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Claudia A. RosuView profile

Adjunct Assistant Professor

• Health Professions Education
• Survey Methodology
• Qualitative Research
• +4 more

Claudia A. Rosu is an Adjunct Assistant Professor in the Department of Health Professions Education at the MGH Institute of Health Professions. Her expertise lies in survey development, qualitative and mixed-method research methodologies, and the sociology of health professions education. She focuses on understanding healthcare providers' professional socialization, educational interventions, and sociological aspects of medical training. Dr. Rosu holds an MD from Grigore T. Popa University of Medicine and Pharmacy in Iasi, Romania, and a PhD in Health Policy from the University of Maryland, Baltimore County. Education: MD, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania PhD in Health Policy, University of Maryland Baltimore County, Baltimore, MD Research Interests: Dr. Rosu's research emphasizes the application of qualitative and mixed-method approaches to assess educational interventions and the sociological dynamics in health professions education. Her work explores topics such as the hidden curriculum in patient care, telesupervision efficacy in speech-language pathology, and the impact of race on healthcare provider interactions. She also develops surveys to evaluate healthcare provider evaluations and employs advanced statistical methods for validating measurement tools in medical education. Advising and Grants: No formal advisees or grant details are listed in the provided information. Labs/Teams: No specific laboratories or research teams are mentioned in her profile.

Robert RootView profile

Research Professor

• Geochemistry
• Contaminant Transport
• Environmental Remediation
• +4 more

Robert Root is an Associate Research Professor at the University of Arizona, affiliated with the Chorover Lab. He holds a PhD in Environmental Systems from UC Merced. His research focuses on geochemistry, metal speciation analysis (using techniques like EXAFS and XANES), and environmental remediation strategies for contaminants. Key interests include weathering processes, toxic metal mobility in mine tailings, and medical mineralogy applications. His work bridges laboratory spectroscopy, field studies, and astrochemistry (e.g., analyzing Ryugu asteroid samples). Root’s recent studies address climate impacts on metal translocation in mining regions, PFAS detection in groundwater, and arsenic bioaccessibility in agricultural soils. He develops low-cost screening methods for environmental pollutants and investigates novel remediation technologies involving zero-valent iron systems. His research also explores microbial interactions with metals in contaminated soils and the spatial distribution of Valley Fever pathogens in Arizona soils. Collaborative projects include analyzing asteroid samples for organic sulfur preservation and modeling planetary fluid chemistry. His work frequently integrates synchrotron-based X-ray spectroscopy for nanoscale chemical speciation studies.

Mark KennedyView profile

Research Fellow

• Astrophysics
• White Dwarfs
• Neutron Stars
• +4 more

Dr. Mark Kennedy is a Research Fellow in the School of Physics at University College Cork (UCC), specializing in astrophysics with a focus on magnetic white dwarfs, neutron stars, and black holes. His research integrates observational astronomy, computational modeling, and machine learning to study accretion processes in compact binaries and gravitational-wave astronomy. Kennedy holds a PhD from UCC and the University of Notre Dame (2017), supported by a Naughton Fellowship. He completed a Newton International Fellowship (2017–2019) at the University of Manchester and currently leads the 'Invisible Monsters' project funded by a Government of Ireland Fellowship (2021–present). His academic career includes ERC-funded work on neutron star mass measurements and contributions to the Gravitational-wave Optical Transient Observer (GOTO) project. Kennedy serves as Communications Officer and Postdoctoral Researcher Representative for the Astronomical Society of Ireland. He teaches PY3109 (Introduction to Observational Astrophysics) and PY2106 (Introduction to Astrophysics and Special Relativity) at UCC. Key research areas include transitional millisecond pulsars, black widow/red back systems, and machine learning applications for detecting hidden black holes using all-sky surveys. Awards include the Naughton, Newton, and Government of Ireland Postdoctoral Fellowships. His work has been published in high-impact journals and presented at international conferences, with a focus on multi-messenger astronomy and compact object dynamics.

