جست‌وجوی استاد و پژوهشگر

• Time Series Analysis
• Count Data Modeling
• Statistical Computing
• +۳ مورد دیگر

Georgi Boshnakov is an Honorary Lecturer in the Department of Statistics at The University of Manchester. His research focuses on time series analysis, count data modeling, and Bayesian methods, with applications in finance, sports analytics, and statistical computing. He has contributed to the development of R packages such as countr and sarima , which provide tools for count data regression and seasonal ARIMA modeling. His work emphasizes statistical software development and methodological advancements in mixture autoregressive models and renewal processes. Education & Affiliations: PhD in Statistics (inferred from career trajectory) Member of the Statistics and its Applications group at The University of Manchester Editorial board member of the Journal of Time Series Analysis Research Interests: Bayesian analysis of time series models Count data regression using renewal processes Financial risk modeling via mixture autoregressive models Development of statistical software for R Recent Contributions: Advances in mixture autoregressive models covering complete parameter spaces Asymptotically unbiased variogram estimation techniques Portfolio optimization strategies using autoregressive frameworks Consultancy & Impact: Statistical advisory work for the University's Statistical Advisory Unit Consultancy on compensation settlements in road traffic accidents

Gah-Yi Banمشاهده پروفایل

دانشیار

• Big Data Analytics
• Machine Learning for Operations Management
• Data-driven Decision-making and Optimization
• +۴ مورد دیگر

Dr. Gah-Yi Ban is an Associate Professor of Analytics & Operations at Imperial College Business School (tenured since 2022). She previously held faculty positions at the University of Maryland and London Business School, and served as an Amazon Visiting Academic (2021–2024). She earned her PhD in Industrial Engineering & Operations Research from UC Berkeley (2012), along with multiple advanced degrees from UC Berkeley and a first-class honors in Physics & Mathematics from the University of Sydney. Her research focuses on Big Data analytics , particularly in decision-making under uncertainty with applications to operations management. Notable contributions include work on personalized pricing algorithms, data-driven inventory optimization, and machine learning applications in finance and retail. Her work has been recognized through awards including the 2021 Best OM Paper in Operations Research and inclusion in Poets & Quants ' 40 Under 40 list. Dr. Ban has taught across diverse programs including MBA, Executive MBA, and PhD-level courses, receiving accolades such as the Best Teacher Award at London Business School. She currently serves as an associate editor for top journals like Management Science and Operations Research , and chairs the Supply Chain Special Interest Group of the M&SOM Society. Her professional engagements include membership in the World Economic Forum Expert Network and roles on editorial and grant review boards. She has delivered keynotes at major conferences and a TEDx talk on algorithmic ethics ('The Power and Perils of Algorithms').

Liza Levinaمشاهده پروفایل

استاد

• High-Dimensional Statistical Inference
• Statistical Network Analysis
• Neuroimaging
• +۳ مورد دیگر

Liza Levina is the Vijay Nair Collegiate Professor and Chair of the Department of Statistics at the University of Michigan. She is affiliated with the Michigan Institute for Data and AI in Society and the Center for the Study of Complex Systems. Her expertise lies in high-dimensional statistical inference and statistical network analysis, with applications in neuroscience and imaging. She earned her PhD in Statistics from UC Berkeley in 2002 and has been at the University of Michigan since then. Her research focuses on network analysis, statistical learning, and big data, particularly in neuroimaging. She has received notable awards, including the ASA Junior Noether Award, and is a Fellow of both the ASA and IMS. She was an invited speaker at the 2018 International Congress of Mathematicians and an IMS Medallion lecturer. Levina’s work bridges theoretical advancements and practical applications, such as analyzing brain connectivity networks and developing methods for community detection in complex systems. She has contributed to methodologies for network cross-validation, pseudo-likelihood approaches, and conformal prediction in network-assisted regression. Her affiliations include leadership roles in the Department of Statistics and collaborations across interdisciplinary institutes. Her research has been recognized with prestigious acknowledgments, reflecting her impactful contributions to statistical science and its applications.

Tracy Beckerمشاهده پروفایل

دانشیار

• Structural dynamics and design
• Earthquake engineering
• Isolation & high-performance systems
• +۴ مورد دیگر

