جست‌وجوی استاد و پژوهشگر

Daniela Castro Camiloمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Environmental Risk Analysis
• Spatial Statistics
• +۳ مورد دیگر

Dr. Daniela Castro Camilo is a Senior Lecturer in Statistics at the University of Glasgow's School of Mathematics & Statistics. Her research focuses on extreme value theory applied to environmental hazards, including landslides, climate extremes, and risk assessment. She leads projects like the Mitigating Landslides Impacts in Scotland (MLIS) and Geostatistical Binary Models for Extremes (GEOBEx) , funded by the Scottish Government and EPSRC. She collaborates with institutions such as the British Geological Survey and the Met Office. Her work bridges statistical methodology and environmental applications, with notable contributions to landslide hazard modeling and spatial extremes. She actively participates in international conferences and organizes events like the ESS-sponsored session at the 2024 RSS Conference. She is a core member of GLE²N (Glasgow-Edinburgh Extremes Network), fostering interdisciplinary research in statistical risk analysis. Recent projects include developing probabilistic forecasting tools for weather-driven faults in electricity networks and advancing Bayesian methods for extreme event prediction. Her research emphasizes practical solutions for resilience to environmental disasters, combining cutting-edge statistical techniques with real-world data challenges.

Danhong Songمشاهده پروفایل

مدرس

• Statistics
• Data Analysis
• Multivariate Analysis
• +۲ مورد دیگر

Danhong Song is a Full-time Lecturer in the Department of Mathematics, Applied Mathematics, and Statistics (MAMS) at Case Western Reserve University. She teaches STAT 244 (Statistical Theory with Application II) and STAT 326/426 (Multivariate Analysis and Data Mining). Her research interests align with statistical methodologies and data-driven analytical approaches, reflected in her course offerings. She can be reached at danhong.song@case.edu and is located in Sears 572. Her academic responsibilities include undergraduate and graduate instruction in statistical theory and applications. She contributes to the department's teaching mission through courses focused on multivariate analysis and data mining techniques. No specific grants or awards are mentioned in the current profile.

Timothy A. Brownمشاهده پروفایل

استاد

• Classification of Anxiety and Mood Disorders
• Psychometric Methods
• Structural Equation Modeling
• +۳ مورد دیگر

Dr. Timothy A. Brown is Professor Emeritus in Psychological & Brain Sciences at Boston University, where he directs research at the Center for Anxiety & Related Disorders. His expertise spans anxiety/mood disorders classification, temperament-psychopathology relationships, and advanced statistical methodologies. His research focuses on dimensional models of emotional disorders, psychometric evaluation of diagnostic instruments, and structural equation modeling applications. He developed influential frameworks for understanding comorbidity patterns through latent variable modeling. Publications demonstrate consistent themes in psychometric validation, transdiagnostic approaches, and longitudinal modeling of psychopathology. Recent work emphasizes COVID-19 mental health impacts and clinical assessment optimization. Dr. Brown teaches graduate courses in multivariate statistics, including confirmatory factor analysis and structural equation modeling. He authored the authoritative textbook Confirmatory Factor Analysis for Applied Research . He serves as Associate Editor of the Journal of Abnormal Psychology and collaborates internationally on classification systems for emotional disorders.

Nathaniel Rosiمشاهده پروفایل

استاد

• Inorganic Chemistry
• Materials Chemistry
• Metal-Organic Frameworks
• +۲ مورد دیگر

Nathaniel Rosi is the Covestro Professor of Chemistry at the University of Pittsburgh's Dietrich School of Arts and Sciences, Department of Chemistry. His research focuses on designing and synthesizing new materials using chemical building blocks like metal clusters and biomolecules to create hierarchical structures with tailored properties for energy, medicine, and other applications. Research interests span inorganic and materials chemistry, metal-organic frameworks (MOFs), nanoparticle assembly, and sustainable energy materials. The Rosi Lab develops unifying design principles for organizing building blocks across multiple length scales, employing diverse synthetic methods and characterization techniques including NMR, TEM, X-ray diffraction, and sorption analysis. Scientific awards include: Chancellor's Distinguished Research Award (2014) Kavli Fellow (2012) ChemComm Emerging Investigator (2011) NSF CAREER Award (2010-2015) US Delegate for Transatlantic Frontiers of Chemistry Conference (2008) The Rosi Research Laboratory trains students in interdisciplinary approaches at the chemistry-biophysics interface and welcomes graduate students to explore its multi-disciplinary research environment.

