جست‌وجوی استاد و پژوهشگر

• Language Processing
• Cognitive Neuroscience
• Neuropsychology
• +۳ مورد دیگر

Marta Kutas is a Distinguished Professor in the Department of Cognitive Science at the University of California, San Diego (UCSD), where she has held roles including Department Chair (2007–2019), Director of the Center for Research in Language (2007–2022), and Chancellor's Associates Endowed Chair (2017–2022). Her research focuses on language processing, neuropsychology, and electrophysiological methods, with a particular emphasis on event-related potentials (ERPs). She has contributed significantly to understanding semantic integration, memory, and neural mechanisms underlying language comprehension. Education includes a B.A. from Oberlin College (1971) and a Ph.D. in Psychology from the University of Illinois (1977). She has held adjunct roles at San Diego State University and the UCSD Department of Neurosciences. Her awards include the Revelle Medal (2023), membership in the American Academy of Arts and Sciences (2018), and the Cognitive Neuroscience Society's Distinguished Career Award (2015). Her research interests span language comprehension/production, neuropsychology, and ERP methodologies. Key publications analyze semantic processing, memory-related brain potentials, and the impact of individual knowledge on word processing. She has collaborated on studies involving Alzheimer's, Parkinson's, and schizophrenia, using ERPs to explore cognitive deficits.

• Artificial Intelligence
• Battery Technology
• Digitalization
• +۷ مورد دیگر

Prof. Dr.-Ing. Weihan Li is a Junior Professor at RWTH Aachen University, specializing in Artificial Intelligence and Digitalization for Batteries. He is affiliated with the Institute for Power Electronics and Electrical Drives (ISEA) and the Center for Ageing, Reliability, and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL). His research bridges informatics, electrochemistry, and power electronics to advance battery technology through AI. B.Sc. in Automotive Engineering (Tongji University, 2014) M.Sc. in Automotive Engineering and Transport (RWTH Aachen, 2017) Ph.D. in Electrical Engineering and Information Technology (RWTH Aachen, 2021, summa cum laude) Prof. Li’s research focuses on AI-driven battery modeling, diagnostics, and optimization. Key areas include digital twin technology, electrochemical parameterization, and lifetime prediction using field data. He explores multi-scale kinetic processes, thermal management, and mechanical-electrochemical coupling effects in battery systems. The articles listed reflect his leadership in AI-powered battery analytics, spanning degradation prediction, fast charging, failure mode analysis, and grid-scale storage. His work emphasizes both theoretical innovation (e.g., diffusion models, physics-informed neural networks) and practical applications (e.g., second-life battery screening, automotive integration). Clarivate Highly Cited Researcher 2024 BMBF BattFutur Research Group (€2M+) German Thesis Award (Körber Foundation) Reichart Prize vgbe Innovation Prize Battery Young Research Award Umbrella Award RWTH Innovation Award Prof. Li leads an interdisciplinary research group with over €6 million in grants from BMBF, BMWK, BMDV, European Commission, and industry partners. His teams focus on battery informatics, AI-driven diagnostics, and digitalization of testing processes at CARL and ISEA.

Yelda Turkanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Lidar Applications
• +۷ مورد دیگر

Dr. Yelda Turkan is an Associate Professor in the School of Civil and Construction Engineering at Oregon State University, where she leads research in automation, computer vision, and machine learning for sustainable infrastructure. She holds a PhD from the University of Waterloo and dual BS degrees in Civil Engineering and Geomatics Engineering from Istanbul Technical University. Her work focuses on leveraging lidar, digital twins, and BIM to improve construction operations and decision-making in the built environment. She has secured over $4M in grants from NSF, FHWA, and other agencies, and currently leads the NSF Convergence Accelerator-funded 'Deep Reality' project for AI-driven infrastructure management. Education: Ph.D., Civil Engineering, University of Waterloo, 2012 M.S., Engineering Informatics & Remote Sensing, Istanbul Technical University, 2006 B.S., Civil Engineering (double major in Geomatics Engineering), Istanbul Technical University, 2005/2003 Professional Roles: Vice President, International Association for Automation and Robotics in Construction (IAARC) Chair, ASCE Computing Division Education Committee Associate Editor, ASCE OPEN Journal Her research emphasizes automation in construction quality control, infrastructure inspection via drones and lidar, and immersive education tools using VR/AR. Recent projects include automated curb ramp compliance analysis, wildfire impact modeling, and digital twin development for timber structures. She has published over 80 peer-reviewed articles and actively promotes computing integration in civil engineering education and professional practice.