Tracy Becker is an Associate Professor in the Department of Civil and Environmental Engineering at the University of California, Berkeley, holding the Ed & Diane Wilson Presidential Chair in Structural Engineering. She serves as Vice Chair for Research & Technical Support and leads projects in structural dynamics, earthquake engineering, and novel material applications. Education: B.S. in Structural Engineering, University of California, San Diego (2006) M.S. in Civil and Environmental Engineering (SEMM), University of California, Berkeley (2007) Ph.D. in Civil and Environmental Engineering (SEMM), University of California, Berkeley (2011) Becker's research focuses on enhancing structural resilience through advanced isolation systems, hybrid simulation methodologies, and machine learning-driven design optimization. Her work addresses collapse behavior under extreme seismic loads, model updating techniques, and innovative steel component design to improve rotational capacity while minimizing damage. Recent publications highlight trends in base isolation performance evaluation, hybrid simulation advancements, and uncertainty modeling for infrastructure management. Key topics include friction pendulum systems, Gaussian process surrogate modeling for collapse prediction, and life-cycle analysis of bridge bearings. Scientific Awards: Best Paper Award at ICOSSAR International Conference Becker's contributions include experimental validation of isolation systems, development of data assimilation frameworks for structural models, and educational initiatives within the Structural Engineering, Mechanics and Materials (SEMM) program. She actively collaborates on full-scale testing and computational modeling projects.

Zubair Abdullah-Vetterمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Renewable Energy Engineering
• Solar Cell Technology
• +۳ مورد دیگر

Dr. Zubair Abdullah-Vetter is a Research Fellow at the University of New South Wales (UNSW) , affiliated with the College of Engineering and the Photovoltaic and Renewable Energy Engineering department. His work focuses on integrating advanced machine learning techniques with photovoltaic device analysis to enhance solar energy system quality and reliability. PhD in Photovoltaic Engineering Specializes in AI-driven solar cell diagnostics Active in both journal publications and conference presentations Research interests center on machine learning for renewable energy systems , particularly: Deep learning for defect analysis in solar cells Predictive modeling of photovoltaic degradation Accelerated testing protocols using AI Quantum efficiency measurement automation Luminescence image interpretation Time-series analysis of PV performance Recent publications demonstrate expertise in applying neural networks and regression techniques to photovoltaic diagnostics. The work has significant implications for climate change mitigation through improved solar technology efficiency. Scientific recognition includes: BT Imaging Photovoltaic Thesis Prize (2020) Best Poster Award at IEEE PVSC (2022) ACAP Best Student Paper Award (2024) Collaborates with leading researchers in photovoltaic materials and device reliability while contributing to practical applications in Australian solar infrastructure projects.

• Econometrics
• High-dimensional Statistics
• Factor Models
• +۳ مورد دیگر

Luca Margaritella is an Associate Senior Lecturer in the Department of Economics at Lund University, part of the Lund University School of Economics and Management (LUSEM). His research focuses on econometrics and high-dimensional statistics, with applications in financial economics and time series analysis. He is actively involved in academic activities, including organizing the FEM seminar series and contributing to international collaborations. His research interests include factor models, vector autoregressive processes, and high-dimensional statistical methods, with recent work addressing topics such as bank network connectedness and climatic attribution using Granger causality. Margaritella has published in top journals like the Oxford Bulletin of Economics and Statistics and the Journal of Business & Economic Statistics . He holds a doctoral degree and has supervised research projects. His work often intersects with applied econometrics, emphasizing methodological advancements for complex economic datasets.

Valentina Corradiمشاهده پروفایل

استاد

• Econometric Theory
• Financial Econometrics
• Time Series Analysis
• +۴ مورد دیگر

Valentina Corradi is a Professor of Econometrics at the University of Surrey. She holds a PhD in Economics from the University of California, San Diego (1994) and has held academic positions at the University of Pennsylvania, Queen Mary-University of London, University of Exeter, and University of Warwick. Her research focuses on econometric theory, financial econometrics, and time series analysis, particularly in predictive evaluation, jump diffusion models, and measurement error correction in child mortality data. Key areas include evaluating trading strategies, financial analysts' bias, and non-stationary processes. Education: PhD in Economics, University of California, San Diego (1994) Research Interests: Modeling and testing for jumps in financial assets Evaluation of trading strategies Financial analysts' bias Bandwidth selection for non-stationary processes Heaping and measurement error in child mortality data Econometric theory and predictive evaluation Publications Trends: Recent work emphasizes predictive ability tests, quantile coverage evaluation in risk analysis (e.g., Growth-at-Risk), and applications of machine learning in trade policy analysis. Her contributions span financial econometrics, macroeconomic determinants of market volatility, and methodological advancements in handling discrete outcome data via the 'heap' command. Advising and Grants: No student advisees listed. Grants and funding details are not specified in the provided text. Labs/Teams: No specific labs or research teams mentioned.

Joseph G. Ibrahimمشاهده پروفایل

استاد

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +۴ مورد دیگر

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.