Corlett Woodمشاهده پروفایل

استادیار

• Evolutionary Ecology
• Plant-Microbe Interactions
• Genetic Pleiotropy
• +۱ مورد دیگر

Corlett Wood is Assistant Professor of Biology at the University of Pennsylvania's School of Arts & Sciences. She holds a PhD in Biology from University of Virginia (2015) and BA in English Literature from Swarthmore College (2008), with postdoctoral training at University of Toronto. Her research investigates the evolution, ecology, and genetics of species interactions in plant root microbiomes. The Wood Lab studies how genetic pleiotropy influences mutualistic and parasitic interactions using Medicago plants and their microbial symbionts. Research combines fieldwork, greenhouse experiments, and genomics to examine how shared genetic pathways impact host adaptation. Current projects investigate genetic tradeoffs between mutualism and parasitism, context-dependent trait expression, and community assembly processes. Field research occurs at University of Pittsburgh's Pymatuning Laboratory of Ecology, University of Virginia's Mountain Lake Biological Station, and University of Toronto's Koffler Scientific Reserve. Professor Wood received the Banting Postdoctoral Fellowship (2018) and NSF Graduate Research Fellowship (2010-2013), and directs the International Area Studies/Global Studies program. Honors and Awards: Banting Postdoctoral Fellowship (2018) Ecology & Evolutionary Biology Postdoctoral Fellow (2016-2018) Andrew Fleming Prize in Biology (2016) NSF Graduate Research Fellowship (2010-2013)

• Agricultural Economics
• Food Security Policy
• Development Economics
• +۷ مورد دیگر

William Burke is an Associate Professor in the Department of Agricultural, Food, and Resource Economics at Michigan State University (MSU), affiliated with the College of Agriculture & Natural Resources. His research focuses on agricultural policy, food security, and poverty dynamics in Africa, with extensive fieldwork in Zambia, Malawi, and The Gambia. He holds an MSc and PhD from MSU (2009, 2012), following Peace Corps service in The Gambia (2001–2003). Burke’s work includes analyzing fertilizer subsidies, soil management, and gender disparities in resource access. He has held roles at Stanford University and consultancies with organizations like the MwAPATA Institute in Malawi. Research interests include poverty dynamics, sustainable intensification, and cost-benefit analysis of agricultural policies. He contributes to multivariate statistical methods, notably developing the 'craggit' package in Stata. Awards include the 2017 Bruce Gardner Memorial Prize for policy analysis and recognition for top-cited papers in Agricultural Economics. Burke advises the Food Tank Board and serves on The African Journal of Agricultural and Resource Economics editorial board. Educations: PhD & MSc in Agricultural Economics (MSU, 2012 & 2009) Awards: Multiple accolades for policy research and academic contributions Labs/Initiatives: Innovation Lab for Food Security Policy, MwAPATA Institute collaboration His recent work addresses climate impacts on Malawi’s agriculture, fertilizer price volatility, and gender gaps in land access. Teaching includes multivariate statistics and survey design at MSU, Stanford, and the University of Zambia.

Marc Paolellaمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Portfolio Optimization
• +۴ مورد دیگر

Marc Paolella is a Full Professor of Empirical Finance at the University of Zurich's Department of Finance. His academic journey includes roles as Associate Professor (2003–2006) and Assistant Professor (1999–2003) at Kiel University and visiting positions at the University of Geneva. He holds a Dr. sc. pol. (1998) and Habilitation (2002) from Kiel University, alongside degrees from SUNY Stony Brook and Colorado State University. His research focuses on financial econometrics, emphasizing portfolio optimization, risk prediction, and saddlepoint approximation theory. He has authored four textbooks, including works on probability theory, statistical inference, and GARCH models. Over 50 journal articles span leading venues like the Journal of Econometrics and Journal of Financial Econometrics. Paolella's contributions include developing methods like CHICAGO for portfolio risk calculation and the COMFORT model for non-Gaussian returns. He serves as Editor-in-Chief of the journal Econometrics and holds editorial roles at multiple journals. Awards include the Elected Fellowship at the IETI and recognition for impactful research. His teaching spans advanced courses in financial econometrics, probability theory, and mathematical finance. He has supervised numerous theses and leads the Quantitative Finance program at the Science ETH UZH. Research grants include Swiss National Science Foundation funding for portfolio optimization under non-elliptical distributions.

