جست‌وجوی استاد و پژوهشگر

Evan Gawlikمشاهده پروفایل

دانشیار

• Numerical Analysis
• Finite Element Methods
• Computational Fluid Dynamics
• +۳ مورد دیگر

Evan Gawlik is an Associate Professor in the Department of Mathematics and Computer Science at Santa Clara University (2024–present), previously serving as Associate Professor (2023–2024) and Assistant Professor (2018–2023) at the University of Hawaii. He holds a Ph.D. in Computational and Mathematical Engineering from Stanford University (2015) and a B.S. in Applied and Computational Mathematics from the California Institute of Technology (2010). Education : Ph.D., Stanford University, 2010–2015 B.S., California Institute of Technology, 2006–2010 Research Interests : Focused on numerical analysis, finite element methods, and structure-preserving algorithms for partial differential equations. His work emphasizes geometric numerical integration, computational fluid dynamics, and applications in magnetohydrodynamics (MHD) and general relativity. Key areas include finite element discretization of differential forms, error analysis on manifolds, and thermodynamically consistent numerical schemes. Grants & Funding : NSF Grant DMS-1703719 (2017–2020) ANR Grant GEOMFLUID (ANR-14-CE23-0002-01) Labs & Collaborations : Active in computational mathematics research groups, including contributions to open-source finite element software and collaborations on geometric numerical methods. His GitHub repositories (e.g., egawlik ) showcase code implementations of his research, such as variable density Euler equations solvers.

Kalimuthu Krishnamoorthyمشاهده پروفایل

استاد

• Statistical Methodology
• Occupational Exposure Analysis
• Missing Data
• +۶ مورد دیگر

Professor Kalimuthu Krishnamoorthy holds the Philip and Jean Piccione Endowed Chair in Statistics at the University of Louisiana at Lafayette. His research focuses on statistical methodologies for occupational exposure analysis, missing data, and tolerance regions, with contributions to censored data analysis and calibration techniques. He has advised over 30 Ph.D. students and led NIOSH-funded projects on exposure assessment. His work includes developing statistical software tools like StatCalc and over 200 peer-reviewed articles. Education: Ph.D. (Statistics, 1985) Indian Institute of Technology-Kanpur; M.Sc. & B.Sc. (Statistics) Madras University. Grants: Multiple NIOSH grants (R01-OH series) totaling $2.6M, focusing on exposure data analysis methodologies. Labs/Teams: Leads statistical research groups at the University of Louisiana, collaborating on occupational health and environmental statistics.

• Biostatistics
• Epidemiology
• Cardiovascular Research
• +۷ مورد دیگر

Michael Griswold, PhD, is a Professor in the Department of Biostatistics at the University of Mississippi Medical Center (UMMC) School of Medicine. He serves as Director of the UMMC Center of Biostatistics and Bioinformatics and holds adjunct faculty positions at Johns Hopkins University and the University of Alabama Birmingham School of Public Health. His roles include leadership in large-scale epidemiological studies like the Jackson Heart Study (JHS) and the Atherosclerosis Risk in Communities (ARIC) Study. Education: PhD in Biostatistics from Johns Hopkins University (2005); BS in Statistics, Mathematics, Computer Science, and Physics from St. Cloud University (1999). His research focuses on biostatistical methods applied to cardiovascular disease, aging, and health disparities, with emphasis on biomarker development, longitudinal data analysis, and clinical trial design. Key areas of research include lipoprotein subclasses, cognitive decline mechanisms, and the impact of socioeconomic factors on health outcomes. He has authored over 150 peer-reviewed articles and serves as Associate Editor for Annals of Internal Medicine . Grants include NIH-funded projects on health disparities, neurocognitive studies, and sepsis outcomes. Awards include the Helen Abbey Teaching Award and Phi Beta Kappa honor society membership. He actively mentors students through programs like the Medical Student Research Program (MSRP) and advises on research design across multiple disciplines. Labs/Teams: Leads the Center of Biostatistics and Bioinformatics, collaborates with the Jackson Heart Study Coordinating Center, and participates in the NIH-funded Center for Integrative Approaches to Health Disparities (CIAHD).