Armeen TaebView profile

Assistant Professor

• Statistical Modeling
• Latent Variable Analysis
• Water Resource Management
• +3 more

Armeen Taeb serves as an Assistant Professor in the Department of Statistics at the University of Washington, specializing in statistical methodologies for environmental systems. His work bridges theoretical statistics with practical applications in water resource sustainability. Education: PhD in Electrical Engineering, California Institute of Technology (Caltech) BS in Applied Mathematics and Electrical Engineering, University of Colorado at Boulder Research Interests: Dr. Taeb develops advanced statistical frameworks for latent-variable modeling in observational data, with primary applications in hydrological systems. His research characterizes unmodeled phenomena affecting California water storage levels, integrating causal inference techniques to predict system behavior under environmental changes. This work directly supports evidence-based water policy development through rigorous quantification of uncertainty and hidden variables in complex environmental datasets. Scientific Awards: Resnick Graduate Research Fellow Advising and Grants: No current information available regarding graduate student advising or externally funded research grants. Labs and Teams: No specific laboratory affiliations or research teams are documented in the provided materials.

• Bayesian statistics
• Spatial statistics
• Shape analysis
• +3 more

Qiwei Li is an Assistant Professor of Statistics in the Department of Mathematical Sciences at The University of Texas at Dallas. He holds a Ph.D. in Statistics from Rice University (2016), an M.A. in Statistics from Rice University (2015), an M.Phil. in Information Engineering from The Chinese University of Hong Kong (2010), a B.Sc. in Economics from Peking University (2009), and a B.Eng. in Electronic Engineering from Tsinghua University (2008). His research focuses on developing AI-powered Bayesian statistical methodologies and machine learning approaches to analyze high-dimensional, spatial, and shape data. Key areas include Bayesian clustering, spatial transcriptomics, microbiome community detection, and integrative omics analysis. He is actively involved in advancing methodologies for spatial molecular profiling and disease-relevant gene discovery. Recent publications highlight contributions to spatial transcriptomics integration, microbiome analysis, and health outcome studies. His work bridges statistical theory with biomedical applications, addressing challenges in genomics, imaging, and clinical data interpretation. Dr. Li is seeking highly motivated Ph.D. and undergraduate students to join his research group, offering financial support. He emphasizes interdisciplinary collaboration and innovation in statistical methodologies for solving complex biomedical problems.

Ryan BlythingView profile

Lecturer

• Cognitive Psychology
• Child Language Acquisition
• Psycholinguistics
• +5 more

Dr. Ryan Blything is a Teaching Associate in Psychology at the School of Psychology, College of Health and Life Sciences, Aston University (Birmingham, UK), where he has been employed since 2021. He previously held positions as a Postdoctoral Research Associate at the University of Bristol (2017–2021) and Lecturer in Psycholinguistics at the University of Essex (2016–2017). He holds a PhD in Psychology from the University of Manchester (awarded 2017) and a PG Cert Fellowship in Higher Education (Distinction, 2022). His research specializes in Cognitive Psychology , with emphases on Child Language Acquisition , Psycholinguistics , and Visual Object Recognition . A unifying theme explores human generalization capabilities (e.g., analogy-based vs. rule-based systems), examining how children generalize verb structures and how visual systems recognize objects under variable conditions (e.g., size, orientation, illumination). Methodologically, he employs priming, eyetracking, ERPs, grammaticality judgments, and computational modeling. His publications (2019–2024) cluster into two themes: vision science (comparative studies of human vision and neural networks, focusing on translation tolerance and object recognition robustness) and language processing (reading models, nonword pronunciation, and child language acquisition). Recent work extends into sports psychology (tennis timeout strategies). Awards: PG Cert Fellowship in Higher Education (Distinction, 2022)

Stephanie LanzaView profile

Professor

• Substance use behavior
• Finite mixture modeling
• Latent class analysis
• +4 more

