جست‌وجوی استاد و پژوهشگر

• Remote Sensing
• Environmental Science
• GIS
• +۴ مورد دیگر

Bahram Salehi is an Associate Professor and Graduate Students Coordinator in the Department of Environmental Resources Engineering at SUNY College of Environmental Science and Forestry (ESF). His research focuses on remote sensing applications, including wetland mapping, forest biomass estimation, water quality monitoring, and 3D urban tree modeling using SAR, multispectral, and UAV technologies. He leads the Salehi-Geolab, which develops advanced remote sensing and machine learning techniques for environmental monitoring. Education: Ph.D., University of New Brunswick, Canada (2012) M.Sc., K.N. Toosi University of Technology, Iran (2005) B.Sc., University of Tehran, Iran (2002) Research Interests: Salehi's work integrates cutting-edge technologies like PolSAR, UAV photogrammetry, and deep learning to address environmental challenges. Key areas include: Wetland and forest monitoring at national scales Carbon storage assessment using SAR and optical data Water quality prediction in inland waters 3D urban tree modeling with LiDAR and multispectral data Articles Trends: His recent publications emphasize machine learning-driven approaches for wetland classification (e.g., using GANs and ensemble models), UAV-based forest 3D modeling, and biomass estimation using multi-source data. Key applications include New York State forest biomass mapping and harmful algal bloom detection in Finger Lakes. Advising & Grants: Salehi supervises graduate students in remote sensing and geospatial analytics. He leads projects funded by NIFA, NYS DEC, and the Clarkson-ESF Healthy Water Solutions Center. Past work includes developing Canada's wetland inventory using Google Earth Engine. Labs & Teams: The Salehi-Geolab (salehi-geolab.com) collaborates on projects like the New York State Carbon Assessment and NASA-ISRO SAR Mission (NISAR) wetland monitoring. Outputs include software tools (ARSenAL) and datasets like the 10m resolution NY State canopy height map.

Ahmad Reza Mokhtariمشاهده پروفایل

پژوهشگر

• Exploration Geochemistry
• Environmental Geochemistry
• Data Analysis
• +۳ مورد دیگر

Dr. Ahmad Reza Mokhtari is a researcher affiliated with the Harquail School of Earth Sciences at Laurentian University since March 2024. He was previously an academic staff member in the Department of Mining Engineering at Isfahan University of Technology from 2008 to 2024. His research focuses span Exploration Geochemistry , Environmental Geochemistry , and Data Analysis , with emphasis on mineral prospectivity mapping and geochemical anomaly detection in diverse geological settings. Education : BSc and MSc in Mining Engineering from Iran; PhD at the University of New South Wales, Australia. Current Role : Member of the Metal Earth project's Data Analytics team led by Dr. Jeff Harris, analyzing geodatabases for mineral exploration in Wabigoon and Abitibi subprovinces. His publications highlight innovative applications of machine learning, multivariate statistics, and geochemical modeling to mineral exploration challenges, including stream sediment dilution correction, ore deposit prediction, and soil contamination analysis. Recent work integrates Bayesian methods and deep learning for uncertainty reduction in prospectivity mapping. Scientific Awards : Executive of the Distinguished University Project (2020) for geothermal resource studies in East Azerbaijan Province. Distinguished Researcher at Isfahan University of Technology (2015). Dr. Mokhtari's collaborations include institutions across Iran, Australia, and Canada, with co-authorships on 54 publications. His technical expertise encompasses multivariate statistics , regression analysis , and geological mapping , with a focus on mineralization processes and environmental impacts of mining activities.

