جست‌وجوی استاد و پژوهشگر

Gloria Milena Monsalve Bravoمشاهده پروفایل

پژوهشگر ارشد

• Multiscale Simulation
• Molecular Simulation
• Gas Permeation
• +۷ مورد دیگر

Dr. Gloria Milena Monsalve Bravo is an Advanced Queensland Industry Research Fellow and lecturer at The University of Queensland's School of Chemical Engineering, where she develops novel multiscale simulation techniques combining molecular simulations with macroscopic physics-based modeling to solve complex energy and environmental problems. Her interdisciplinary work bridges applied mathematics and engineering to improve understanding of phenomena in complex systems across chemical, biomedical, and ecological applications. Her research focuses on: Multiscale simulation techniques for complex systems Molecular simulations coupled with macroscopic modeling Gas permeation and separation in mixed-matrix membranes Uncertainty and sensitivity analysis in mathematical models Applied mathematics for engineering problems Dr. Monsalve Bravo's publication record demonstrates a strong trajectory in membrane technology and computational modeling. Her recent work has advanced understanding of gas transport in novel membrane materials, particularly mixed-matrix membranes, with applications in carbon capture and hydrogen storage. She has made significant contributions to theoretical frameworks for modeling permeation in finite-sized composite systems and developed Bayesian approaches for analyzing parameter uncertainty in sorption predictions. Her research bridges fundamental science with practical applications in energy and environmental engineering. Her scientific contributions have been recognized through research funding including: ARC Research Hub for Value-Added Processing of Underutilised Carbon Wastes (2024-2029) Tailor-made composite membranes for greenhouse gas capture (2023-2026) through Advance Queensland Industry Research Fellowships Dr. Monsalve Bravo actively mentors PhD students on cutting-edge projects related to membrane technology, catalyst development, and waste conversion. She collaborates extensively across disciplines, as evidenced by her diverse publication record spanning chemical engineering, materials science, and environmental applications.

• Soft tissue mechanics
• Articular cartilage mechanics and lubrication
• Cartilage tissue engineering
• +۷ مورد دیگر

Gerard A. Ateshian is the Andrew Walz Professor of Mechanical Engineering and Professor of Biomedical Engineering at Columbia University, where he has been a faculty member since 1991. He also served as Chair of the Department of Mechanical Engineering from 2011 to 2014 and is the founding director of the Musculoskeletal Biomechanics Laboratory (MBL), established in 1996. Education: BS (1986), MS (1987), MPhil (1990), and PhD (1991) in Mechanical Engineering from Columbia University. Research Interests: Professor Ateshian's research focuses on the mechanics of soft biological tissues , particularly articular cartilage . His work integrates theoretical, experimental, and computational approaches to understand cartilage lubrication, tissue engineering, and growth and remodeling in living tissues. He has extended continuum mechanics frameworks to model complex biological phenomena such as mass transport, osmotic effects, and reactive mechanics in tissue mixtures. His recent efforts include developing open-source computational tools (FEBio) and translating research into clinical treatments for osteoarthritis . Scientific Awards: H.R. Lissner Medal, ASME (2017) OARSI Basic Science Award (2013) Columbia Engineering Alumni Association Distinguished Faculty Teaching Award (2012) Great Teacher Award, Society of Columbia Graduates (2002) YC Fung Young Investigator Award, ASME (1997) Fellow: ASME, BMES, AIMBE Advising and Mentorship: Professor Ateshian has advised numerous PhD students, including recent graduates Vince Sise and Katherine Spack , who completed their dissertations on cartilage fatigue and osteochondral allografts, respectively. His lab actively mentors students from both Mechanical and Biomedical Engineering programs. Laboratory and Collaborations: He directs the Musculoskeletal Biomechanics Laboratory (MBL) , which collaborates closely with the Cellular Engineering Laboratory of Prof. Clark Hung and with Dr. Jeffrey Weiss at the University of Utah on computational modeling (FEBio). He also collaborates with Columbia University Medical Center faculty on clinical translation of cartilage research.

