on convergence properties, equilibrium states, and numerical approximation methods. His methodology often combines rigorous analytical techniques with computational approaches to extract meaningful insights from complex mathematical structures. His extensive publication record demonstrates a consistent focus on gradient flow systems, with recent work exploring discretization methods, convergence analysis, and applications to physical and economic models. Matthes has developed innovative numerical schemes for solving complex PDEs while maintaining important structural properties of the continuous equations, contributing significantly to both theoretical understanding and practical computational methods in the field. As part of the Dynamical Systems group at TUM, Professor Matthes contributes to the department's research in mathematical analysis and its applications. His work intersects with several research areas within the Department of Mathematics, including numerical analysis, mathematical physics, and applied analysis. The group maintains active collaborations with researchers across Europe and participates in various interdisciplinary projects that apply advanced mathematical techniques to real-world problems.

Mitko Aleksandrovمشاهده پروفایل

پژوهشگر

• Geospatial Information Systems
• Three-Dimensional Modeling
• Indoor Navigation
• +۷ مورد دیگر

Dr. Mitko Aleksandrov serves as a Casual Academic and Research Associate at the University of New South Wales (UNSW), working with Geospatial Research Innovations and Development (GRID) within the School of Built Environment under the Faculty of Arts, Design & Architecture. With specialized expertise in geospatial information systems and three-dimensional modeling, Dr. Aleksandrov contributes to cutting-edge research in indoor navigation, spatial analysis, and building information modeling applications for urban planning and safety. Dr. Aleksandrov holds a Master's degree in GIS (Geographic Information Systems), though specific details about his educational institutions and timeline are not provided in the available information. His academic background has positioned him at the forefront of geospatial research with practical applications in built environments. Specializing in three-dimensional modeling and navigation systems for indoor environments, Dr. Aleksandrov's research spans geospatial analysis, building information modeling, and spatial data structures. His work focuses on evacuation planning, crowd simulation, and hazard detection through voxel-based representations of 3D spaces, visibility analysis, and the integration of BIM with geospatial data. His research bridges computer science, civil engineering, and urban design to create innovative solutions for complex spatial problems in built environments, particularly addressing safety and navigation challenges in complex buildings. Dr. Aleksandrov's publication record demonstrates a clear progression from foundational research on voxelization algorithms toward practical applications in evacuation planning and pedestrian safety. His recent work shows increasing interdisciplinary collaboration, integrating computer vision techniques with geospatial analysis to address urban challenges. He has made significant contributions to indoor navigation systems, 3D modeling for evacuation scenarios, and the application of voxel-based representations in building information modeling, with publications appearing in high-impact journals across geospatial science, computer science, and civil engineering domains. While specific awards are not mentioned in the available information, Dr. Aleksandrov's research impact is evidenced by his consistent publication record in reputable journals including Transactions in GIS, Journal of Spatial Science, and ISPRS Annals. His work has contributed to advancing methodologies in 3D geoinformation and spatial analysis for practical urban applications. Dr. Aleksandrov actively collaborates with researchers across UNSW and internationally, particularly with Sisi Zlatanova, Jennifer Barton, and other members of the geospatial research community. His publications indicate involvement in significant research projects, potentially including the CRC-LCL project referenced in his 2020 work. While specific student supervision details aren't provided, his research program suggests engagement with graduate students in the geospatial and built environment fields. Working with Geospatial Research Innovations and Development (GRID) at UNSW, Dr. Aleksandrov is part of a dynamic research team focused on advancing geospatial technologies for urban applications. His involvement in organizing academic conferences such as the 3D Geoinfo Conference and GEOINFORMATION for DISASTER MANAGEMENT demonstrates his leadership within the research community. His work with voxel-based modeling in UNITY3D suggests engagement with visualization technologies that bridge academic research and practical applications in urban planning and emergency response.

