جست‌وجوی استاد و پژوهشگر

• Materials Science
• Solid-State Physics
• X-ray Diffraction
• +۳ مورد دیگر

Paul Evans is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on nanoscale materials synthesis, ultrafast dynamics, and advanced X-ray characterization techniques. PhD, Harvard University (2000) MS, Harvard University (1996) BS, Cornell University (1994) Evans investigates solid-phase epitaxy of complex oxides, strain imaging in acoustic devices, and optically driven phase transitions. His work combines experimental and computational approaches, including deep learning for diffraction data analysis. His recent publications highlight breakthroughs in nanoscale crystallization, ultrafast magnetization dynamics, and hybrid magnon-phonon systems. Awards include the Bascom Professorship and Vilas Mid-Career Award. Surface Science and Technology Bascom Professorship (2022) Vilas Associate Award (2019) Polygon Engineering Outstanding Instructor Award (2006) Evans teaches courses in materials structure, advanced X-ray methods, and thesis research. His lab enables scalable synthesis of perovskites and defect-minimized oxide heterostructures.

Neil Garrettمشاهده پروفایل

استادیار

• Cognitive Psychology
• Decision-Making
• Belief Updating
• +۷ مورد دیگر

Neil Garrett is a Research Lecturer (Assistant Professor) in the Psychology Department at the University of East Anglia, where he is also a Visiting Lecturer and member of the Cognition, Action and Perception and Social Cognition Research Groups. He is actively involved in research and accepts PhD students, indicating ongoing academic engagement. Research Lecturer, Psychology Department, University of East Anglia Visiting Lecturer, School of Psychology, UEA Member, Cognition, Action and Perception Research Group Member, Social Cognition Research Group Neil Garrett's research focuses on how beliefs and motivation emerge from learning processes. He employs computational modeling, neuroimaging, behavioral experiments, online games, and economic theory to understand decision-making and its neurobiological basis. His work spans cognitive and affective neuroscience, with applications in clinical disorders such as depression and schizophrenia. His recent publications reveal a strong emphasis on belief updating, optimism bias, decision heuristics, and neural mechanisms of motivation. Articles span journals like Nature Communications , eLife , and Journal of Neuroscience , reflecting interdisciplinary and high-impact research. Topics include biased belief updating, foraging decisions, habenula-insular circuits, and computational psychiatry, indicating a cohesive research program in cognitive-affective neuroscience. Sir Henry Wellcome Research Fellow UCL IMPACT Scholar in Experimental Psychology Published in Nature Neuroscience , Nature Communications , eLife , PNAS Contributor to Aeon Magazine , The Conversation , and NBC Garrett has led research projects funded by the Wellcome Trust and British Academy, focusing on information sharing motivations and neural mechanisms in clinical disorders. He collaborates widely, particularly with Tali Sharot, and has advised or co-authored with multiple early-career researchers. He is active in academic dissemination, having spoken at the Pavlovian Society conference. His lab investigates cognitive and neural mechanisms of belief and decision-making, likely involving computational and behavioral methodologies.

Trilce Estradaمشاهده پروفایل

دانشیار

• Machine Learning
• Big Data
• High Performance Computing
• +۹ مورد دیگر

Trilce Estrada is an Associate Professor in the Department of Computer Science at the University of New Mexico (UNM), School of Engineering. She leads the Data Science Laboratory and is actively involved in research, teaching, and service. Her work focuses on solving data- and compute-intensive problems in science, health, and education, particularly in resource-constrained environments. Her research interests include: Machine Learning and scalable learning techniques Big Data analytics and distributed systems High Performance Computing and cyberinfrastructure In-situ data analytics for scientific workflows Pervasive healthcare using mobile and distributed systems Although no publication list is provided, her current research projects indicate a strong focus on distributed learning, in-situ analysis for molecular dynamics, cyberinfrastructure optimization, and robust science in high-throughput computing. These projects reflect interdisciplinary collaboration and a commitment to scalable, reproducible, and impactful computing solutions. Dr. Estrada has served in numerous leadership roles in major computing conferences, including as Program Co-Chair for IEEE Cluster 2022, Chair of the Mentor-Protégé Program at SC19, and Chair of the IPDPS PhD Forum and Student Program for multiple years. She has also been active in NSF panels and curriculum development, particularly in integrating Big Data into educational frameworks. She is committed to mentoring and improving diversity in computing, serving as faculty advisor for Women in Computing and CSGSA, and participating in outreach to attract underrepresented groups to computer science. She currently oversees a research group but is not accepting new students due to capacity. Independent study opportunities are available under strict eligibility criteria. Her lab, the Data Science Laboratory, supports interdisciplinary research in data-intensive domains. She is also involved in educational initiatives and curriculum development at UNM and nationally.