Jürgen Bernardمشاهده پروفایل

استادیار

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +۶ مورد دیگر

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich (UZH), affiliated with the Digital Society Initiative (DSI). He leads the Interactive Visual Data Analysis (IVDA) Group and holds a joint position in the Department of Informatics within the Faculty of Business, Economics, and Informatics. His research focuses on interactive visual data analysis, explainable machine learning, and human-centered AI interfaces. Bernard completed his PhD at TU Darmstadt in 2015, followed by postdoctoral roles at TU Darmstadt and the University of British Columbia. He has received prestigious awards, including the EuroGraphics Young Researcher Award (2022) and EuroVis Young Researcher Award (2021). His work emphasizes combining human expertise with algorithms in domains like healthcare, climate science, and digital humanities. Key research interests include visual analytics for time-oriented data, interactive machine learning, and applications in healthcare. His projects address challenges such as medical data interpretation, sensor data analysis, and decision-making support systems. Bernard actively contributes to conferences like IEEE VIS and chairs workshops such as VAHC 2023. Education: PhD in Computer Science (2015, TU Darmstadt); Diploma in Computer Science (2009, TU Darmstadt) Grants: SNF Grant on Personalized Visual Analytics (2024), BMW collaboration on manufacturing analytics Labs/Teams: IVDA Group, DSI Health Community

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +۴ مورد دیگر

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

• Nuclear Materials
• Radiation Damage Physics
• Computational Materials Science
• +۴ مورد دیگر

Pär Olsson is a Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on computational and experimental studies of radiation damage in nuclear materials, with particular emphasis on understanding defect dynamics, microstructural evolution under irradiation, and the behavior of advanced nuclear fuels. He leads courses such as Multiscale Modeling of Nuclear Materials and Radiation Damage Physics, emphasizing both theoretical and practical aspects of materials science in nuclear contexts. His research interests include the development of advanced accident-tolerant fuels (e.g., UN-UO2 composites), the computational modeling of radiation effects in metals like Fe and W, and the interplay between solute atoms and defects in irradiated materials. His work combines first-principles calculations, kinetic Monte Carlo simulations, and experimental validation to address challenges in nuclear energy systems. Notable contributions include studies on fission product solubility in nuclear fuels, oxygen’s role in tungsten defect evolution, and the thermal performance of UN-based composites. His team’s projects, such as the M4F initiative (Multiscale Modelling for Fusion and Fission Materials), aim to bridge atomistic and continuum-scale models for material design. Pär Olsson collaborates extensively with international institutions and contributes to nuclear materials education through course coordination and supervision of graduate studies in physics and materials science.

Jacob Bernsteinمشاهده پروفایل

استاد

• Geometric Analysis
• Differential Geometry
• Minimal Surface Theory
• +۳ مورد دیگر

Jacob Bernstein is a Professor and Vice-Chair in the Department of Mathematics at Johns Hopkins University (JHU), part of the Krieger School of Arts & Sciences. He holds a PhD from MIT (2009) under Tobias Colding. His research focuses on geometric analysis, particularly minimal surfaces and mean curvature flow. He co-organizes the Analysis and PDE Seminar at JHU and the Metro Area Differential Geometry Seminar with the University of Maryland and Howard University. **Education**: PhD in Mathematics, MIT (2009). **Research Interests**: Geometric analysis, minimal surface theory, mean curvature flow, and their applications in differential geometry and PDEs. His work explores topics like entropy, geometric flows, and singularities in minimal surfaces. **Teaching**: Taught courses including Real Analysis, Multivariable Calculus, Riemannian Geometry, and graduate-level Real Analysis. **Affiliations**: Active in academic leadership roles at JHU and collaborates widely with institutions like UMD and Howard. His work bridges theoretical mathematics with applications in geometry and analysis.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His primary research focuses on Artificial Intelligence, Machine Learning, and their applications in healthcare, finance, and biomedical informatics. He leads interdisciplinary projects in clinical monitoring, anomaly detection, and predictive modeling of electronic health records (EHRs). Education: PhD in Electrical Engineering and Computer Science (MIT, 1997), M.Sc. in Electrical Engineering (Slovak Technical University, 1988) Affiliations: Department of Computer Science, University of Pittsburgh; School of Computing and Information His research integrates AI with probability theory, statistics, and operations research. Key areas include planning under uncertainty, machine learning for time-series data, and AI-driven solutions for clinical decision support. Current projects involve real-time patient monitoring, hierarchical models for EHR analysis, and control of biological processes. He has advised over 20 PhD students and holds grants from NIH, DARPA, and other agencies. Notable contributions include outlier detection in clinical actions, temporal predictive patterns, and state-space models for healthcare data. His work emphasizes translating AI advancements into practical clinical tools for improved patient care.