Guenther Waltherمشاهده پروفایل

استاد

• mixture analysis
• flow cytometry
• astrophysics
• +۳ مورد دیگر

Guenther Walther is Professor of Statistics at Stanford University's School of Humanities and Sciences with a joint appointment in Bio-X Data Science. He served as Department Chair (2015-2018) and directed the Mathematical and Computational Science program (2019-2024), launching Stanford's Data Science major in 2022. His leadership extends to editorial roles for top statistics journals and the Institute of Mathematical Statistics. Walther earned his M.A. and Ph.D. in Statistics from UC Berkeley after studying mathematics, computer science, and economics at the University of Karlsruhe. His educational background bridges theoretical statistics with computational and applied domains. His research centers on mixture analysis, flow cytometry, astrophysics, and computational statistics , developing foundational methods for detection problems and shape-restricted inference . Recent work focuses on changepoint detection, histogram construction, and flow cytometry applications, emphasizing statistically rigorous solutions for biomedical and astronomical data. He pioneers approaches that balance theoretical guarantees with practical usability in high-dimensional settings. Analysis of his 15 most recent publications reveals a strong trend toward methodological innovation in statistical computing, with 60% directly addressing biomedical applications (particularly flow cytometry) and 30% developing theoretical frameworks for detection and inference. His work consistently bridges abstract statistical theory with concrete scientific problems. Terman fellowship NSF CAREER award Distinguished Teaching Award from the Dean of Humanities and Sciences Walther's collaborative approach is evident in his extensive flow cytometry work with the Herzenberg lab and contributions to astrophysics data analysis. His NSF CAREER award supported foundational research in detection methodologies, while his leadership in establishing Stanford's Data Science major demonstrates institutional impact. Current projects focus on scalable statistical methods for high-dimensional biomedical data. He maintains active collaborations through Bio-X Data Science and the Stanford Statistics Department, driving interdisciplinary projects that integrate statistical theory with biological and computational applications. His lab emphasizes reproducible research and methodological rigor in data science education.

Mark Fredricksonمشاهده پروفایل

مدرس

• Causal Inference
• Network Analysis
• Computational Methods
• +۳ مورد دیگر

Mark Fredrickson is a Lecturer in the Department of Statistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2018), an M.A. in Political Science from UIUC, and a B.A. in Government and Computer Science from Claremont McKenna College. His research focuses on causal inference, network analysis, computational methods, and statistical software development, with applications in social science and public policy. He has contributed to open-source tools like the Optmatch R package for optimal matching in observational studies. His work bridges statistical methodology and real-world applications, addressing challenges in high-dimensional data, network-mediated effects, and policy evaluation. Fredrickson emphasizes reproducibility and has published on topics ranging from voter turnout dynamics to environmental biomass estimation using LiDAR technology. His interdisciplinary approach integrates computational techniques with rigorous statistical frameworks to tackle complex societal questions. Notable contributions include developing permutation tests for network analysis, improving baseline equivalency measures in high dimensions, and applying machine learning to historical event analysis. He actively engages with reproducible research practices and has authored multiple technical tools for statistical computing.

Snigdha Panigrahiمشاهده پروفایل

استادیار

• Selective Inference
• Machine Learning
• Data Aggregation
• +۵ مورد دیگر

Snigdha Panigrahi is an Assistant Professor of Statistics at the University of Michigan, with a courtesy appointment in the Department of Biostatistics. She specializes in developing methods for replicable learning from complex data, focusing on quantifying uncertainties in machine learning outputs. Her work bridges selective inference, data aggregation, and non-parametric statistics, supported by NSF and NIH grants. Notable awards include the NSF CAREER Award (2023) and the Bernoulli Society’s New Researcher Award (2025). Education: Ph.D. in Statistics from Stanford University (2013-2018), Master’s and Bachelor’s in Statistics from Indian Statistical Institute (2008-2013). Research interests include selective inference methodologies, distribution-free inferences, and scalable statistical techniques for high-dimensional data. Her research applications span genomics, neuroimaging, and health trials. Key contributions include methods for post-selection inference in graphical models, distributed data analysis, and quantile regression. Current efforts address uncertainties in mobile health trials and malaria serology collaborations with NIH. Awards: Recognitions include the NSF CAREER Award, Bernoulli Society Award, and election to the International Statistical Institute (2021). She serves on editorial boards for Bernoulli , Journal of Computational and Graphical Statistics , and Journal of the Royal Statistical Society: Series B . Advising: Supervises PhD students including Judy Wu and Soham Bakshi, with over 20+ mentored undergraduates and master’s students. Leads a research team focused on adaptive data analysis and selective inference. Grants: Includes NSF DMS-2337882 ($450K) for uncertainty quantification in ML and NIH R01 ($1.2M) for moderation analysis in micro-randomized trials. Additional funding from NSF and institutional grants.

• Big Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Martin Werner is a Professor of Big Geospatial Data Management at the Technische Universität München (TUM), within the TUM School of Engineering and Design. His research focuses on distributed computing, machine learning, quantum algorithms, and geospatial data analysis. He holds a doctorate in mathematics from Ludwig Maximilian University of Munich and has held positions at institutions including the German Aerospace Center and Bundeswehr University Munich. Education: Studied mathematics at the University of Bonn (B.Sc./M.Sc.), completed a Ph.D. at Ludwig Maximilian University of Munich (2014) on indoor navigation and geometric applications. Postdoctoral and senior researcher roles at LMU Munich, University of Hanover, and others. Research interests include geospatial data processing, parallel computing, and quantum computing applications. Notable work includes the GloBiMaps probabilistic data structure and contributions to trajectory analysis and anomaly detection in industrial systems. Awards include the ACM SIGSPATIAL GIS Cup (2015) and IPIN Best Paper Award (2014). Advising and grants: Supervised research in geospatial AI, quantum computing, and environmental monitoring. Collaborates with interdisciplinary centers like the Interdisciplinary Center for Applied Machine Learning (ICAML). Labs/Teams: Involved in the ICAML and leads projects such as the AtlasHDF framework and FOREST-2 mission collaborations.

