جست‌وجوی استاد و پژوهشگر

• Mathematical Modeling
• Stochastic Simulation
• Computational Biology
• +۵ مورد دیگر

Dr. Pamela Burrage is an Adjunct Senior Lecturer at the School of Mathematical Sciences , Queensland University of Technology (QUT) . She holds a PhD in Mathematics from the University of Queensland (1999) and a Master of Arts (First Class Honors) from the University of Auckland. Her research focuses on mathematical modeling , stochastic simulation , computational biology , numerical methods , and high-performance computing . She has contributed significantly to parallel implementations, visualization techniques, and the numerical solution of differential equations. With over 2,145 citations and an h-index of 22, her work bridges computational science and real-world applications in biology, ecology, and engineering. Teaching areas include modeling and simulation science and engineering mathematics . Burrage has supervised research on topics like optimal drug delivery to chronic wounds and fractional models in cell motility. Her collaborations span institutions like the Institute for Molecular Bioscience (UQ) and the Queensland Parallel Supercomputing Foundation. She has also contributed to interdisciplinary projects, such as modeling jaguar abundance under climate change and cardiac fibrosis patterns using Perlin noise algorithms. Publications highlight advancements in numerical algorithms for fractional differential equations, stochastic processes in biological systems, and agent-based modeling in cancer research. Her work emphasizes translational research , integrating computational tools with practical challenges in medicine, ecology, and environmental science.

Matt Suttonمشاهده پروفایل

مدرس

• Bayesian Methodology
• Monte Carlo Methods
• High-Dimensional Statistics
• +۳ مورد دیگر

Dr. Matt Sutton is a Lecturer in Statistical Inference for Complex Models at the School of Mathematical Sciences. He earned his PhD in 2019, focusing on developing statistical methods for high-dimensional data in clinical health and biological contexts. Previously, he worked as a postdoc at Lancaster University under the Bayes4Health grant. His research emphasizes Monte Carlo methods, Bayesian methodology, and high-dimensional statistics, with a current focus on continuous-time Monte Carlo techniques to accelerate Bayesian inference. Sutton actively contributes to the Models and Algorithms research program at the Centre for Data Science. His research interests include computational statistics, specifically advancements in PDMP samplers, control variates, and scalable Bayesian methods. Notable work spans applications in genomics, geophysics, and healthcare data analysis. Sutton’s methodologies aim to enhance computational efficiency in complex statistical inference tasks. His articles reflect a trend toward optimizing sampling algorithms and addressing challenges in high-dimensional Bayesian problems. Key areas include PDMP-based sampling, debiasing techniques, and federated learning applications. Sutton has not yet reported formal scientific awards or listed advisees in the provided text. His involvement in collaborative initiatives like the Centre for Data Science underscores his commitment to interdisciplinary research.

Alen Alexanderianمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +۳ مورد دیگر

Alen Alexanderian is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), serving since 2022. Previously, he was an Assistant Professor at NC State (2015-2022), a Research Associate and Postdoctoral Fellow at The University of Texas at Austin’s ICES (2012-2015), and a Postdoctoral Fellow at Johns Hopkins University (2010-2012). He earned his PhD in Applied Mathematics from the University of Maryland, Baltimore County (2010), with prior degrees including an MS (2007) and BA in Mathematics (2005) from Hood College. His research focuses on numerical analysis, scientific computing, and uncertainty quantification. Key areas include numerical methods for inverse problems, optimization under uncertainty, Bayesian inversion, and sensitivity analysis. He develops algorithms for optimal experimental design and applies these to large-scale inverse problems in engineering and science. His recent work emphasizes hyper-differential sensitivity analysis, robust optimal design under model uncertainty, and applications in PDE-constrained optimization. His studies often bridge theoretical developments with practical implementations in fields like porous media flow, biotransport, and epidemiological models. Notable contributions include frameworks for Bayesian optimal sensor placement, variance-based sensitivity analysis techniques, and scalable computational methods for high-dimensional inverse problems. His research integrates mathematical rigor with computational efficiency, addressing challenges in data assimilation, model reduction, and risk-aware decision-making.