Jonathan Stallrichمشاهده پروفایل

دانشیار

• Experimental Design
• Response Surface Methodology
• Statistical Collaboration
• +۴ مورد دیگر

Jonathan Stallrich is an Associate Professor and Director of PhD Programs (effective July 2025) in the Department of Statistics at North Carolina State University. He holds a Ph.D. in Statistics from Virginia Tech (2014). His research focuses on Experimental Design, Response Surface Methodology, and Statistical Collaboration, with applications in robotics, healthcare, and agricultural sciences. Recent work emphasizes optimal design frameworks for high-dimensional data analysis, A/B testing methodologies, and biomechanical prosthetics control systems. He has contributed to over 30 peer-reviewed articles since 2012, addressing topics ranging from statistical optimality criteria to interdisciplinary challenges in material science and entomology. Stallrich is actively involved in statistical consulting and maintains administrative roles supporting graduate education within the university's statistics program. Research interests include advancing experimental design theory to address modern data complexities, developing statistical methods for collaborative research environments, and applying statistical principles to biomedical engineering and agricultural pest management. His work bridges theoretical statistics with practical applications, particularly in optimizing systems for robotic prosthetics and agricultural pest monitoring. Publications highlight trends in optimal design methodologies, with a focus on supersaturated designs, Lasso sign recovery, and sequential optimization techniques. Collaborative projects span disciplines including biomechanics, polymer engineering, and veterinary medicine. No scientific awards are explicitly documented in the provided materials. His administrative responsibilities include overseeing doctoral program curricula and admissions processes beginning in 2025.

Mehmet Canerمشاهده پروفایل

استاد

• Econometrics
• Empirical International Finance
• High-Dimensional Econometrics
• +۲ مورد دیگر

Dr. Mehmet Caner is a Professor in the Department of Agricultural and Resource Economics at North Carolina State University's College of Agriculture and Life Sciences. Previously at Ohio State University (2015-2019), he specializes in econometrics with research emphasis on empirical international finance and high-dimensional econometric testing. He serves as associate editor for multiple leading journals including Journal of Econometrics and Journal of Business and Economic Statistics. His methodological innovations focus on high-dimensional econometrics, particularly developing testing frameworks for models with large parameter spaces. Research extends to applications in financial portfolio optimization, market integration analysis, and machine learning incentive structures. Recent publications (2020-2024) demonstrate strong emphasis on LASSO-based inference, deep learning integration in factor models, and constrained portfolio analysis under high-dimensional complexity. Education: Ph.D. Economics, Brown University (1996) A.M. Economics, Brown University (1993) Bachelor of Science Business Administration, Middle East Technical University (1988) Scientific Awards: Research Award, NC State University Research Award, University of Pittsburgh

Dror Baronمشاهده پروفایل

دانشیار

• Quantum Information Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dror Baron is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and has held positions including Postdoctoral Researcher at Rice University, Visiting Scientist at the Technion, and Quantitative Research Analyst at Menta Capital. His research focuses on the intersection of fast algorithms, signal processing, and information theory, with particular interests in quantum information science, machine learning, and communication systems. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (2003) M.Sc. in Electrical Engineering, Israel Institute of Technology (1999) B.Sc. in Electrical Engineering, Israel Institute of Technology (1997) Research Interests: Quantum error mitigation and algorithms Machine learning applications in signal processing Group testing and compressed sensing Generative adversarial networks (GANs) for system modeling Federated learning and privacy-preserving techniques Channel estimation and mmWave communication Awards & Honors: IEEE Senior Member (2010) M. E. Van Valkenburg Graduate Research Award (2002) Program for Outstanding Students, Technion (1994–1997) President's Roll every semester, Technion (1994–1997) Recent Contributions: His work includes developing algorithms to reduce testing quantities for pandemic management, advancing GAN-based receiver modeling, and exploring quantum advantage in finance. Collaborations span academia and industry, with notable contributions to federated learning security and compressed sensing theory.

Andreas Tsanakasمشاهده پروفایل

استاد

• Quantitative Risk Management
• Insurance Risk Modelling
• Sensitivity Analysis
• +۴ مورد دیگر

Andreas Tsanakas is a Professor of Risk Management at the Faculty of Actuarial Science and Insurance within Bayes Business School at City St George's, University of London. He joined in 2006 and previously worked at Lloyd's. His expertise spans quantitative risk management, insurance pricing fairness, sensitivity analysis, and model uncertainty. He holds a PhD from Imperial College London, alongside degrees from the University of Patras and Birkbeck College. His research focuses on developing robust risk models, addressing discrimination in insurance pricing, and advancing stress-testing methodologies. Notable contributions include work on copula models, capital allocation principles, and reverse sensitivity testing. He has received prestigious awards such as the Bob Alting von Geusau Prize (2023) and the Robert I. Mehr Award (2022). Tsanakas has supervised PhD students including Lei Fang, Silvana Pesenti, and Valeria Bignozzi. His work appears in leading journals like ASTIN Bulletin and European Journal of Operational Research. He co-organizes the Insurance Data Science Conference and the One World Actuarial Research Seminar (OWARS).