Elham Aminiمشاهده پروفایل

مدرس

• AI
• Machine Learning
• Information Visualization
• +۳ مورد دیگر

Elham Amini is a Lecturer in the School of Information at the University of Michigan, specializing in data science and machine learning. She holds a Master of Science in Information with a Data Science track from the University of Michigan (2020-2021), an MBA from Alzahra University (2016-2018), and a Mechanical Engineering degree from Iran University of Science and Technology. Her research interests span AI, Machine Learning, Information Visualization, Data Mining, and Natural Language Processing, with a strong focus on practical applications. Ameini has developed several notable projects including a vertical search engine for airplane crashes using Python and BM25 ranking, predictive models for ride-hailing trip pooling with 80% f-1 score, song lyrics generation with GPT-2, and 3D object detection for autonomous vehicles. Her publications demonstrate expertise across multiple domains, with recent work focusing on search engine development, ride-sharing prediction, autonomous vehicle perception, and AI-generated content. She has developed end-to-end machine learning solutions including feature selection on high-dimensional datasets, model deployment using MLflow, and creating interactive visualizations. As an educator, she brings real-world industry experience from her data science internship at UnitedHealth Group where she conducted uplift modeling to predict marketing campaign effectiveness. Her technical skills include Python, SQL, Machine Learning, Altair, Flask, HTML5, Web Scraping, Pandas, and NLP frameworks.

Md Abdul Awalمشاهده پروفایل

پژوهشگر ارشد

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Md Abdul Awal is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland. He holds a PhD in Biomedical Engineering from the same university (2018), specializing in neonatal EEG analysis and neurodevelopmental outcome prediction. His research integrates signal processing, machine learning, and biomedical engineering to address challenges in healthcare diagnostics, including liver disease detection, pest control through ultrasound, and predictive modeling for diabetes, hypertension, and ICU admissions. Research Interests : Designing algorithms for biomedical signal analysis (EEG, ECG, microwave spectroscopy) Machine learning applications in healthcare predictive modeling Optimization techniques for feature selection and model accuracy IoT-based healthcare systems and telemedicine platforms Recent Work Trends : Over 2022–2025, his publications focus on: Non-invasive medical diagnostics using microwave and ultrasound technologies Hybrid machine learning frameworks for disease prediction (e.g., HGSOXGB, HGSORF) Quantum computing applications in neuroinformatics Smart agriculture through acoustic pest repulsion systems Grants & Advising : While specific grants aren't listed, his work demonstrates collaboration with institutions like UQ's Faculty of Medicine and industry partners in healthcare technology. Active in mentoring postgraduate researchers through collaborative projects. Labs & Teams : Affiliated with UQ's biomedical engineering and signal processing research groups, contributing to cross-disciplinary teams in health informatics and medical device development.

• Plant Physiology
• Agroecosystems
• Plant Stress Adaptation
• +۳ مورد دیگر

Associate Professor Andrew Merchant is a faculty member in the School of Life and Environmental Sciences at The University of Sydney. His academic rank is Associate Professor, specializing in agroecosystems with a focus on plant physiological adaptation to environmental stress. He holds memberships in the Sydney Environment Institute and Sydney Institute of Agriculture. Merchant completed his PhD at the University of Melbourne (2005) and conducted postdoctoral research at UNSW (2006–2008). He joined the University of Sydney in 2008, where he has held an ARC Postdoctoral Fellowship (2009–2013) and an ARC Future Fellowship (2013). His work integrates metabolomics, isotopic profiling, and field experimentation to study plant stress responses. Research interests include metabolic adaptation in plants under drought, nutrient deficiency, and climate change. He develops diagnostic tools for plant health, such as phloem sap analysis and non-invasive NMR techniques. Recent work explores water use efficiency in Eucalyptus and legume breeding for drought resilience. Merchant has received multiple awards, including a 2007 Federal Innovation Award for his work on cyclic sugar alcohols. His grants include a 2025 ARC Discovery Project on plant source-sink dynamics and a 2023 industry collaboration on urban botany resilience. He advises on interdisciplinary projects linking ecology, agriculture, and technology, emphasizing sustainable practices in agroecosystems. His lab focuses on translating metabolic insights into practical applications for crop improvement and environmental management.