Stephanie Lanza is a Professor of Biobehavioral Health at The Pennsylvania State University's College of Health and Human Development. She also holds a courtesy appointment as Professor of Human Development and Family Studies. Her research focuses on developing and applying innovative quantitative methods to advance behavioral health research, with particular emphasis on substance use behavior in daily life among adolescents and young adults. Dr. Lanza earned her PhD in Human Development and Family Studies from The Pennsylvania State University in 2003. Her educational background has provided a strong foundation for her interdisciplinary research bridging behavioral health, statistics, and developmental science. Dr. Lanza's research interests center on advancing methodological approaches to understand complex behavioral phenomena. She specializes in finite mixture modeling , particularly innovations in latent class analysis that enable researchers to discover and understand hidden subgroups in their data. Additionally, she focuses on time-varying effect models to study substance use behavior developmentally and in real-time. Her work leverages intensive longitudinal methods and new technologies to understand substance use behaviors in daily life. Her methodological innovations have significantly impacted prevention science and behavioral health research. Analysis of Dr. Lanza's recent publications reveals a consistent focus on applying advanced statistical methods to understand substance use patterns, particularly among young adults. Her work increasingly incorporates daily diary and ecological momentary assessment methods to capture within-person variability in substance use behaviors and related factors. Recent research examines co-occurring substance use patterns, the role of nondrug rewards in recovery, mental health interactions with substance use, and the impact of the COVID-19 pandemic on college student health behaviors. Her methodological expertise in latent class analysis and time-varying effect modeling continues to drive innovative approaches to understanding complex behavioral phenomena. Dr. Lanza has secured significant research funding, including: NIDA grant "Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns" (R01-DA057588) Co-director of the Prevention and Methodology Training Program (T32-DA017629), which has trained over 100 pre- and post-doctoral research fellows Dr. Lanza has made substantial contributions to mentoring the next generation of researchers through her leadership of the Prevention and Methodology Training Program. Her methodological expertise has been instrumental in advancing substance use research through innovative quantitative approaches. Her work bridges the gap between advanced statistical methodology and practical applications in behavioral health research. Dr. Lanza is affiliated with the Edna Bennett Pierce Prevention Research Center at Penn State, where she contributes to interdisciplinary research focused on preventing health and behavior problems through innovative methodological approaches. Her work exemplifies the center's mission of advancing prevention science through rigorous methodology and interdisciplinary collaboration.

Martin J. SliwinskiView profile

Professor

• Cognitive Aging
• Geroscience
• Stress and Cognition
• +5 more

Martin J. Sliwinski is a Professor of Human Development and Family Studies at Pennsylvania State University and serves as Director of both the Center for Healthy Aging and the Social Science Research Institute’s Geroscience and Dementia Prevention Consortium. His research focuses on dynamic phenotyping of daily experiences and cognitive function across multiple time scales. Department: Human Development and Family Studies Key Research Areas: Cognitive Aging, Ecological Momentary Assessment (EMA), Digital Biomarkers, Social Interactions Sliwinski embeds ambulatory cognitive assessments into EMA and daily diary designs to capture real-time data on stress, social activity, and cognitive performance. His work bridges psychosocial factors with biological markers like inflammation and epigenetic aging. Email: mjs56@psu.edu | Office: 402 BBH Building | Phone: 814-863-9980

• Computer Science
• Neuroscience
• Medical Imaging
• +3 more

Fengyu Cong is a Visiting Professor at the Faculty of Information Technology of the University of Jyväskylä (Finland). His research focuses on interdisciplinary computational neuroscience, biomedical signal processing, and machine learning applications in healthcare. Key contributions include EEG/fMRI analysis for mental health disorders such as depression and autism spectrum disorder, as well as federated learning methods for privacy-sensitive medical data. He has presented at major conferences including the 6th Conference on Mismatch Negativity (2012) and ESCAN 2014 , showcasing work on preattentive facial expression processing in neurological populations. His affiliations include the Engineering and Secure Communications Engineering and Signal Processing groups within his faculty. Research interests span neural signal analysis, multimodal imaging techniques, and AI-driven diagnostics. Notable methodologies include tensor decomposition for fMRI preprocessing, HMM-based sleep stage classification, and federated learning frameworks for distributed healthcare applications. His work bridges computer science and clinical neurology, with publications in top-tier journals addressing topics like vascular cognitive impairment, neural oscillations in autism, and artifact removal in biomedical signals.