Yeonjong Shinمشاهده پروفایل

استادیار

• Applied and Computational Mathematics
• Artificial Intelligence
• Scientific Machine Learning
• +۶ مورد دیگر

Yeonjong Shin is an Assistant Professor of Mathematics at North Carolina State University (NC State), with prior tenure-track roles at KAIST (South Korea) and Brown University (USA). His research focuses on applied and computational mathematics, particularly in artificial intelligence, scientific machine learning, and numerical methods for partial differential equations. He holds a Ph.D. in Mathematics from The Ohio State University (2018) and dual B.S. in Mathematics and B.A. in Economics from Yonsei University (2013), with military service as a KATUSA (2009–2011). Shin’s expertise includes approximation theory, numerical optimization, and uncertainty quantification. He leads research in AI-driven scientific computing, reduced order modeling, and physics-informed neural networks. His work bridges computational mathematics and machine learning, addressing challenges in data-driven dynamical systems and high-dimensional function approximation. He has published extensively in top journals like SIAM Journals, Neural Networks, and Comput. Methods Appl. Mech. Eng. Notable contributions include thermodynamics-informed latent space dynamics (tLaSDI), S-OPT hyper-reduction algorithms, and error analysis for neural network-based PDE solvers. Shin has taught courses at NC State, KAIST, and Brown, including statistical inference, numerical optimization, and computational linear algebra. He actively participates in conferences such as SIAM CSE, ICIAM, and KSIAM, and has delivered invited talks at institutions worldwide.

• Mathematical Statistics
• Probability Theory
• Learning Theory
• +۷ مورد دیگر

Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His research lies at the intersection of mathematical statistics, probability theory, and learning theory . Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions. The recent publications reflect a strong trend in theoretical machine learning , particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field. Scientific Awards: Best Paper Award at Conference on Learning Theory (COLT), 2020 Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics , Probability Theory and Related Fields , and IEEE Transactions on Information Theory , and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research. His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.

Akshay Ramachandranمشاهده پروفایل

استادیار

• convex analysis
• convex optimization algorithms
• non-Euclidean spaces
• +۴ مورد دیگر

Akshay Ramachandran is an upcoming Assistant Professor in the Department of Computer Science at the University of British Columbia starting July 2025. Currently, he is a Postdoctoral Researcher at École Normale Supérieure de Lyon (ENS Lyon) under Professor Omar Fawzi in the QINFO group, and previously held a postdoctoral position at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His research focuses on convex analysis, optimization algorithms in non-Euclidean spaces, and applications to quantum information theory, numerical linear algebra, and high-dimensional statistics. He earned his Ph.D. from the Cheriton School of Computer Science at the University of Waterloo, advised by Lap Chi Lau. Prior to that, he graduated from UC Berkeley with a B.S. in Electrical Engineering and Computer Science, working with Luca Trevisan and Nikhil Srivastava. His work bridges fundamental mathematical theory with computational challenges in optimization and statistics. Research Interests Generalizations of convex analysis to non-Euclidean spaces Quantum information theory applications Geometric invariant theory High-dimensional statistical models Optimization algorithms for matrix and tensor scaling His recent publications emphasize algorithmic advancements in frame scaling, matrix/tensor normal models, and spectral analysis with interdisciplinary applications across computer science and pure mathematics. He has been recognized with prestigious awards including the Mathematics Doctoral Prize (second place) and the Cheriton Distinguished Dissertation Award. Professional activities include teaching roles at CWI and University of Waterloo, and active participation in workshops such as SODA, FOCS, and the Simons Workshop on Symmetry in Optimization.

Helmut Farbmacherمشاهده پروفایل

استاد

• Econometrics
• Data Science
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Helmut Farbmacher is a Professor of Applied Econometrics at the Technical University of Munich (TUM) since 2021, affiliated with the TUM School of Management. His research focuses on econometric methods applied to health economics, labor markets, experimental economics, and big data analysis. He holds a PhD in Economics from Ludwig Maximilian University of Munich (2012) and has held positions at the Max Planck Society (as Head of Health Econometrics), University of Mannheim, and LMU Munich. Research interests include causal inference, machine learning, statistical learning, and data science applications in economics. Notable work includes developing methodologies for handling complex data structures and improving instrument validity in econometric analyses. He received the Otto Hahn Medal (2013) for his outstanding scientific contributions. His research team includes researchers such as Rebecca Groh, Michael Mühlegger, Gabriel Vollert, and Yasemin Karamik. He has contributed to Stata modules like SIVREG and NWIND, advancing econometric software tools. Current activities emphasize bridging econometric theory with practical applications in health and policy evaluation.