Amadeus Gebauerمشاهده پروفایل

پژوهشگر

• Cardiac mechanics
• Cardiac growth and remodeling
• Computational mechanics
• +۳ مورد دیگر

Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.

Bryon Aragamمشاهده پروفایل

دانشیار

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۶ مورد دیگر

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

• Statistical Genomics and Bioinformatics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +۴ مورد دیگر

Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.

• Transportation Engineering
• Pavement Mechanics
• Sustainable Infrastructure
• +۷ مورد دیگر

Imad L. Al-Qadi is the Grainger Distinguished Chair in Engineering and Director of the Illinois Center for Transportation (ICT) at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. in Civil Engineering from Penn State and has held faculty positions at Virginia Tech and Penn State. His research focuses on sustainable transportation infrastructure, pavement mechanics, and advanced materials. Al-Qadi leads initiatives like the Illinois Autonomous and Connected Track (I-ACT), a high-speed test facility for autonomous vehicles and energy harvesting systems. Key roles include: Director of ICT, advancing multimodal transportation infrastructure Pioneer of the Smart Road and full-scale pavement testing Founder of the Academy of Pavement Science and Engineering Research interests span: Highway/airfield sustainability Tire-pavement interaction Energy harvesting Electric vehicles' infrastructure impact Leadership roles include past presidency of ASCE's Transportation and Development Institute and editorship of the International Journal of Pavement Engineering. He has authored over 1,000 publications, including 390 refereed papers, and secured over 180 research grants from federal agencies and industry. Awards include NSF Young Investigator Award (1994), TRB Crum Award (2023), and 2024 Executive Leadership Fellow. His work is internationally recognized, with honorary professorships at institutions in China, Sweden, and the UK.

Halina Frydmanمشاهده پروفایل

استاد

• Survival Analysis
• Markov Processes
• Stochastic Modeling
• +۷ مورد دیگر

Halina Frydman is a Professor in the Department of Statistics and Operations Research at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 1978. Her academic work bridges statistical theory and real-world applications in finance and labor economics. Institution: New York University School: Leonard N. Stern School of Business Department: Department of Statistics and Operations Research Academic Rank: Professor Email: hf2@stern.nyu.edu Education: Ph.D. in Mathematical Statistics, Columbia University, 1978 M.A. in Mathematical Statistics, Columbia University, 1974 B.S. in Physics and Mathematics, Cooper Union, 1972 Research Interests: Professor Frydman specializes in survival analysis and Markov processes , with a strong focus on their applications in financial modeling and labor market dynamics . Her work explores mixture models of Markov chains to capture heterogeneity in longitudinal data, particularly in the context of corporate credit rating migrations and employment/unemployment transitions. She also contributes to methodological advances in stochastic modeling and statistical inference for time-to-event data. Publication Trends: Her recent research, reflected in reconstructed articles, demonstrates a consistent focus on developing and applying advanced statistical models—particularly survival models, Markov chains, and mixture models—to problems in finance and economics. There is a clear progression toward more complex, data-driven models incorporating Bayesian methods, high-dimensional estimation, and time-varying effects. Scientific Awards: No awards explicitly mentioned in the source text. Advising and Grants: While specific advisees and grant funding are not listed in the available text, Professor Frydman's long-standing research program and publications in premier journals such as the Journal of the American Statistical Association and The Journal of Finance suggest a significant scholarly impact and likely history of research sponsorship. She teaches core courses including Regression & Forecasting Models , Stochastic Processes I , and Stochastic Models in Finance , indicating active engagement in graduate education. Labs and Research Teams: No specific laboratories or research groups are mentioned in the provided content. However, her research aligns with interdisciplinary efforts in financial statistics and econometric modeling, potentially involving collaboration within NYU’s broader quantitative research community.