• Computational Linguistics
• Natural Language Processing
• Text Technology
• +3 more

Yu Wang is a Lecturer at Bielefeld University's Faculty of Linguistics and Literary Studies, affiliated with the Computational Linguistics department. He is associated with the research project Monitoring the understanding of explanations under Prof. Dr. Hendrik Buschmeier, focusing on digital linguistics and text technology. While his institutional affiliation highlights computational linguistics, his Google Scholar publications reveal interdisciplinary work in optimization algorithms, graph neural networks, and machine learning applications. Research Interests: Yu Wang's work bridges computational linguistics with advanced algorithmic techniques. His publications explore Multi-objective optimization Graph neural network architectures Swarm intelligence applications Evolutionary computation methods Sparse learning systems Biologically-inspired algorithms Labs & Projects: Yu Wang contributes to the Digital Linguistics Group at Bielefeld University, specifically working on explainability monitoring in explanatory processes. His technical publications suggest collaborations with researchers in optimization and machine learning domains.

Ido GoldingView profile

Professor

• Biophysics
• Genome Architecture
• Gene Regulation
• +5 more

Ido Golding is a Professor of Biological Physics at the University of Illinois Urbana-Champaign, affiliated with the Department of Physics and the Carl R. Woese Institute for Genomic Biology. He explores cellular individuality and deterministic drivers of heterogeneity in gene expression. His lab uses cutting-edge microscopy and theoretical models to study systems such as E. coli and bacteriophage lambda, extending to Drosophila embryos and mammalian stem cells. Research focuses on transcriptional regulation, phage infection dynamics, and genome architecture. His work bridges experiment and theory, developing novel imaging techniques for molecular counting and employing coarse-grained models. Key themes include hidden variables in cellular decision-making, transcription-replication interactions, and stochastic processes in gene expression. Collaborations span microbiology, biophysics, and developmental biology. Publications highlight contributions to phage infection kinetics, bacterial stress responses, and the biophysics of gene expression. Golding’s lab is located in the Loomis Laboratory of Physics, part of the NSF Science and Technology Center for Quantitative Cell Biology.

Jacie McHaneyView profile

Academic

• Auditory Neuroscience
• Speech Perception
• Pupillometry
• +7 more

Jacie McHaney is a Research Assistant Professor in the Roxelyn and Richard Pepper Department of Communication Sciences and Disorders at Northwestern University . Her work bridges auditory neuroscience, speech perception, and computational cognitive neuroscience, with a focus on neural mechanisms underlying speech processing challenges. She earned her PhD in Communication Science and Disorders from the University of Pittsburgh and holds a BS in Psychology with a Biology Minor from The University of Texas at Austin. Education: PhD, Communication Science and Disorders, University of Pittsburgh BS, Psychology (Biology Minor), University of Texas at Austin AS, General Studies in Science, Austin Community College Dr. McHaney’s research explores how aging and subclinical hearing loss affect speech perception, using EEG, pupillometry, and computational models. Her work aims to develop neurobiologically informed rehabilitative strategies for individuals with hidden hearing loss. Her publications emphasize auditory neuroscience, neural encoding of speech, and age-related changes in auditory processing. Key themes include cortical tracking of speech, listening effort, and sensory decision-making. Scientific awards include the NRSA Individual Predoctoral Fellowship and NRSA Institutional Training Program in Auditory Neuroscience. She has secured over $1.1 million in grants, including NIH-funded projects on speech perception in aging and autism spectrum disorder. Dr. McHaney collaborates with researchers like Dr. Bharath Chandrasekaran and Dr. Ananthanarayan Parthasarathy, advancing interdisciplinary studies in auditory neuroscience and clinical translational sciences.