Shima Abdullateefمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Medical Informatics
• Computational Physiology
• +۳ مورد دیگر

Shima Abdullateef is a Postdoctoral Research Fellow at the Centre for Medical Informatics within the Usher Institute, College of Medicine and Veterinary Medicine at the University of Edinburgh. Her work bridges biomedical engineering and clinical medicine through computational modeling and data science applications. Education: PhD in Biomedical Engineering, Brunel University London (2016-2020) MSc in Biomedical Engineering, University of Surrey (2014-2015) BSc in Biomedical Engineering (Bioelectrics), Science and Research IA University (awarded 2013) Research Focus: Dr. Abdullateef specializes in two interconnected domains: computational hemodynamics modeling arterial wave propagation and reflection phenomena, and machine learning-driven seizure detection using minimal-density EEG montages. Her arterial research investigates how vascular geometry impacts blood pressure dynamics, while her neuroscience work develops practical clinical tools for critical care seizure monitoring that reduce electrode requirements by 50-75% compared to standard EEG setups. Publication Trends: Her 15 most recent publications (2018-2025) reveal a strategic shift from pure cardiovascular modeling toward integrated neurological applications, with 60% focusing on seizure detection algorithms. The work consistently applies one-dimensional computational models and phase-synchrony analysis to solve clinical monitoring challenges, particularly in resource-constrained pediatric intensive care settings. Active Projects: A Window in the Brain: Developing a novel seizure detection tool for pediatric critical care (since 2020), funded through University of Edinburgh research channels Collaborative Environment: She operates within the Centre for Medical Informatics' interdisciplinary ecosystem, collaborating with clinicians from Edinburgh BioQuarter and data scientists to translate engineering solutions into clinical practice, with particular emphasis on making neurocritical care monitoring more accessible through reduced-sensor EEG technology.

Michael Levineمشاهده پروفایل

استاد

• Genomics
• Molecular Biology
• Developmental Biology
• +۷ مورد دیگر

Michael Levine is the Anthony B. Evnin '62 Professor in Genomics and Professor of Molecular Biology at Princeton University, where he also serves as Director of the Lewis-Sigler Institute for Integrative Genomics. He joined Princeton in 2015 after a distinguished career at UC Berkeley, where he was Professor of Genetics and held leadership roles in genetics and genomics programs. His research focuses on how noncoding regions of the genome regulate gene expression in space and time during development. His lab has pioneered studies in Drosophila and the protovertebrate Ciona intestinalis , uncovering fundamental mechanisms such as enhancer function, transcriptional bursting, short-range repression, and long-range enhancer-promoter interactions. His work has also revealed evolutionary insights into the origins of vertebrate innovations like the neural crest and neurogenic placodes. Levine's recent publications demonstrate a strong emphasis on quantitative and live-imaging approaches to dissect gene regulation dynamics. His work integrates experimental embryology with computational modeling, especially using deep learning to predict transcriptional outcomes. Themes across his recent articles include biomolecular condensates, chromatin architecture, and the physical principles underlying enhancer function. Elected to the National Academy of Sciences (1998) Molecular Biology Award, National Academy of Sciences (1996) Wilbur Cross Medal, Yale University (2009) EG Conklin Medal, Society of Development Biology (2015) Dr. Levine has trained numerous researchers and co-authored studies with emerging scientists, indicating active mentoring and grant-funded research. His leadership roles at major institutes and sustained publication record reflect a robust, well-supported research program. He has also contributed to national genomics initiatives, including service at the DOE Joint Genome Institute. His lab operates at the intersection of molecular biology, genomics, and quantitative developmental biology, utilizing model organisms and cutting-edge imaging and computational tools to unravel the logic of gene regulatory networks.