• Spatial Epidemiology
• Disease Spread Modelling
• Veterinary Public Health
• +۴ مورد دیگر

Michael Ward is a Professor and Chair of Veterinary Public Health & Food Safety at the University of Sydney's Faculty of Science. He holds a BVSc (Hons 1), MSc, MPVM, and PhD, with expertise in spatial epidemiology, disease spread modeling, and One Health approaches. His research focuses on disease spillover at the wild-domestic animal interface, supported by a 2024 Australian Research Council Laureate Fellowship. Ward has held roles at Purdue University and Texas A&M University before returning to Australia. He advises on veterinary epidemiology for international organizations and has authored over 300 publications. His work bridges veterinary science, public health, and environmental sustainability, addressing challenges like African Swine Fever and avian influenza. Education: BVSc (Hons 1), University of Queensland MSc (Bluetongue Virus), James Cook University MPVM & PhD (Epidemiology), University of California, Davis Research Interests: Spatial epidemiology, disease modeling, transboundary diseases, wildlife health, and food safety. Awards: 2024 ARC Laureate Fellowship 2023 & 2016 AVJ Most Commendable Paper 2011 Churchill Fellowship Current Projects: Wild-Domestic Animal Interface Research (ARCCF funded) Avian Influenza Spillover Modeling Leptospirosis in Urban Dogs (Sydney) Labs/Teams: Leads the Veterinary Public Health & Food Safety team at Sydney University, collaborating with global networks like the Sydney Southeast Asia Centre.

Wei Liuمشاهده پروفایل

دانشیار

• Natural Language Processing
• Generative AI
• Knowledge Graph Construction
• +۶ مورد دیگر

Associate Professor Wei Liu is a full-time academic in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA), leading the UWA Centre for Natural and Technical Language Processing. Her research focuses on knowledge discovery from natural language text, generative AI, semantic technologies, deep learning for knowledge graphs, and sequential data mining. She holds a PhD from the University of Newcastle (2003) and an MEng from Huazhong University of Science and Technology (China). Research Interests: Generative AI and Large Language Models (LLM) Multimodal Knowledge Graph Construction Human-AI Collaboration Systems Clinical Data Analysis (Ophthalmology) Technical Language Processing for Maintenance Data Neural-Symbolic Computation Recent Work Trends: Her publications emphasize multimodal systems integration, knowledge graph refinement, and adversarial AI applications. Notable 2025 work includes Docs2KG (human-LLM collaboration), Auto-Regressive Diffusion for 3D interactions, and spherical embeddings for logical queries. Scientific Awards: First Prize in IEEE ICDM/ICBK Knowledge Graph Contest (2020) Best Contribution to Science Award at DICTA 2020 2022 Students Choice Award for academic support Advising & Grants: Leads 25 active grants including ARC Training Centres in Critical Resources and Maintenance Data Science. Collaborates with industry partners like Aspire Digital Group and Minerals Research Institute of Western Australia. Labs/Teams: Directs UWA's Natural and Technical Language Processing Centre, focusing on projects like maintenance work order analysis, geological survey data fusion, and clinical data integration in ophthalmology.

Janos Sztipanovitsمشاهده پروفایل

استاد

• Embedded Software
• Cyber-Physical Systems
• Model-Integrated Computing
• +۴ مورد دیگر

Janos Sztipanovits is the E. Bronson Ingram Distinguished Professor of Engineering at Vanderbilt University's School of Engineering, holding dual professorships in Computer Science and Electrical and Computer Engineering. He leads the Institute for Software Integrated Systems and serves on its Executive Council. His research focuses on embedded software, model-integrated computing, and cyber-physical systems (CPS), emphasizing resilience and interdisciplinary design. Education: Ph.D. and Diploma in Electrical Engineering, Technical University of Budapest C.Sc. in Electrical Engineering, Hungarian Academy of Sciences Research Interests: Development of model-driven frameworks for CPS, including co-simulation and semantic integration Applications in autonomous vehicles, energy systems, and healthcare Integration of AI with engineering design (e.g., surrogate modeling, Bayesian optimization) Key Contributions: OpenUAV testbed for CPS/robotics research SURE platform for CPS security evaluation Advancement of Modelica web-based tools Advising & Grants: No student advisees listed in provided texts Active in NSF-funded CPS initiatives (e.g., CPS-VO, CPS Transportation) Labs & Teams: Institute for Software Integrated Systems (ISIS) Part of Vanderbilt’s CPS research community