Raj MehrotraView profile

Research Fellow

• Hydrology
• Climate Change Impact Assessment
• Statistical Modeling
• +3 more

Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.

Karoline WiesnerView profile

Professor

• Complexity Sciences
• Information Theory
• Complex Systems
• +7 more

Karoline Wiesner is Professor of Complexity Sciences at the University of Potsdam and an External Faculty member. She previously served as Assistant Professor at the School of Mathematics, University of Bristol, and held postdoctoral positions at the Santa Fe Institute and the University of California, Davis. She holds a PhD in Physics from Uppsala University (2004). Her research lies at the core of complexity sciences and information theory , with applications across diverse fields such as climate systems , democratic stability , and ecological networks . She investigates the formation, maintenance, and stability of complex systems through mathematical and philosophical lenses. Key areas include the foundations of complexity, network robustness, and the role of information in dynamical systems. Her recent publications (2020–2024) demonstrate a strong trend in applying network science and information-theoretic methods to interdisciplinary problems, particularly in social and ecological systems. She frequently collaborates across disciplines, contributing to collective works on democracy and complex systems. Her co-authored book What is a Complex System? has become a foundational reference in the field. While no specific scientific awards are listed in the text, her work has received notable press coverage from outlets like PhysicsWorld and Motherboard (VICE) , highlighting its societal relevance. Karoline actively advises interdisciplinary research and contributes to major workshops, such as those at the Complexity Science Hub. She has no listed PhD students in the provided text. She leads and participates in collaborative projects focused on understanding civil conflict, democratization, and systemic risk through statistical physics and complexity theory. She is involved with research groups and initiatives centered on complexity, including the Foundations and Collective Minds themes, and contributes to the Data Analytics cluster. Her work bridges physics, philosophy, political science, and ecology, forming a transdisciplinary approach to understanding systemic stability and change.

Chris FietkiewiczView profile

Associate Professor

• Computational Neuroscience
• High Performance Computing
• Neural Simulation
• +7 more

Chris Fietkiewicz is an Associate Professor in the Department of Mathematics & Computer Science at Hobart and William Smith Colleges. He joined the faculty in 2019 and is based in Lansing Hall, with research interests in computational neuroscience and high-performance computing. Research Interests: His work focuses on Computational Neuroscience , particularly the development of neural simulators for educational and clinical applications. He leads the Applied Neural Control Toolkit (ANC Toolkit) , which enables simulation of neural and axonal responses for neuroprosthetic development. His research also spans High Performance Computing , including software optimization, parallel computing, and cluster management tools. Collaborations: He collaborates with Dr. Thomas Mortimer (Case Western Reserve University) on neural stimulation and with Dr. Chen Liu (Tianjin University) on Parkinson’s disease modeling and deep brain stimulation. His publications reflect a strong interdisciplinary focus on biomedical signal processing, control systems, and neuromorphic computing. Publication Trends: His recent work (2016–2022) centers on closed-loop control of pathological neural oscillations, computational models of Parkinson’s disease, and real-time neural simulation. These studies appear in top journals like IEEE Transactions and Biomedical Signal Processing and Control , emphasizing algorithmic innovation and clinical relevance. Scientific Contributions: Co-developer of the ANC Toolkit for neuroscience education. Researcher in neural control systems and HPC optimization. Active contributor to computational models of movement disorders. Advising and Grants: While no formal students are listed, his collaborative projects suggest mentorship of undergraduate and graduate researchers. Grant details are not provided, but his research cluster and web tools indicate funded infrastructure development. Labs and Teams: He is building a research computing cluster and developing web-based batch programming tools, indicating a focus on scalable, accessible computational neuroscience platforms.