Shurui Zhouمشاهده پروفایل

استادیار

• Software Engineering
• Open-Source Collaboration
• Fork-Based Development
• +۴ مورد دیگر

Shurui Zhou is an Assistant Professor at the University of Toronto , with primary appointment in the Department of Electrical & Computer Engineering and cross-appointments in the Department of Computer Science and the Department of Mechanical & Industrial Engineering . She is also affiliated with the Schwartz Reisman Institute and a member of the Data Sciences Institute . Zhou directs the FORCOLAB , focusing on enhancing collaboration in distributed software teams, especially in open-source and AI-enabled systems development. Her research integrates software engineering principles with human collaboration insights from organizational behavior, aiming to improve collaboration efficiency through tooling and interdisciplinary methods. Recent publications (2023-2025) explore LLM integration in software development , sustainability in scientific open-source communities , and collaborative challenges in computer-aided design . Education: PhD (2020) from Carnegie Mellon University's Software and Societal Systems Department , MSc from Peking University , BSc from Xi'an Jiaotong University Scientific Awards: Gordon Slemon Teaching of Design Award (2022) Grants: Collaborator on NSERC Alliance-Mitacs Accelerate Grant (2025) for LLM analysis in political data Her lab, FORCOLAB, develops tools like forks-insight.com to analyze GitHub forks and Redundant PR detection bots . Zhou organizes events like the Responsible LLM-Human Collaboration Hackathon (2024) and actively serves on conference committees including ICSE 2025 and ICSME 2024 .

Hanna Meyerمشاهده پروفایل

استاد

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +۷ مورد دیگر

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

Susann Müllerمشاهده پروفایل

دانشیار

• Microbial Ecology
• Flow Cytometry
• Environmental Biotechnology
• +۵ مورد دیگر

Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.

Gaby Umbachمشاهده پروفایل

مدرس پاره‌وقت

• Global governance
• EU governance and Europeanisation
• Evidence-based policy-making
• +۸ مورد دیگر

Gaby Umbach is a Part-time Professor and Founding Director of GlobalStat at the European University Institute's Robert Schuman Centre for Advanced Studies . She serves as a non-resident Visiting Fellow for the European Parliament, Adjunct Professor at the Universities of Cologne and Innsbruck, and Board member of the Institute for European Politics Berlin. Her research focuses on knowledge-evidence-data interactions in governance, analyzing measuring/statistics as governance techniques , data literacy in policy-making, and transformations of politics through multilevel/anticipatory governance and sustainable development . She designed the GlobalStat database, linking it to the European Parliamentary Research Service and OECD. Scientific awards: Stiftung Demokratie Best PhD Thesis Award (2009) Key article trends include: global governance frameworks, data-driven EU policy innovations, open science impacts, and strategic foresight mechanisms. Her 2023-2024 publications emphasize economic indicators in international trade, policy evaluation methodologies, and database design for global governance.

• artificial intelligence
• image analysis
• deep learning
• +۸ مورد دیگر

Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.

Abolfazl Asudehمشاهده پروفایل

دانشیار

• Algorithmic Fairness
• Data-centric Responsible AI
• Ranking algorithms and indices
• +۵ مورد دیگر