• AI in Healthcare
• Electronic Health Records Analysis
• Biomedical Time Series Analysis
• +۶ مورد دیگر

Dr. Kezhi (Ken) Li is an Associate Professor of AI in Healthcare at the Institute of Health Informatics, University College London (UCL). He leads the AI for Health research group and has established himself as a leading expert in applying artificial intelligence to solve complex problems in healthcare, with over 130 publications in leading journals (total impact factor greater than 400). Dr. Li earned his Doctor of Philosophy from Imperial College of Science, Technology and Medicine in 2013, followed by research positions at the Medical Research Council (2015-2017), University of Cambridge (2014-2015), and Royal Institute of Technology (KTH) (2013-2014). His academic journey reflects a consistent trajectory from technical AI research toward increasingly healthcare-focused applications. Dr. Li's research focuses on solving physiological, medical, clinical, and operational problems in healthcare using AI techniques. His specific expertise includes AI in healthcare using electronic health records (EHR), biomedical time series analysis using monitors/wearables, diabetes management, large language models (LLM) in healthcare (especially mental health), patient flow optimization, and digital health with federated learning. His work bridges the gap between cutting-edge AI methodologies and practical healthcare applications, with a strong focus on improving patient outcomes and healthcare system efficiency. Analysis of Dr. Li's publication history reveals a strong emphasis on diabetes management technologies, particularly blood glucose prediction systems using advanced neural network architectures. More recently, his work has expanded into mental health applications of large language models, blockchain-based federated learning for healthcare data, and mortality prediction in critical care settings. His research demonstrates a clear evolution from purely technical AI development toward increasingly clinically impactful applications, with growing emphasis on explainability, privacy preservation, and real-world implementation challenges. Dr. Li has received numerous prestigious awards recognizing his contributions to healthcare AI: Best Application Award of IEEE Global Blockchain Conference (2025) Fellow of British Computer Society (2025) Fellow of the Royal Society for Public Health (2024) Healthcare Partnership of the Year category at the London Higher Awards (2024) ECR Promising Project Award (2023) Gallivan Award finalists (2022) Stylianos Kalaitzis PhD Award Winner (2022) HDR UK Team of the Year (COVID-19) Award (2021) As an educator, Dr. Li serves as the Director of MRes study (AI-enabled Healthcare Systems) at UCL. He leads multiple key modules including Healthcare Artificial Intelligence Journal Club, Dissertation in Artificial Intelligence Enabled Healthcare, and Advanced Machine Learning for Healthcare. His supervision extends across dissertation projects and junior researchers in his AI for Health group. His research has been supported by various grants, including those from HDR UK, focusing on translating AI innovations into practical healthcare solutions. Dr. Li leads the AI for Health research group (https://ai4hucl.github.io/ai4h_webs/), which comprises researchers with diverse expertise in machine learning, healthcare systems, and clinical domains. The group maintains strong collaborations with healthcare providers and industry partners to ensure their research addresses real-world healthcare challenges and can be effectively translated into clinical practice.

• Gravitational Wave Data Analysis
• Cosmological Parameter Estimation
• LISA Data Analysis
• +۳ مورد دیگر

Dr. Jonathan Gair is a Group Leader in the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. Previously, he served as Professor of Astrostatistics at the University of Edinburgh (2018-2019) and as Reader (Associate Professor) in Statistics at the same institution (2015-2018). Dr. Gair's research focuses on gravitational wave data analysis and its applications to cosmology and fundamental physics. His work spans multiple areas of gravitational wave astronomy, with particular emphasis on: Developing and applying new methodologies for gravitational wave data analysis Using gravitational wave observations to derive cosmological parameters, particularly the Hubble constant Developing data analysis tools for the LISA space-based gravitational wave detector Exploring the scientific potential of gravitational wave observations for testing general relativity Creating computationally efficient techniques for parameter inference in gravitational wave astronomy Dr. Gair plays a leading role within the LIGO/Virgo collaboration in deriving cosmological constraints from gravitational wave observations. He currently chairs the LISA Science Group, overseeing the development of data analysis tools for the planned ESA-led LISA mission. His research has significantly contributed to our understanding of how gravitational wave observations can serve as "standard sirens" for measuring cosmic distances and probing the expansion history of the universe. Dr. Gair's work involves both theoretical development and practical application of data analysis techniques. He has developed methods for handling selection effects in rate estimation of gravitational wave events, techniques for mapping gravitational wave backgrounds using methods adapted from cosmic microwave background analysis, and approaches for incorporating model uncertainties into gravitational wave parameter estimation.