• Computer Graphics
• Visualization
• 3D Reconstruction
• +۳ مورد دیگر

Susanne Weitz is a Researcher at the Chair of Computer Graphics and Visualization, Technical University of Munich, working under Prof. Westermann. Her office is located in room 02.13.052 at Boltzmannstr. 3, 85748 Garching b. München, with contact email susanne.weitz@tum.de and office hours Monday–Thursday 9:00–15:00. Her research focuses on advancing computer graphics through 3D Gaussian Splatting, topology optimization, and neural rendering techniques. Key interests include efficient novel view synthesis for consumer devices, scientific visualization of meteorological data, and geometry processing for cinematic applications. She bridges theoretical innovation with practical implementation, emphasizing real-time performance and accessibility. Analysis of her 2022–2025 publications reveals a strong trajectory in deep learning-enhanced graphics, particularly for 3D reconstruction and visualization. Work spans Gaussian Splatting optimizations, turbulence analysis in fluid dynamics, and lattice structure design, demonstrating interdisciplinary applications from medical visualization to climate science. At TUM, she teaches core courses including Game Physics, Visual Data Analytics, Deep Learning in Computer Graphics, and Machine Learning for 3D Geometry across winter and summer terms. Her curriculum integrates cutting-edge research like neural rendering and topology optimization into practical student projects.

Qin Fangمشاهده پروفایل

مدرس

• Dynamic Network Analysis
• Functional Data Analysis
• High-Dimensional Statistics
• +۳ مورد دیگر

Dr. Qin Fang is a Lecturer (Assistant Professor) in the Discipline of Business Analytics at the University of Sydney Business School. She joined USYD in 2024 after completing a postdoctoral fellowship at the London School of Economics (LSE) under Prof. Qiwei Yao. Her research focuses on dynamic network analysis, functional data/time series analysis, and high-dimensional statistics. She holds a PhD in Statistics from LSE (2022) and an MSc with the Winton Prize for Academic Excellence. Her teaching experience includes courses at both the University of Sydney (e.g., QBUS2820 Predictive Analytics) and LSE (e.g., ST309 Elementary Data Analytics). She has received multiple accolades, including the LSE Class Teacher Award (Highly Commended) and the University of Sydney Business School Emerging Scholar Research Fellowship (2024–2026). Dr. Fang’s publications emphasize methodological advancements in high-dimensional statistical modeling, with applications to functional time series and network dynamics. Her work bridges theory and practice, addressing challenges in big data analysis and statistical inference. Grants/Awards: Emerging Scholar Research Fellow (USYD), LSE PhD Studentships, Winton Prize Teaching: Predictive Analytics, Data Analytics for Business Capstone, Quantitative Methods

Laura Donlinمشاهده پروفایل

دانشیار

• Autoimmunity
• Cancer Immunology
• Cell Signaling and Signal Transduction
• +۹ مورد دیگر

Laura Donlin, PhD is an Associate Professor at Weill Cornell Medical School and Graduate School of Medical Sciences, with a primary appointment as Principal Investigator at the Hospital for Special Surgery (HSS) Research Institute. She serves as Co-Director of the Derfner Foundation Precision Medicine Laboratory at HSS and leads a research program focused on defining mechanisms of the human immune system at the molecular level through analysis of patient samples with high-dimensional molecular technologies. Dr. Donlin received her B.S. in Biology from Villanova University (1998), followed by a Ph.D. in Signaling and Immunology from Columbia University (2004). She completed postdoctoral training in Epigenetics and Immunology at Rockefeller University (2012) and further postdoctoral work in Human Immunology at Hospital for Special Surgery (2016). Her research program focuses on autoimmune and musculoskeletal disorders, particularly rheumatoid arthritis (RA) and related conditions. Key areas of investigation include: Rheumatoid arthritis molecular subtypes and optimized treatment Immune checkpoint inhibitor arthritis (ICI-arthritis) Autoimmune muscle disease Musculoskeletal tumors Prosthetic joint infections Synovial tumors Dr. Donlin's recent work has been instrumental in identifying new subtypes of rheumatoid arthritis through molecular analysis of patient joint biopsies. This research, published in high-impact journals including Nature, has the potential to transform RA treatment by enabling personalized approaches based on specific molecular signatures rather than the current trial-and-error methodology. Her publications reveal a strong focus on synovial tissue analysis, cellular heterogeneity, and the application of advanced computational methods to understand disease mechanisms. Her scientific contributions have been recognized with numerous awards: NIH, NIAMS K01 Career Development Award (2012) Ben Pernis Memorial Lectures awardee, FASEB Autoimmunity Conference (2017) PhRMA Foundation Predoctoral Fellow (2003) NIH, NIAID F32 NRSA Postdoctoral Fellow (2006) Dr. Donlin leads multidisciplinary research teams participating in the NIH-industry sponsored Accelerating Medicines Partnership (AMP) consortium and the NIH Impact of Genomic Variation on Function (IGVF) consortium. Her laboratory collaborates closely with HSS rheumatologists, surgeons, and pathologists to rapidly collect and analyze molecular features of disease from clinically well-characterized patients, leveraging HSS's position as the largest U.S. research hospital for orthopedics and rheumatology.