Abolfazl Asudeh is an Associate Professor at the University of Illinois Chicago , affiliated with the Department of Computer Science and director of the Innovative Data Exploration Laboratory (InDeX Lab) . His work bridges data management, fairness, and AI. ACM and IEEE Senior Member VLDB Ambassador VLDB Endowment’s NSF Liaison Associate Editor for IEEE TKDE Research Interests focus on Algorithmic Fairness and Data-centric Responsible AI , with applications to ranking systems, LLMs, social networks, and misinformation detection. His work leverages Approximation Algorithms , Computational Geometry , and Randomized Methods to build efficient, fair systems. Scientific Awards : 2021: Google Research Scholar Award 2021: Communications of the ACM Research Highlight 2019: ACM SIGMOD Research Highlight 2020: VLDB Journal Special Issue on Best of VLDB 2017: ACM SIGMOD Most Reproducible Paper Grants include NSF IIS-2348919 (2024-2027) for fairness-aware data structures and NSF IIS-2107290 (2021-2024) for collaborative fairness research. Labs & Collaborations : Leads InDeX Lab with interdisciplinary teams, collaborating with institutions like University of Michigan, University of Texas at Arlington, and industry partners including Google and ACM.

Jolanda Spieringمشاهده پروفایل

پژوهشگر

• Supramolecular Chemistry
• Catalysis
• Biomaterials
• +۴ مورد دیگر

Jolanda Spiering is a Researcher at the Eindhoven University of Technology , affiliated with the Chemical Engineering and Chemistry school and its departments of Macro-Organic Chemistry and Supramolecular Chemistry & Catalysis . Her career spans over 25 years, focusing on organic synthesis and biomaterials development. Education: Organic Synthetic Chemistry, HU University of Applied Sciences (1996) Research Interests include supramolecular chemistry, catalysis, polymer nanoparticles, chirality, and biomaterials. Her work bridges organic synthesis with functional materials for tissue engineering and dynamic systems. Recent Research Trends highlight innovations in bisurea-based supramolecular polymers for tunable biomaterials (2024), advancements in copper-containing polymeric nanoparticles (2024), and studies on reproducibility in supramolecular systems (2022). These reflect her expertise in creating and analyzing complex organic structures for biomedical applications. Scientific Contributions are documented in 26 peer-reviewed publications, including articles in Chemistry: A European Journal and Angewandte Chemie , with over 6,160 citations. Her collaborations with groups led by Bert Meijer and Sijbesma demonstrate interdisciplinary engagement. Teaching involves hands-on organic chemistry courses for undergraduates, aligning with her technical expertise in synthesis and material science.

Gideon Naveمشاهده پروفایل

دانشیار

• Consumer Neuroscience
• Marketing
• Decision-making
• +۷ مورد دیگر

Gideon Nave is the Carlos and Rosa de la Cruz Associate Professor of Marketing at The Wharton School, University of Pennsylvania. He holds a prominent position within the Department of Marketing and contributes significantly to the school's research and teaching missions in consumer behavior, marketing analytics, and decision sciences. His academic journey began with B.Sc and M.Sc degrees in Electrical Engineering from the Technion – Israel Institute of Technology, specializing in Signal Processing. He then pursued and earned his PhD in Computation & Neural Systems from the California Institute of Technology (Caltech), establishing the interdisciplinary foundation for his current research program. Nave's research focuses on how technological developments in measurement instruments have granted unprecedented access to individual-level data. His work spans several interconnected domains: Consumer neuroscience and neuroeconomics Applications of genetic data in marketing AI's impact on creativity and consumer research Ethical implications of new data collection technologies Decision-making processes across various contexts He investigates how digital footprints of online behavior, MRI, genotyping, and hormonal assays can help understand consumer preferences and behavior, while critically evaluating the ethical challenges these technologies present. His publication record demonstrates a clear trajectory toward understanding the intersection of emerging technologies and consumer behavior. Recent work particularly emphasizes AI applications in marketing research, genetic data utilization for personalization, and neuroscientific approaches to understanding decision-making processes. His methodological approach often combines large-scale datasets with rigorous experimental designs, contributing significantly to both theoretical advancement and practical applications in marketing. Nave's contributions to the field have been recognized with several prestigious awards: National Science Foundation Faculty Early Career Development (CAREER) Award (2020-2025) Association For Consumer Research (ACR) Early Career Award (2024-2025) APS Rising Star Award (2020) Poets & Quants Selection as one of the "World's Best 40 B-School Professors under the Age of 40" (2021) As a recipient of the NSF CAREER Award, Nave leads significant research initiatives examining the applications and ethical implications of new measurement technologies in marketing and consumer behavior. His work has attracted considerable media attention, with features in major outlets including The New York Times, The Guardian, CNN, and The Economist, reflecting the broad relevance and impact of his research. While specific lab names aren't prominently featured in his public profile, Nave's research appears to involve extensive interdisciplinary collaborations spanning neuroscience, genetics, computer science, and marketing. His publications frequently include co-authors from diverse disciplinary backgrounds, suggesting a team-based research approach that bridges traditional academic boundaries.