• Networking
• Communications
• Machine Learning
• +۷ مورد دیگر

Christopher G. Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. He is affiliated with the Department of Electrical and Computer Engineering in the College of Engineering at Purdue University's West Lafayette campus. Dr. Brinton received his PhD from Princeton University, where he was previously the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering. His research focuses on the intersection of networking, communications, and machine learning, with particular emphasis on Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. His research integrates foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in networked intelligent systems. The ION lab under his leadership develops both theoretical frameworks and practical implementations for next-generation networking solutions, with strong industry collaborations including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson. Recent publications reveal a strong trend toward federated learning, decentralized algorithms, and edge intelligence, with significant contributions to model partitioning, communication-efficient learning, and robust network architectures. His work increasingly bridges traditional communication theory with modern machine learning techniques to solve emerging challenges in distributed networked systems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton teaches several courses including ECE 647: Performance Modeling of Computer Communication Networks, ECE 301: Signals and Systems, and ECE 547: Introduction to Computer Communication Networks. He has co-authored the book 'The Power of Networks: Six Principles That Connect Our Lives' and taught three Massive Open Online Courses (MOOCs) with over 400,000 cumulative students. While not currently actively recruiting students, he remains open to connecting with highly motivated individuals. Dr. Brinton leads the ION (Intelligent Optimization and Networking) research lab, which focuses on creating theoretical foundations and practical implementations for next-generation networked systems. The lab has recently published significant work on 6G taxonomy in collaboration with major industry partners and continues to push boundaries in distributed learning and network optimization.

SangHyung Ahnمشاهده پروفایل

مدرس

• Construction Process Modelling
• Building Information Modelling (BIM)
• Agent-Based Simulation
• +۳ مورد دیگر

SangHyung Ahn is a Lecturer at the School of Civil Engineering , University of Queensland (UQ), since 2017. He joined UQ as a postdoctoral research fellow in 2015 after earning his PhD in Civil Engineering (Construction Engineering and Management) from Purdue University, USA. Prior to his academic career, he worked as an assistant manager at Hyundai Engineering and Construction Co., Ltd. (2003-2007) and holds an MBA in international business from Hanyang University and a B.Sc in Civil Engineering from Korea University. Research Focus: Construction process modelling with virtual reality, decision support systems for construction, automation of data-driven simulation modelling, sensor-based operations analysis, and integration of Building Information Modelling (BIM). Teaching: Coordinates undergraduate courses Introduction to Project Management (CIVL3510) and Construction Engineering Management (CIVL4522) . Research Trends: His recent publications highlight interdisciplinary work in transportation engineering, structural design, and AI-driven simulation tools. Key themes include application of machine learning to car-following models, drone-based vehicle identification, and optimization of public transport systems using agent-based simulations. Supervision: Available for supervision, with completed supervision of PhD and Master’s theses on topics such as BIM-LCA integration, pedestrian trajectory analysis, and AI-driven driving behavior models.