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۴ مورد دیگر

Michael Steinbach is a Researcher in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, working in the research group of Prof. Vipin Kumar. He holds B.S. in Mathematics, M.S. in Statistics, and M.S./Ph.D. in Computer Science from the University of Minnesota. His research focuses on data mining, machine learning, biomedical informatics, and statistics, with applications in environmental science, healthcare, and engineering. He co-authored the widely used textbook Introduction to Data Mining , translated internationally. Previously, he held software engineering roles at Silicon Biology, Racotek, and NCR. Research interests emphasize integrating scientific knowledge with machine learning for real-world systems, such as climate modeling, flood forecasting, and healthcare analytics. His work spans causal inference, physics-guided AI, and interpretable predictive models. Collaborations include environmental monitoring, wildfire management, and clinical decision support systems in healthcare. Publications highlight innovations in knowledge-guided learning, multi-scale modeling, and causal discovery. His contributions bridge theory and practice, advancing AI applications in complex systems. The lab engages in interdisciplinary projects, combining computational methods with domain expertise to address societal challenges.

René Westerholtمشاهده پروفایل

استادیار

• Spatial Analysis
• GIScience
• Urban Analytics
• +۷ مورد دیگر

René Westerholt is an Assistant Professor (Juniorprofessor) in Spatial Modelling at the Department of Spatial Planning at Technische Universität Dortmund, Germany. He also leads the Spatial Modelling Lab (RAM) and is affiliated with the GIScience research group at Heidelberg University. His academic background includes a PhD in Geography from Heidelberg University and an M.Sc. in Geoinformatics from Osnabrück University. His research focuses on spatial analysis methodologies, particularly integrating social dimensions into quantitative analysis. Key areas include geosocial media data analysis, spatial autocorrelation, and urban analytics. He has interdisciplinary experience in software development for nature conservation and agricultural projects, and is active in professional societies like the Royal Geographical Society and DGPF. Teaching responsibilities include leading the M.Sc. Urban Analytics and Visualisation programme at the University of Warwick and courses such as IM913 Spatial Methods and IM927 Digital Cities. His work bridges theoretical GIScience with practical applications in urban planning and environmental studies.

Bradley W. Carrollمشاهده پروفایل

استاد

• Astrophysics
• Stellar Pulsations
• Neutron Stars
• +۳ مورد دیگر

Bradley W. Carroll is a Professor in the Department of Physics at Weber State University. His primary affiliations include academic leadership in astrophysics education and research, with significant contributions to theoretical astrophysics and physics pedagogy. He has co-authored seminal textbooks in astrophysics and developed educational software used in physics instruction globally. Carroll's research spans stellar pulsations, neutron star physics, accretion disk dynamics, and computational astrophysics. He maintains active interests in physics education innovation, particularly through multimedia tools and simulations. His work consistently bridges theoretical astrophysics with practical teaching methodologies, enhancing astronomy education at multiple levels. His publication portfolio demonstrates a strong focus on stellar phenomena, pedagogical advancements, and computational applications. Recent works emphasize integrating technology into physics education while maintaining foundational research in variable stars and compact objects. The interdisciplinary nature of his output connects astrophysics, computational modeling, and educational theory. Carroll actively contributes to academic outreach through public lectures on topics ranging from Archimedes' achievements to modern astrophysics. He maintains collaborations with educational software developers and participates in physics education research conferences.