Sandra Martinمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Aging and Cognition
• Neurolinguistics
• +۳ مورد دیگر

Sandra Martin is a Postdoctoral Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, where she works in the Lise Meitner Research Group Cognition and Plasticity. Her research combines neuroimaging and neuromodulation methods with graph theory, large language models, and behavioral modeling to explore cognitive changes across the adult lifespan, with a particular focus on age-related changes in memory processes and their interaction with executive functions. Education: PhD in Cognitive Neuroscience (2018-2023) from the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig M.Sc. in European Master of Clinical Linguistics (2014) from University of Potsdam, Germany & University of Groningen, The Netherlands B.A. in Linguistics (2012) from Humboldt University Berlin, Germany Research Interests: Dr. Martin's research focuses on cognitive aging , neurolinguistics , and network neuroscience . She investigates how domain-general networks contribute to language prediction and semantic cognition across the adult lifespan. Her work often employs non-invasive brain stimulation techniques like transcranial magnetic stimulation (TMS) to understand the causal roles of specific brain regions in cognitive processes. She is particularly interested in how executive resources shape language predictability effects and how these relationships change with age. She's a strong advocate for open and reproducible research practices including preregistration and registered reports. Her recent publications reveal a strong trend toward understanding the neural mechanisms of semantic cognition and language processing across the lifespan. She combines multiple methodologies including fMRI, TMS, graph theory, and computational modeling to investigate how brain networks reorganize with age and how this affects cognitive performance. Her work has important implications for understanding cognitive aging and developing interventions for language disorders like aphasia. Scientific Awards: Doctoral fellowship from the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) Advising and Grants: Dr. Martin has been involved in several research projects, including a DFG-funded project on 'The role of domain-general networks in natural language predictions' together with Prof. Dr. Gesa Hartwigsen (Leipzig University) and Prof. Dr. Jonas Obleser (University of Lübeck). While there's no explicit mention of students she's formally advising, her role as a postdoc suggests she likely mentors junior researchers. She actively promotes open and reproducible research practices and co-organized a symposium on Good Scientific Practice titled 'Doing Good – Scientific Practice under Review'. Labs and Teams: Dr. Martin is part of the Lise Meitner Research Group Cognition and Plasticity at the Max Planck Institute for Human Cognitive and Brain Sciences. She collaborates with researchers from Leipzig University and University of Lübeck. Her work often involves interdisciplinary teams combining expertise in cognitive neuroscience, linguistics, and clinical neurology, particularly in studying aphasia and language recovery after stroke. Her research group maintains open science practices with code and data available on GitLab and OSF.

David R Cooperمشاهده پروفایل

استادیار

• X-ray crystallography
• Data management
• Structural biology
• +۳ مورد دیگر

David R Cooper serves as an Assistant Professor of Research in the Department of Molecular Physiology and Biological Physics at the University of Virginia School of Medicine. His work focuses on developing data management systems for structural biology research, particularly in the field of X-ray crystallography. Dr. Cooper earned his BS in Biochemistry from Old Dominion University followed by a PhD in Biochemistry and Molecular Biology from Purdue University. His educational background provides the foundation for his current research in structural biology data systems. His primary research interest centers on data management and analysis for scientific endeavors, with specialization in X-ray crystallography. Dr. Cooper is currently developing a next-generation Laboratory Information Management System (LIMS) designed to track experimental procedures and parameters throughout the entire structural biology pipeline - from initial cloning to final structure deposition in the Protein Data Bank. His work addresses critical challenges in scientific reproducibility by ensuring protocols, data, and necessary metadata are properly documented and accessible. Analysis of Dr. Cooper's publication history reveals consistent contributions to structural biology data management, with particular emphasis on database development, validation tools, and visualization systems. His research spans bioinformatics, structural biology, and data science, with applications in macromolecular characterization and scientific reproducibility frameworks. Dr. Cooper maintains active research collaborations within the structural biology community, particularly with Dr. Wladek Minor's research group as indicated by his website (https://minorlab.org/person/dcoop/). His work supports the broader structural biology research enterprise through development of essential data infrastructure tools. His laboratory focuses on creating flexible systems capable of managing experimental samples and workflows for all stages of structural biology research. The LIMS development project represents a significant contribution to standardizing and improving data management practices in macromolecular structural analysis.