• Smart Wireless Systems (IoT)
• Information Security (Biometric Systems)
• Digital Electronics
• +۲ مورد دیگر

About Mohammad Derawi Mohammad Derawi is a Professor at NTNU's Department of Electronic Systems, part of the Faculty of Information Technology and Electrical Engineering. He leads the Smart Wireless Systems (SWS) Research Group and directs the IoT-Lab at NTNU Gjøvik. His academic journey includes a PhD from NISLab (Norway) and CASED (Germany), alongside BSc/MSc degrees in Informatics from DTU (Denmark). Research Interests Derawi specializes in Smart Wireless Systems (IoT), Information Security (Biometric Systems), Digital Electronics, and Machine Learning applications in activity recognition and e-learning. His work bridges theoretical advancements with practical implementations, emphasizing secure and efficient IoT solutions. Research Projects Notable projects include IoT-Safetraffic (RFF Innlandet), Ambulance Drone (NTNU), Wireless ECG (Innovation Norway), and the EU-funded TURBINE project. These projects focus on IoT security, healthcare applications, and smart transportation systems. Awards & Recognition Recipient of NTNU's Quality in Education Award (2017) Honored as Norway's Youngest Professor (2016) Received multiple best paper awards from IEEE/ACM/Springer Invited to the Crown Prince Couple's 50th Birthday Celebration (2023) Grants & Leadership Led multi-institutional initiatives funded by RFF Innlandet, Innovation Norway, and EU grants. His research group collaborates with industry partners to develop cutting-edge IoT and wireless communication solutions. Labs & Teams Scientific Director of NTNU's IoT-Lab, fostering innovation in IoT ecosystems and smart city technologies. Active in mentoring students and industry professionals through workshops and conferences.

Feifang Huمشاهده پروفایل

استاد

• Adaptive design of clinical trials
• Bioinformatics
• Biostatistics
• +۴ مورد دیگر

Feifang Hu is a Professor and Department Chair in the Department of Statistics at George Washington University. His expertise includes adaptive design of clinical trials, bioinformatics, biostatistics, bootstrap methods, statistical issues in personalized medicine, financial econometrics, and stochastic processes. He holds a PhD from the University of British Columbia (1994). His research focuses on developing and refining statistical methodologies for clinical trials, particularly in covariate-adaptive randomization and response-adaptive designs, with applications in precision medicine and network-based trials. Dr. Hu has published extensively in top journals such as the Journal of the American Statistical Association, Biometrics, and Annals of Statistics. His work emphasizes balancing covariates in experimental designs, improving statistical inference under adaptive frameworks, and addressing challenges in real-world clinical data. His contributions include theoretical advancements in urn models, sequential monitoring, and efficient randomized allocation strategies. Notably, Dr. Hu’s research trends highlight innovations in handling delayed responses, missing covariates, and network structures in trials. He has developed the 'carat' R package to facilitate covariate-adaptive randomization in clinical studies. His work bridges statistical theory and practical applications, influencing modern clinical trial design and personalized medicine.

Dimitris Lagoudasمشاهده پروفایل

استاد

• Shape Memory Alloys
• Smart Materials
• Metamaterials
• +۵ مورد دیگر

Dimitris Lagoudas is a Professor in the Departments of Aerospace Engineering and Materials Science & Engineering at Texas A&M University, holding the Robert C. “Bud” Hagner Chair of Engineering and the title of University Distinguished Professor. His research focuses on smart materials, including shape memory alloys (SMAs), metamaterials, additive manufacturing, and structural batteries. He has pioneered work in active origami structures, functional fatigue of materials, and the design of multifunctional materials for aerospace and energy applications. Education: Postdoctoral studies in Theoretical and Applied Physics/Mechanics at Cornell University and the Max Planck Institute (1986–88); PhD in Applied Mathematics from Lehigh University (1986); Diploma in Mechanical Engineering from Aristotle University of Thessaloniki (1982). Research interests span SMA constitutive modeling, metamaterial bandgap engineering, and additive manufacturing of functional materials. His work emphasizes material behavior under extreme conditions, fatigue analysis, and integration of structural and energy functions in materials systems. Key contributions include the development of predictive models for SMA actuators, design frameworks for adaptive structures, and advancements in structural supercapacitors. His publications reflect interdisciplinary approaches combining mechanics, materials science, and data-driven methods. Scientific awards include the SPIE Smart Structures Lifetime Achievement Award (2012) and the ASME Adaptive Structures Prize (2006). His research has been supported by grants focusing on materials discovery, multifunctional systems, and aerospace applications. Advising and grants: While no student names are listed here, his work involves collaborative projects with industry and federal agencies. His labs focus on experimental and computational studies of advanced materials, with applications ranging from morphing aerospace systems to energy storage technologies.