• X-ray Physics
• Synchrotron Radiation
• Medical Imaging
• +۵ مورد دیگر

Benedikt Günther is a research scientist at the Technical University of Munich (TUM) working within the Chair of Biomedical Physics led by Prof. Dr. Franz Pfeiffer. His research focuses on the Munich Compact Light Source (MuCLS), a laboratory-scale inverse Compton X-ray source that provides synchrotron-like radiation for biomedical applications. Günther plays a key role in developing, optimizing, and characterizing this innovative technology, contributing to both its fundamental physics and practical medical applications. His primary research interests center around X-ray physics and imaging techniques, particularly laser enhancement cavities for inverse Compton X-ray sources, X-ray microscopy, dynamic phase-contrast imaging, and X-ray spectroscopy. Günther's work bridges fundamental physics with practical medical applications, developing instrumentation that brings synchrotron-quality imaging to conventional laboratory settings. His research has significant implications for improving medical diagnostics while making advanced imaging techniques more accessible. Analysis of Günther's publication record reveals a consistent focus on advancing compact X-ray source technology and its applications. His work demonstrates expertise in both theoretical modeling and experimental implementation, with publications spanning instrument development, imaging techniques, and specific medical applications. The research shows progression from fundamental source characterization to increasingly sophisticated biomedical applications, particularly in breast imaging, dental diagnostics, and materials science. 2019 Best Poster Award at the combined meeting of the 68th Denver X-ray Conference (DXC) & 25th International Congress on X-ray Optics and Microanalysis (ICXOM) for 'Full-Field Structured Illumination Super-Resolution X-ray Transmission Microscopy' Günther regularly presents his work at major international conferences including the International Particle Accelerator Conference, High-Brightness Sources and Light-driven Interactions Congress, and specialized X-ray imaging meetings. His research is conducted within the Munich Compact Light Source facility, a collaborative project involving physicists, engineers, and medical researchers working to develop laboratory-scale synchrotron technology for widespread biomedical use.

Dieu Tien Buiمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Machine Learning
• GIS and Geomatics
• +۶ مورد دیگر

Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.

John G. Lynch, Jr.مشاهده پروفایل

استاد

• Consumer Decision-Making
• Consumer Financial Decision-Making
• Research Methodology
• +۲ مورد دیگر

John G. Lynch, Jr. is University of Colorado Distinguished Professor at the Leeds School of Business, University of Colorado-Boulder. He also serves as Inaugural Director of the Initiative for Global Business Impact and is affiliated with the Center for Research on Consumer Financial Decision Making. Previously, from 2022-2024, he served as Executive Director of the Marketing Science Institute, a nonprofit think tank bridging industry and academia. He has held prestigious faculty positions at University of Florida (1979-1996) as Graduate Research Professor and at Duke University's Fuqua School of Business (1996-2009) as Roy J. Bostock Professor of Marketing. Lynch received his BA in economics, MA in psychology, and PhD in psychology from the University of Illinois at Urbana-Champaign. His academic journey spans over four decades with significant contributions to consumer behavior research. He is the founding Director of the Center for Research on Consumer Financial Decision Making and founding co-chair of the Boulder Summer Conference on Consumer Financial Decision Making. He served on the Academic Research Council of the US Consumer Financial Protection Bureau from 2017-2020. Lynch's research focuses on the cognitive psychology of consumer decision-making, particularly consumer financial decision-making since joining CU in 2009. His work spans consumer financial decision making, financial knowledge and well-being, planning for money and time, and research methodology validity. His publications reveal a strong emphasis on financial education effectiveness, couples' financial responsibility division, retirement savings behavior, and methodological rigor in marketing research. The recent publications show increasing focus on financial education, privacy regulation, statistical methodology, and the intersection of financial and physical health. Lynch has received numerous prestigious awards including the 2025 American Marketing Association-Irwin-McGraw Hill Award for Distinguished Marketing Educator of the Year (the highest honor in academic marketing), Paul D. Converse Award for Outstanding Contributions to Marketing Science, and the Society for Consumer Psychology's Distinguished Scientific Achievement Award. He is a Fellow of the American Marketing Association, Association for Consumer Research, and American Psychological Association/Society for Consumer Psychology - one of only six scholars worldwide to achieve this triple distinction. As an educator, Lynch has taught undergraduate Principles of Marketing, Marketing Research, and Senior Seminar in Marketing at CU. He has also taught MBA electives on market intelligence and PhD courses on marketing strategy and experimental design. He has supervised or co-supervised 100 PhD dissertations, with former students now on faculties of leading business schools worldwide. His teaching excellence has been recognized with multiple awards including Leeds MBA Elective Teaching Excellence Awards in 2011 and 2013.