• Causal Inference
• Data integration
• Measurement Error
• +۳ مورد دیگر

Qixuan Chen, PhD, is an Associate Professor of Biostatistics at Columbia University Mailman School of Public Health. She obtained her PhD from the University of Michigan in 2009, with dual expertise in biostatistics and survey sampling. Education: BA in Economics (Nankai University), MS in Applied Statistics (Bowling Green State University), PhD in Biostatistics (University of Michigan) Her research focuses on advanced statistical methods for complex surveys, causal inference, and handling missing data. Key contributions include developing Bayesian predictive inference frameworks using machine learning and regularized regression for integrating administrative records with survey samples. Recent publications emphasize environmental health applications, including measurement error correction for immunoassays and variable selection in multiply imputed data. Her work bridges biostatistics with computational methods for data integration. Scientific Awards: NIEHS Career Development Award, Teaching Award, Calderone Research Prize, Bryant Scholarship, Hutzinger Award She actively contributes to public health through dashboards like the New York City Neighborhoods COVID-19 tracker and PRIME radiology diagnostics platform. Grants such as R01ES035784 support her ongoing work in exposure-response analysis.

Elie Hajjمشاهده پروفایل

استاد

• Sustainable Paving Materials
• Asphalt Pavement Engineering
• Transportation Infrastructure Analysis
• +۴ مورد دیگر

Elie Hajj is a Professor in the Department of Civil & Environmental Engineering at the University of Nevada, Reno (UNR), serving as Associate Director of the Western Regional Superpave Center. His research focuses on asphalt pavement engineering, sustainable materials, and infrastructure resilience. He specializes in pavement rehabilitation, numerical modeling of dynamic load impacts, and economic analysis of pavement preservation strategies. Dr. Hajj has received recognition for his 2016 ASTM award for outstanding work on pavement rehabilitation economics. He actively engages in professional service, including TRB webinars and academic seminars on topics like pavement damage assessment and vehicle operating costs. His teaching spans graduate and undergraduate courses in pavement design, materials engineering, and advanced pavement analysis. His research integrates experimental and computational methods to address challenges in pavement performance under superheavy loads, recycled material utilization, and energy-efficient construction practices. Collaborations with industry and government agencies enhance the practical applicability of his findings.

Zelalem Negeriمشاهده پروفایل

استادیار

• Biostatistics
• Meta-Analysis
• Diagnostic Test Accuracy
• +۳ مورد دیگر

Zelalem Negeri is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the Faculty of Mathematics. His work focuses on statistical methods for diagnostic test accuracy, depression screening tools, and meta-analytic techniques. He collaborates with groups like DEPRESSD (Depression Screening Data) to improve mental health assessment methodologies. Research interests include robust meta-analysis models for accommodating outliers, optimal cutoff determination for screening tools (e.g., PHQ-9, EPDS), and evaluating diagnostic accuracy across diverse populations. His studies often use individual participant data (IPD) to enhance precision in estimating depression prevalence and screening tool performance. Recent work emphasizes validating shortened screening tools (e.g., EPDS-Dep-5) and comparing depression assessment methods across modes (e.g., self-administered vs. clinician-administered). He also investigates pandemic-related mental health impacts in populations with systemic sclerosis and explores statistical methods to mitigate biases in data-driven cutoff selection. No scientific awards are explicitly mentioned. His contributions span collaborations with interdisciplinary teams, including the Scleroderma Patient-centered Intervention Network (SPIN), and involve developing robust statistical frameworks to address variability in diagnostic studies.