Yann Renisioمشاهده پروفایل

پژوهشگر ارشد

• Social stratification
• Higher education
• Kinship networks
• +۵ مورد دیگر

Yann Renisio is a CNRS Research Fellow at the Center for Research on Social Inequalities (CRIS) at Sciences Po, a position he has held since October 2021. He is also an affiliated researcher at the Department of Sociology of Education and Culture at Uppsala University. His work focuses on understanding social structures and inequalities through rigorous sociological research. Dr. Renisio earned his PhD in sociology from EHESS in 2017. Prior to his current position, he held postdoctoral positions at Sciences Po, Uppsala University, and the Collège de France, demonstrating his international research experience and academic mobility across prestigious institutions. Yann Renisio's research spans multiple interconnected areas within sociology. His primary interests include social stratification, higher education systems, kinship networks, and the sociology of science. He investigates how social inequalities are reproduced and transformed through educational pathways and professional specialization, with particular attention to methodological innovations in social science research. His current projects examine higher education trajectories, professional specialization in medicine, practice-report gaps in survey data, and kinship networks. His methodological approach often integrates digital traces with traditional research methods to provide more comprehensive insights into social phenomena. Dr. Renisio's publication record reveals a strong focus on methodological innovation and the sociology of knowledge. His recent work explores the integration of digital trace data with survey methodologies, topic modeling of scientific disciplines, and the social organization of academic knowledge. Across his publications, he consistently examines how social structures shape individual opportunities and choices, with particular attention to educational and professional trajectories. His research bridges theoretical sociology with empirical analysis of contemporary social issues, particularly in understanding how digital practices intersect with traditional social stratification patterns. Dr. Renisio is actively involved in significant research projects including ANR RECORDS (2019-2023), which investigates music streaming practices and their relationship to social stratification, and ANR MEDSPE (2021), which examines the choice of medical specialties and locations among French doctors. These projects demonstrate his commitment to understanding how social structures influence individual choices in contemporary society through both traditional and digital methodologies. As a CNRS Research Fellow, Dr. Renisio contributes to the vibrant research community at CRIS, collaborating with scholars across disciplines and institutions. His work exemplifies the interdisciplinary approach needed to address complex social inequalities in modern societies, particularly through innovative methodological approaches that bridge digital and traditional research paradigms.

Avery Bermanمشاهده پروفایل

استادیار

• Medical Physics
• Functional Neuroimaging
• Biomedical Engineering
• +۵ مورد دیگر

Avery Berman is an Assistant Professor in the Department of Physics at Carleton University and a Scientist at the University of Ottawa Institute of Mental Health Research (IMHR) at The Royal. His work focuses on advancing functional MRI (fMRI) techniques for high-resolution imaging of brain activity and physiology, with applications in neuroscience and mental health disorders. PhD in Biomedical Engineering and MSc in Medical Radiation Physics from McGill University Postdoctoral research at Harvard Medical School and the Martinos Center for Biomedical Imaging Research Interests include: High-resolution fMRI at 7 Tesla Biophysical modeling of vascular networks Quantitative biomarkers for oxygen metabolism PET-MRI hybrid imaging systems Neurovascular coupling in mental illness Scientific Awards include: NSERC Canada Graduate Scholarship (Master's) CIHR Canada Graduate Scholarship (Doctoral) CIHR Postdoctoral Fellowship (top 10/600 applicants) NSERC Postdoctoral Fellowship (top Physics section recipient) Research Funding from NSERC, CFI, and institutional support from Carleton University and IMHR. His lab develops the open-source BOLDsωimsuite software for fMRI signal modeling and collaborates with Canada-wide vascular training programs.