• Informatics
• Perceptual Information Processing
• Computer Vision
• +۳ مورد دیگر

SAKAUE Fumihiko is an Associate Professor at the Faculty of Engineering, Department of Information Engineering at Okayama University. He holds a Doctor of Engineering degree from Okayama University, awarded in 2006. His research focuses on innovative computer vision and computational photography techniques, aiming to develop novel methods for 3D measurement and real-time motion analysis without relying on conventional cameras or computers. His work integrates light-ray processing and spatiotemporal modulation to enhance visual information extraction and has contributed significantly to fields such as pattern recognition and imaging systems. Education background includes graduation from Okayama University's Faculty of Engineering in 2001, followed by earning his Doctor of Engineering degree from the same institution in 2006. His research interests span several key areas in informatics and engineering, including: Perceptual information processing for dynamic visual analysis Computer vision applications in 3D reconstruction and motion capture Computational photography techniques for advanced imaging systems Pattern recognition methodologies in unstructured environments Development of light-based measurement systems without conventional sensors Integration of light projection and observation for real-time data interpretation SAKAUE has received numerous awards, including: MIRUインタラクティブ発表賞 (2024) Best student paper award at International Workshop on Frontiers of Computer Vision (2022) Best paper award at INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (2022) MIRU2018長尾賞 (2018) 山下記念研究賞 (2018) CGVI研究会優秀研究発表賞 (2017) ICPR Best Student Paper Award (2016) MIRUインタラクティブ発表賞 (2019) MRU2016インタラクティブ発表賞 (2016) In terms of advising, SAKAUE has guided notable students including Toshiki Kamiya (recipient of 2022 Best Student Paper Award) and Yuma Nishikawa (2024 Best Student Paper award recipient). He has secured multiple grants from the Japan Society for the Promotion of Science (JSPS), including a 2020-2023 grant focused on 3D motion visualization through spatiotemporal light-ray integration, and a 2016-2019 grant on 5D light field display technologies. Additional funding includes a 2022-2025 project with Aichi Prefecture for AI-driven textile inspection systems. SAKAUE collaborates with interdisciplinary research teams at Okayama University, focusing on developing innovative imaging systems and sensorless vision technologies. His work often involves partnerships with industry through patents and applied research projects, such as the 'Position Measurement Device' (2016) and 'Three-Dimensional Information Presentation Device' (2014), demonstrating practical applications of his theoretical research.

Matthew Hiattمشاهده پروفایل

دانشیار

• Coastal Hydrology
• Delta Morphodynamics
• Water Connectivity
• +۲ مورد دیگر

Dr. Matthew Hiatt is Associate Professor in the Department of Oceanography & Coastal Sciences at Louisiana State University's College of the Coast and Environment. He researches coastal hydrology, delta morphodynamics, and hydrological connectivity using field measurements and numerical modeling. Education: Ph.D. in Civil Engineering, University of Texas at Austin (2016) M.S.E. in Civil Engineering, University of Texas at Austin (2013) B.S.C.E. in Civil Engineering, University of Kansas (2011) His work examines water transport timescales, river delta evolution, and estuarine responses to atmospheric forcing. Current projects investigate cold front impacts on delta circulation, network connectivity in braided estuaries, and hurricane effects on mangrove nutrient cycles. Field sites include the Wax Lake Delta and Barataria Bay in Louisiana. Publications demonstrate expertise in hydrodynamic modeling, remote sensing of channel networks, and interdisciplinary approaches to coastal change. Recent studies focus on automated extraction of channel geometry, three-dimensional flow structures, and ecological implications of water connectivity. The Coastal H2O Research Group employs real-time monitoring systems, drone surveys, and computational models. Teaching includes courses on environmental fluid dynamics and coastal observation techniques.