Sanda Koboevicمشاهده پروفایل

دانشیار

• Structural Engineering
• Earthquake Engineering
• Steel Structures
• +۳ مورد دیگر

Associate Professor Sanda Koboevic is affiliated with Polytechnique Montréal in the Department of Civil, Geological and Mining Engineering . With degrees including B.Sc. (Université de Sarajevo) , M.Eng. (McGill) , and Ph.D. (McGill) , she specializes in structural engineering, earthquake engineering, and soil-structure interaction. Education: B.Sc. (Université de Sarajevo), M.Eng. (McGill), Ph.D. (McGill) Her research focuses on seismic design of steel and concrete structures , foundation engineering , and structural resilience . She investigates how footing size , soil-structure interaction , and variability in design parameters affect seismic performance. Recent publications include studies on functional recovery time of braced frame buildings (2025), predictive equations for footing behavior using machine learning (2025), and Canadian seismic design procedures for steel braced frames (2024). These works align with her expertise in steel and concrete structures , foundation analysis , and seismic rehabilitation . Scientific Awards: 2022: H.A. Krentz Award from Canadian Institute of Steel Construction She supervises graduate students and has advised numerous Ph.D. and Master's theses on topics like seismic deformations of concrete shear walls and retrofit of steel frames. Her work impacts building codes , engineering practices , and disaster preparedness in structural engineering.

Edward Ionidesمشاهده پروفایل

استاد

• Time Series Analysis
• Epidemiology
• Ecology
• +۵ مورد دیگر

Edward Ionides is a Professor of Statistics and Associate Chair for Undergraduate Studies at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts. He holds a Ph.D. from the University of California, Berkeley (2001) and a B.A. in Mathematics from the University of Cambridge (1994). His research focuses on statistical theory and computational methods for time series analysis, particularly in ecology, epidemiology, and public health. He develops methodologies for inference in partially observed stochastic dynamic systems, with applications to infectious disease modeling, climate change, and economic health interactions. Ionides has made significant contributions to statistical software, notably the R package pomp for analyzing partially observed Markov processes. He has been recognized with prestigious awards, including Fellow of the Institute of Mathematical Statistics (2023) and Fellow of the AAAS (2019). His teaching spans courses like Time Series Analysis (STATS 531), Probability (MATH/STATS 425), and Applied Statistical Methods (STATS 401). He has advised numerous Ph.D., master's, and undergraduate students, contributing to over 50 peer-reviewed publications and multiple funded grants totaling over $15 million. Key grants include studies on urban vector-borne disease transmission and spatiotemporal statistical inference, funded by the NSF and NIH. His work integrates interdisciplinary collaborations, addressing challenges in public health policy, climate science, and mathematical biology. He also chairs departmental committees, including Undergraduate Programs and the Michigan Student Symposium for Interdisciplinary Statistical Sciences.

Edouard Bugnionمشاهده پروفایل

استاد

• Computer Systems
• Data Center Systems
• Data Center Infrastructure and Networking
• +۷ مورد دیگر

Prof. Edouard Bugnion is a Full Professor at the Data Center Systems Laboratory within the School of Computer Science and Communications at École Polytechnique Fédérale de Lausanne (EPFL). Since joining EPFL in 2012, he has led research in datacenter systems and virtualization, with prior experience as VMware's first CTO and Cisco's VP/CTO. Research Interests: Data Center Systems Network and Data Plane Efficiency Operating System Design Virtualization Technologies Trusted Execution Environments Low-Latency Computing His recent publications focus on datacenter efficiency for OLDI applications, system security through hardware-based trust, and network-compute co-design . These works span topics from microsecond-scale latency optimization to privacy-preserving proximity tracing systems. Scientific Recognition: Elected ACM Fellow (2017) ACM Software Systems Award (2009) Teaching: Currently teaching CS-212: Systems Programming Project , with past courses including CS-522: Principles of Computer Systems and CS-410: Technology Ventures .