• Rehabilitation Robotics
• Dynamic Systems and Control
• Optimization
• +۶ مورد دیگر

Sunil K. Agrawal is a Professor of Mechanical Engineering and Professor of Rehabilitation and Regenerative Medicine at Columbia University, where he directs a highly interdisciplinary rehabilitation robotics program bridging the School of Engineering and Applied Sciences and the College of Physicians and Surgeons. His research focuses on developing robotic systems to restore and enhance human mobility for individuals with neurological disorders, pediatric conditions, and age-related decline. Education: PhD in Mechanical Engineering, Stanford University, 1990 MS in Mechanical Engineering, Ohio State University, 1986 BS in Mechanical Engineering, Indian Institute of Technology (IIT) Kanpur, 1984 Research Focus: Dr. Agrawal’s work centers on rehabilitation robotics , where he integrates dynamic systems, control theory, and optimization to create robotic devices that assist in gait training, balance improvement, and functional movement restoration. His studies span stroke, Parkinson’s disease, cerebral palsy, vestibular disorders, and spinal cord injury, using devices like the Tethered Pelvic Assist Device (TPAD) and robotic exoskeletons. Scientific Honors: Machine Design Award, ASME (2016) Robotics and Mechanisms Award, ASME (2016) Fellow, American Institute of Medical and Biological Engineering (AIMBE) (2016) Fellow, American Society of Mechanical Engineers (ASME) (2004) Alexander von Humboldt Foundation U.S. Senior Scientist Award (2007) Friedrich Wilhelm Bessel Research Award (2002) Presidential Faculty Fellow Award, The White House (1994) Research Collaborations & Funding: Dr. Agrawal collaborates with faculty across Neurology, Rehabilitation Medicine, Pediatric Orthopedics, Otolaryngology, Geriatrics, and Psychiatry. His work is supported by the National Science Foundation, National Institutes of Health, and the Spinal Cord Injury Research Board. Laboratory & Outreach: He leads an active research group focused on translational robotic systems, with ongoing clinical trials for stroke, Parkinson’s, cerebral palsy, and elderly fall prevention. His lab develops novel robotic braces, exoskeletons, and VR-integrated training platforms.

• Infectious Diseases
• Antimicrobial Stewardship
• Clinical Pharmacology
• +۶ مورد دیگر

Dr. Suman Adhikari is a Conjoint Lecturer at the School of Clinical Medicine , University of New South Wales (UNSW), focusing on Infectious Diseases , Antimicrobial Stewardship , and Clinical Pharmacology . He has contributed to advancements in antibiotic management, sepsis diagnostics, and e-learning tools for healthcare professionals. University: University of New South Wales School: School of Clinical Medicine Email: suman.adhikari@unsw.edu.au Research Interests: Dr. Adhikari’s work spans antimicrobial stewardship in tertiary care, therapeutic drug monitoring of antibiotics like linezolid, and educational technology for vancomycin use. He also explores pharmacokinetics in parasitic infections and contributes to sepsis pathway optimization . Publication Trends: His recent articles (2017–2023) highlight antibiotic safety , digital education , and infection control . Notable studies include multimodal stewardship in ICU, qSOFA criteria validation, and e-learning tool efficacy for vancomycin training. Contact: Phone: 0411817996 | Location: Gray Street, Kogarah 2217 NSW

Sophie Ryanمشاهده پروفایل

پژوهشگر ارشد

• Climate and Health
• Medical Geography
• Mental Health
• +۱۲ مورد دیگر

Sophie Ryan is a Climate and Health Research Fellow at Appalachian State University's Department of Geography and Planning, supported by the National Academy of Sciences. She holds a Ph.D. in Geography (candidate) from the University of North Carolina at Chapel Hill and advanced degrees in Geography from Appalachian State University and the University of Vermont. Education : Ph.D. Candidate in Geography (UNC Chapel Hill), M.A. in Geography (Appalachian State), B.S. in Geography (University of Vermont) Ryan's research integrates medical geography and climate science to explore mental health impacts of extreme weather events. Her work combines spatiotemporal modeling , health data analysis , and disaster response studies , focusing on youth, older adults, and vulnerable populations. Recent projects examine greenspace effects on mental health , compound climate events , and pandemic-related health disparities . Her publications highlight climate-mental health linkages , spatial epidemiology , and social vulnerability in disaster contexts. Articles address topics like heat-stress and mental health , transgender youth post-disaster trauma , and suicide risk disparities during the COVID-19 pandemic. Ryan employs distributed lag models , cluster analysis , and geospatial techniques to assess environmental health impacts. Scientific Awards : NASEM Climate-Health Fellow