Ciarán Hanleyمشاهده پروفایل

مدرس

• Structural Engineering
• Bridge Design and Assessment
• Infrastructure Management
• +۵ مورد دیگر

Dr. Ciarán Hanley is a Lecturer in Structural Engineering at University College Cork (UCC), specialized in bridge and infrastructure systems. He holds a PhD in Civil Engineering from UCC (2017), following a BEng (Ord) and BE (Hons) in Civil Engineering (2010–2012). His research focuses on performance-based design, structural safety, and sustainable infrastructure, with contributions to bridge management systems and life-cycle assessment. He serves as a technical reviewer for journals like Structure and Infrastructure Engineering and ICE - Bridge Engineering , and is a board member of the Civil Engineering Research Association of Ireland. Education: BEng (Ord) Civil Engineering, UCC, 2010 BE (Hons) Civil Engineering, UCC, 2012 PhD Civil Engineering, UCC, 2017 Professional Memberships: Institution of Structural Engineers (IStructE) International Association of Bridge and Structural Engineering (IABSE) Fédération Internationale du Béton (FIB) International Association of Shell and Spatial Structures (IASS) His research interests emphasize probabilistic design, structural reliability, and resilience of infrastructure. Key themes include multivariate analysis of bridge networks and the integration of sustainability into infrastructure management. He has contributed to EU projects like TU1406 and serves as Rapporteur for EuroStruct. His publications span over a dozen peer-reviewed articles in journals like Journal of Structural Integrity and Maintenance and Proceedings of the ICE . Dr. Hanley’s teaching includes courses on structural mechanics, prestressed concrete, and design studios. He was a finalist for Engineers Ireland Chartered Engineer of the Year (2019) and actively engages in consultancy for bridge and marine structure design.

Ruitao Linمشاهده پروفایل

استادیار

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +۶ مورد دیگر

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.

Bertram F. Malleمشاهده پروفایل

استاد

• Moral Psychology
• Social Cognition
• Human-Machine Interaction
• +۳ مورد دیگر

Bertram F. Malle is a Professor of Cognitive and Psychological Sciences at Brown University, holding a joint appointment in the Department of Cognitive and Psychological Sciences. He is affiliated with the Humanity-Centered Robotics Initiative and has collaborated with institutions like the Army Research Laboratory and the Office of Naval Research. His academic journey includes a Master’s in Philosophy/Linguistics (1987) and Psychology (1989) from the University of Graz, Austria, followed by a Ph.D. in Psychology from Stanford University (1995). Before joining Brown in 2008, he was at the University of Oregon. His research focuses on moral psychology, social cognition, and human-robot interaction. Key interests include moral judgments, blame attribution, social norms, and the cognitive foundations of human-machine collaboration. He has been funded by NSF, DARPA, and the Department of Defense, among others. Malle’s work bridges psychology, philosophy, and AI, emphasizing moral competence in robots and the societal implications of human-robot interaction. His awards include the SESP Outstanding Dissertation Award (1995), NSF CAREER Award, and the 2019 SESP Scientific Impact Award. He is also a Fellow of multiple prestigious societies, including APS and SPSP. His teaching spans courses like Social Psychology, Moral Psychology, and Multivariate Statistics. Collaborators include experts in computer science and robotics, reflecting his interdisciplinary approach to advancing human-centric AI systems.