• Transport Planning
• Traffic Modelling
• Urban Mobility
• +۴ مورد دیگر

Professor Michiel Bliemer is a Professor of Transport Planning and Modelling at the University of Sydney Business School within the Institute of Transport and Logistics Studies. He joined in 2012 as Chair in Transport Network Modelling, previously holding an Associate Professor position at Delft University of Technology and working as a consultant at Goudappel Coffeng in the Netherlands. His research focuses on transport planning, traffic modelling, road pricing, and urban mobility systems. Education: MSc from the University of Groningen (RUG) PhD from Delft University of Technology His work explores road user charging schemes , macroscopic traffic flow models , and stated choice experiments . Key contributions include the Handbook on Transport and Urban Planning in the Developed World (2016) and influential research on network design, traffic management, and mobility consumption theories. He has secured grants such as the 2022 'Fair Road Pricing Scheme' project and the 2020 Australian Transport Research Cloud initiative. Media & Outreach: Professor Bliemer frequently advises on transport infrastructure issues, including cycling infrastructure, public transport investments, and Sydney's high-density urban planning challenges. His expert commentary has appeared in SBS News, ABC News, and radio interviews addressing traffic congestion and Rozelle Interchange design flaws. Grants & Collaboration: 2022: Fair road pricing scheme research 2020: Australian Transport Research Cloud International collaborations in transport policy and simulation studies His research integrates cutting-edge methodologies like discrete choice experiments , agent-based modelling , and macroscopic fundamental diagrams , addressing both theoretical and applied challenges in modern transport systems.

Fei Zouمشاهده پروفایل

استاد

• Biostatistics
• Genetic Epidemiology
• Statistical Genetics
• +۴ مورد دیگر

Dr. Fei Zou is a Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill, affiliated with the Gillings School of Global Public Health. She leads research in statistical methodologies for genetic data analysis, specializing in quantitative genetics and genomic data interpretation. Her work focuses on advancing machine learning applications for big EHR data and high-dimensional genetic datasets. Education: PhD in Statistics from the University of Wisconsin-Madison (2001), MS in Mathematical Statistics from Wuhan University (1993), and BS in Mathematics from Wuhan University (1990). Research interests include genetic mapping (linkage/association studies), copy number variation analysis, and next-generation sequencing methodologies. She directs the Biostatistics/Bioinformatics Core of the UNC Neuroscience Center and is a core member of the Carolina Center for Genome Sciences. Her contributions to statistical genetics include developing algorithms for collaborative cross mouse models and genomic data analysis tools like ShrunkenPCA. Awards include the David Byar Young Investigator Award (2001) and the Gordon Research Fellowship (2001). Active in academic service, she serves on NIH review panels and curriculum committees. Teaching focuses on statistical genetics and bioinformatics courses such as BIOS 783 and BCB723. Software contributions include HapSample (SNP genotype simulation) and Semiparametric QTL mapping tools. Her research bridges statistical theory with practical applications in translational and clinical research through collaborations in the UNC Center for AIDS Research and NC TraCS Institute.

Derek Karssenbergمشاهده پروفایل

استاد

• Earth Surface Systems
• Spatio-Temporal Modelling
• Hydrology
• +۷ مورد دیگر

Prof. Derek Karssenberg is a Professor of Computational Geography at Utrecht University's Department of Physical Geography within the Faculty of Geosciences. His research focuses on spatio-temporal modeling of Earth surface systems, including hydrology, geomorphology, ecology, and environmental health. He develops software frameworks like PCRaster, LUE, and Campo to enhance model scalability and usability. Current roles include coordinating the Computational Geography group and leading the AI & Sustainability Lab. His work integrates geocomputation with applied data science, addressing sustainability challenges and health-environment linkages. Research interests emphasize model development for complex systems, such as air pollution exposure assessment, land-use change impacts, and global hydrological modeling. Collaborations span environmental epidemiology, remote sensing, and machine learning. He co-initiated the Applied Data Science Master's program and innovates teaching methods using blended learning. Publications (2024–2020) highlight advancements in air pollution modeling, streamflow prediction via machine learning, and scalable hydrological frameworks. Activities include editorial roles for Environmental Modelling & Software and steering committees for Utrecht's Applied Data Science initiatives.