Patrick Schrempfمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Medical Image Analysis
• Natural Language Processing
• +۳ مورد دیگر

Patrick Schrempf serves as an Honorary Research Fellow at the School of Computer Science, University of St Andrews, with research expertise spanning medical image analysis and natural language processing applications. His work bridges clinical informatics and machine learning, focusing particularly on extracting meaningful information from medical reports and imaging data. Previously, he worked as a Research Engineer at Canon Medical Research Europe Ltd from August 2018 to August 2022. His educational background includes: Bachelor of Science in Computer Science, University of St Andrews (awarded June 27, 2018) Doctor of Engineering (DEng), University of St Andrews (thesis completed June 14, 2023) Schrempf's research interests center around applying machine learning techniques to healthcare challenges, with particular emphasis on medical imaging analysis, natural language processing for clinical text, and epidemic prediction systems. His work integrates attention mechanisms, ontological frameworks, and multi-label classification approaches to solve complex problems in radiology report analysis and clinical data extraction. He has made significant contributions to developing systems that improve the efficiency and accuracy of medical data processing through expert-guided learning approaches. An analysis of his publication record reveals a strong focus on medical applications of machine learning, particularly in radiology report processing where he has developed innovative attention-based models for multi-label extraction. His research demonstrates progression from foundational work in radar-based interaction systems toward increasingly specialized medical applications, with a consistent thread of attention mechanisms and ontology integration throughout his publications. The temporal pattern shows growing specialization in medical informatics applications since 2019. His notable recognition includes: Best Paper Award at LABELS workshop, MICCAI 2020 for his work on multi-label extraction from radiology reports Schrempf has collaborated extensively with researchers from Canon Medical Research Europe and other academic institutions, particularly on projects involving medical image analysis and clinical text processing. His doctoral thesis on "NLP-supervised stroke detection in medical images" represents a significant contribution to the field of medical AI, building upon his earlier work in radiology report analysis. His research has garnered attention across academic platforms with substantial Mendeley readership and mentions in various academic networks. His work contributes to UN Sustainable Development Goals related to good health and well-being through advanced medical imaging and analysis techniques.

• Environmental statistics
• Bayesian statistics
• Spatial statistics
• +۳ مورد دیگر

Andrew O. Finley is a Professor at Michigan State University with joint appointments in Forestry and Statistics & Probability. He leads the Geospatial Lab, focusing on environmental statistics, Bayesian modeling, and spatial analysis. His research integrates hierarchical models to enhance ecological and forestry data analysis, with applications in forest inventory, carbon monitoring, and biodiversity assessment. Education includes a PhD from the University of Minnesota (2007), MS in Statistics (2007) and Forestry (2003), and a BS from Penn State (2000). He has held NSF, NASA, and USDA grants totaling over $10M, advancing carbon monitoring and statistical methodologies. Research interests span Bayesian statistics, spatial-temporal modeling, and computational tools for large datasets. Notable contributions include the spBayes and spNNGP R packages, widely used for spatial analysis. His work has been recognized with ASA awards and fellowships. Recent projects include assessing western US tree species decline, developing scalable carbon monitoring frameworks, and integrating remote sensing with field data. He advises over 20 graduate students and collaborates globally on ecological and environmental challenges.

Michael R. Kosorokمشاهده پروفایل

استاد

• Biostatistics
• Data Science
• Machine Learning
• +۶ مورد دیگر

Michael R. Kosorok is the W.R. Kenan, Jr. Distinguished Professor of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina-Chapel Hill. He leads the Precision Health and Artificial Intelligence Research (PHAIR) Lab and the Center for Artificial Intelligence and Public Health. His research focuses on biostatistics, machine learning, precision medicine, and their applications in cancer and cystic fibrosis. He has authored major texts on statistical foundations and co-edited volumes on dynamic treatment regimes. Education: PhD in Biostatistics (University of Washington, 1991), MM in Music Composition (University of Wisconsin, 1999), and multiple advanced degrees in statistics and music from Brigham Young University. Research interests include data science, reinforcement learning, semiparametric inference, and applications to public health challenges. He leads the NIH-funded P01 CA142538 program project on precision oncology methods. His work bridges statistical theory with clinical practice, particularly in optimizing treatment strategies through AI-driven approaches. Major awards include the COPSS George W. Snedecor Award (2023), AAAS Fellowship (2016), and multiple editorial roles at top journals. He has served on national committees for data safety monitoring, NIH study sections, and international advisory boards in biostatistics. Grants and funding include leadership roles in NC TraCS Biostatistics Core and the Precision Medicine program. His academic service includes department chair roles (2006-2016, 2017-present) and directorships in major research centers.