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

• Implant Medicine and Dentistry
• Implant Placement
• Oral Implants
• +۵ مورد دیگر

Tiago Borges is a Researcher at the Center for Interdisciplinary Research in Health (CIIS) at Universidade Católica Portuguesa. His work primarily focuses on dental implantology and oral surgery, with significant contributions to systematic reviews and meta-analyses in the field of implant dentistry. He maintains an active research profile with 36 publications, an h-index of 9, and 217 Scopus citations. Dr. Borges specializes in several key research areas within dentistry: Implant Medicine and Dentistry Implant Placement techniques Systematic Review methodology Meta-Analysis of clinical trials Oral Implants Marginal Bone Loss Randomized Clinical Trials Clinical Study methodology His recent publication trend shows a strong emphasis on evidence-based approaches to dental implant procedures, with systematic reviews examining immediate implant placement in damaged extraction sockets, comparative analysis of zirconia versus titanium implants, and the use of tissue grafts for peri-implant stability. His research methodology consistently employs rigorous meta-analytical techniques to evaluate clinical outcomes and treatment efficacy in implant dentistry. Dr. Borges has supervised several students including Nunes, S. M. V. who researched customized healing abutments for immediate dental implants, and Ramos, S. P. F. F. who conducted three-dimensional evaluations of palate healing after connective tissue graft harvesting. He is an active participant in research projects such as "InDig: Innovation and digital teaching technologies in Dental Medicine" (Portugal2020, 2022-2023) and the broader "CIIS: Center for Interdisciplinary Research in Health" initiative (Fundação para a Ciência e a Tecnologia, 2020-2024), where he contributes to advancing dental education and interdisciplinary health research.

Carl Salvaggioمشاهده پروفایل

استاد

• Image Processing
• Computer Vision
• Remote Sensing
• +۱۷ مورد دیگر

Carl Salvaggio is a Professor at the Rochester Institute of Technology within the Chester F. Carlson Center for Imaging Science , College of Science . He serves as Director of the Digital Imaging and Remote Sensing Laboratory , focusing on applied solutions using imaging modalities and algorithmic approaches. His expertise spans thermal infrared phenomenology, 3D geometry extraction, material property modeling, and UAV sensor calibration. Education : Not explicitly mentioned Research Interests : Image processing, computer vision, remote sensing, thermal/hyperspectral imaging, and UAS applications in precision agriculture and infrastructure inspection. Article Trends : Recent work emphasizes UAV-based remote sensing (2017-2021), with applications in agriculture , ecological monitoring , and deep learning architectures . Key subfields include 3D reconstruction , sensor calibration , and hyperspectral analysis , reflecting interdisciplinary engagement with computer science, environmental science, and aerospace engineering. Scientific Recognition : Eisenhart Award for Excellence in Teaching (2024) Leadership : Principal Investigator for RIT's Signature Interdisciplinary Research Areas/Center for Unmanned Aircraft Systems Research , driving innovations in radiometric calibration and interdisciplinary collaboration. His Digital Imaging and Remote Sensing Laboratory provides public services in data collection and equipment calibration. He has mentored students in advanced imaging projects and co-authored publications with researchers like Baabak Mamaghani and Christopher Kanan.

Xiaojing Yuanمشاهده پروفایل

استاد

• BioMedical Signal and Image Processing
• Intelligent System Modeling and Simulation
• Distributed Sensor Networks
• +۳ مورد دیگر

Dr. Xiaojing (Jane) Yuan is a Professor in the Department of Engineering Technology at the University of Houston’s Cullen College of Engineering. She specializes in biomedical signal processing, intelligent systems, and wireless sensor networks. Her research focuses on applications in health informatics, disease management, and sensor network optimization. Education Ph.D. in Robotics and Automation, Tulane University (2003) M.S. in Computational Intelligence, Tulane University (2002) M.S. in Computer-Aided Automation, University of Science and Technology of China (1997) B.S. in Electrical Engineering, Hefei University of Technology (1994) Research Interests Dr. Yuan’s work spans biomedical signal/image processing, intelligent system modeling, and distributed sensor networks for health monitoring. She develops algorithms for skin cancer detection and wireless sensor network protocols for chronic disease management. Her lab, the Intelligent Sensor Grid and Informatics Lab, integrates hardware and software solutions for real-world applications. Grants & Projects Principal Investigator (PI) of projects totaling over $200k in funding, including grants from NASA, NSF, and the Texas Emerging Technology Fund Co-PI for interdisciplinary initiatives like the NSF NeTS-NOSS program and NASA’s ISHM testbed Labs & Teams Director of the Intelligent Sensor Grid and Informatics Lab and advisor for the International Society of Automation (ISA).