Adarsh Pyarelalمشاهده پروفایل

استادیار

• Artificial social intelligence
• Automated causal model assembly
• Machine learning
• +۳ مورد دیگر

Adarsh Pyarelal is an Assistant Professor at the School of Information , University of Arizona. His research focuses on socially-aware AI teammates, automated assembly of causal models from text/software, and probabilistic modeling of human coordination. He holds a Ph.D. and has contributed to projects like MultiCAT and Eidos/INDRA/Delphi frameworks. His work spans multimodal dialogue systems, variable extraction from scientific literature, and applications in particle physics collaborations. Notable contributions include the Future Circular Collider feasibility studies and vector quantization techniques in reinforcement learning. Despite numerous grant proposal rejections (noted in his Shadow CV), his research emphasizes resilience in academic pursuits. Current focus areas include AI team coordination and improving model interpretability through causal frameworks. He does not currently accept new PhD students. Technical interests include cross-platform GUI libraries (wxWidgets), MacPorts Python management, and reproducible research workflows using Pandoc/Zotero.

Taeyoon Leeمشاهده پروفایل

استاد آموزشی

• Precision Forestry
• Geographic Information Systems (GIS)
• Remote Sensing
• +۳ مورد دیگر

Taeyoon Lee is an Instructional Assistant Professor at the College of Agriculture & Life Sciences, Texas A&M University, where he focuses on integrating technology into forestry education and research. He is affiliated with Texas A&M AgriLife Extension Service and Texas A&M AgriLife Research, contributing to advancements in precision forestry, geographic information systems (GIS), and forest management strategies. Lee holds a B.S. and M.S. in Environmental Horticulture from the University of Seoul, South Korea, and a Ph.D. in Forestry from the University of Georgia. His educational background underscores his expertise in ecological and technological approaches to natural resource management. His research interests span precision forestry, GIS and remote sensing applications, forest management under climate change, and tree ecophysiology. Key areas of exploration include: Development and application of geospatial technologies for forest inventory and monitoring Impact of environmental variables (e.g., water, CO2 levels) on tree physiology Integration of online tools for forest management education and stakeholder engagement Lee’s academic contributions include advising and mentoring students in forestry and natural resource management. He has also been involved in developing educational tools and systems to enhance forest management practices, such as the online forest management evaluation system for small private landowners. While specific grant details are not provided, his research aligns with broader initiatives in sustainable forestry and climate adaptation. As part of Texas A&M AgriLife Research and Extension Service, Lee collaborates with interdisciplinary teams to advance applied research in forest ecosystems and land use. His work often involves partnerships with stakeholders to implement technology-driven solutions for ecological and management challenges.

• Trustworthy Machine Learning
• Automated Machine Learning
• Data-Driven Software Engineering
• +۳ مورد دیگر

Dr. Huong Ha is a Senior Lecturer in Computer Science at RMIT University's School of Computing Technologies, located at the City Campus in Australia. Her research focuses on trustworthy machine learning, automated machine learning, and data-driven software engineering. She holds an ORCID ID (0000-0003-2463-7770) and is actively involved in supervising Masters and PhD students. Prior to her academic role, she worked as a Data Scientist at Freelancer International Pty Limited in Sydney from 2017 to 2018. Her research interests emphasize practical applications of machine learning in software systems optimization, including root cause analysis for microservices, Bayesian optimization techniques, and performance prediction for configurable software. She has published extensively in top conferences such as ICSE, AAAI, and NeurIPS, addressing challenges in high-dimensional optimization, system monitoring, and algorithmic efficiency. Current supervision projects include topics like Explainable AI in Human-Agent Planning, Fraud Detection on Blockchain networks, and High-dimensional Bayesian Optimization via Evolutionary Computation. Her work bridges theoretical advancements with real-world software engineering problems, contributing to robust and scalable solutions in AI-driven systems.