Inge Hoffمشاهده پروفایل

استاد

• Materials for road construction
• Frost protection
• Laboratory testing
• +۵ مورد دیگر

Professor Inge Hoff is affiliated with the Norwegian University of Science and Technology (NTNU) in the Department of Civil and Environmental Engineering, where he has served since 2009. Prior to this, he held roles as senior researcher and research leader at SINTEF. Research Interests : Materials for road construction, frost protection, laboratory testing, pavement dimensioning, road rehabilitation, state development modeling, ground-penetrating radar surveys, and concrete/natural stone coverings. Students : Mentors active PhD fellows Lisa Hannasvik, Arman Hamidi, Clara Weber, and Shoiab Ahmad. Teaching : Coordinates courses like TBA4204/BYGT1102 Transport Infrastructure , BYGT2204 Road and Railway Construction , and BA8600 Pavement Structure Dimensioning . Recent publications highlight his expertise in granular material behavior, asphalt durability under climate stressors, and advanced structural assessment techniques. Collaborations with international researchers and presentations at major conferences (TRB, International Conference on Bituminous Mixtures) demonstrate his ongoing contributions to road engineering.

Ryan Giordanoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Variational Methods
• +۴ مورد دیگر

Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.

Zhaohai Liمشاهده پروفایل

استاد

• Genetic Epidemiology
• Statistical Methods for Meta-analysis
• Clinical Biostatistics
• +۳ مورد دیگر

Dr. Zhaohai Li Professor of Statistics at George Washington University, specializing in statistical methodologies for genetic epidemiology and clinical biostatistics. His research focuses on meta-analysis techniques, empirical Bayes methods, and population-based study designs. He has contributed extensively to improving statistical approaches in clinical trials and addressing challenges in genetic association studies. Education: Ph.D. in Statistics, Columbia University, 1989 Research Interests: His work addresses critical issues in modern biostatistics including: Population stratification in genetic studies Hardy-Weinberg equilibrium testing Optimal experimental design for case-control studies Handling missing data in genetic linkage analysis Development of robust statistical tests for complex survey data Publications Overview: Dr. Li's recent work emphasizes methodological advancements in: Bayesian approaches to population genetics Meta-analytic frameworks for combining study results Statistical solutions for multi-stage clinical trials Algorithmic improvements for genome-wide association analyses Professional Contributions: His articles consistently address practical challenges in biomedical research, bridging theoretical statistics with real-world genetic and clinical applications.

Kyle DeMarsمشاهده پروفایل

دانشیار

• Space situational awareness
• Navigation, guidance, and control
• Bayesian filtering
• +۶ مورد دیگر

Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.

Cengiz Zopluogluمشاهده پروفایل

دانشیار

• Quantitative Methods in Education
• Item Response Theory
• Computational Psychometrics
• +۴ مورد دیگر

Cengiz Zopluoglu is an Associate Professor in the Department of Special Education and Clinical Sciences at the University of Oregon's College of Education. His research focuses on quantitative methods in education, item response theory, computational psychometrics, and educational data science. He teaches advanced courses on psychometrics, statistical methodology, and data analysis using R. Education: PhD, 2013: University of Minnesota (Educational Psychology, Quantitative Methods) MA, 2009: University of Minnesota (Educational Psychology, Quantitative Methods) BA, 2005: Abant Izzet Baysal University (Mathematics Education, K-8) Research Interests: Zopluoglu's work emphasizes integrating machine learning and statistical models into educational measurement. He develops methods to detect test misconduct (e.g., item preknowledge) using response time and accuracy data, and explores automated scoring of open-ended responses using AI (e.g., transformers). His contributions include advancements in continuous response models, multidimensional IRT, and DETECT analysis for dimensionality assessment. Awards: 2023 Runner-up Prize in NAEP Math Automated Scoring Challenge (NCES) 2021 3rd Place in NIJ Recidivism Forecasting Challenge 2013 Graduate Student Research Award (University of Minnesota) Advising & Grants: Zopluoglu has advised on projects related to test security, automated scoring, and machine learning applications. His work often involves open-source tools like R and Stan, with a focus on reproducible research. Labs & Collaborations: He collaborates on initiatives like the Deterministic Gated Models for Test Security and the WrightRightNow automated scoring platform. His research leverages interdisciplinary approaches, blending psychometrics with computer science and data science.