Vasilis Sarafidisمشاهده پروفایل

استاد

• Econometric Theory
• Panel Data Analysis
• Spatial Econometrics
• +۹ مورد دیگر

Professor Vasilis Sarafidis is a faculty member in the Department of Economics and Finance at Brunel University London, within the College of Business, Arts and Social Sciences. He holds the academic rank of Professor in Finance and is actively engaged in research, teaching, and academic leadership. His educational background includes a PhD in Econometrics from the University of Cambridge (2006), an MPhil in Economics and Econometrics from the same institution (2001), and a BA in Economics with Computing and Quantitative Methods from the University of Sussex (2000). His research is centered on the econometric analysis of panel data, with emphasis on model specification and testing, dynamic panels, common factor structures, spatial interactions and networks, multi-dimensional panels, and random coefficient models. Applications span banking and finance, crime analysis, natural resource allocation, and policy evaluation. His methodological contributions are particularly significant in models with heterogeneous effects and nonlinear error components. His recent publications (2022–2024) reveal a strong focus on advanced panel data techniques, including IV estimation of spatial dynamic panels, Granger non-causality tests, factor-augmented models, and rank conditions in CCE estimators. These works demonstrate a consistent trend toward improving inference in complex, high-dimensional panel settings with cross-sectional dependence and unobserved heterogeneity. He has served as Associate Editor for Empirical Economics since 2019 and as Guest Editor for the Journal of Econometrics and Empirical Economics . He is a member of the Scientific Committee for the International Panel Data Conference series since 2016. He has held leadership roles including Director of Education at Monash University (2017–2019) and Associate Dean for PhD Research at BI Norwegian Business School (2021–2023). His research has been supported by competitive funding, including a major Australian Research Council (ARC) grant (DP-170103135) for AUD 379,500 on micro-panel data with nonlinear error components, applied to alcohol-fueled violence and drug-related harm. He has advised major institutions such as the Productivity Commission (Australia), Essential Services Commission, NSW IPART, and water and energy regulators. He has organized major international conferences, including the 2015 AAE Conference, 2017 and 2021 International Panel Data Conferences, and is organizing the 2025 conference on 'Social and Sustainable Finance' at Brunel. He also contributes to the academic community through open-source software, having co-developed the Stata package xtivdfreg for IV estimation of panel data models with unobserved factors, available on GitHub.

Miguel D. Mahechaمشاهده پروفایل

استاد

• Effects of climate variability and extremes on ecosystems
• Earth system data cubes and data science
• Human environment nexus
• +۱ مورد دیگر

Miguel D. Mahecha is a Full Professor for Earth System Data Science at Leipzig University and a research group leader at the Max Planck Institute for Biogeochemistry. He is affiliated with the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and the Center for Scalable Data Analytics and Artificial Intelligence. As co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth), he focuses on integrating empirical Earth observations with theoretical frameworks to address climate-ecosystem-human interactions. PhD in Environmental Sciences (2009), ETH Zurich Diploma in Geoecology (2006), Bayreuth University His research centers on climate extremes and ecosystem responses , Earth system data cubes , and human-environment feedbacks . He develops advanced data science methodologies, including nonlinear dimensionality reduction (Isomap) and causal inference, to analyze high-dimensional Earth observations. His work bridges macroecological gradients , vegetation-climate feedbacks , and data interoperability challenges in Earth sciences. Recent publications highlight applications of machine learning (e.g., DeepExtremeCubes , Explainable Earth Surface Forecasting ) and remote sensing (e.g., SpectralIndices.Jl , On-Demand Data Cubes ). His group pioneered the Earth System Data Cube concept to unify spatiotemporal data analysis. Collaborative efforts span ecological monitoring networks and planetary boundary interactions . Current projects emphasize global vegetation dynamics , compound climate-ecosystem events , and improving data availability through open-access databases (e.g., Ddeadtrees.Earth , Dheed ). He advocates for nonlinear methods in ecological pattern analysis and causal Earth system modeling , particularly in tropical montane forests and post-conflict ecological transitions.

• Hypersonics
• Aerospace Engineering
• Planetary Entry Simulations
• +۳ مورد دیگر

Dr. Yu Liu is a Postdoctoral Research Fellow at the School of Mechanical and Mining Engineering , The University of Queensland, with affiliations to the Centre for Hypersonics . His academic career focuses on hypersonic flight , planetary entry simulations , and advanced materials processing , particularly in radiation measurement and thermodynamic modeling. Yu Liu holds a PhD in Mechanical and Aerospace Engineering from The University of Queensland (2021) and a Bachelor (Honours) in Power and Energy Systems Engineering from Xi'an Jiaotong University. His research centers on non-equilibrium radiation in planetary entry conditions , validated through expansion tube experiments and spectroscopic analysis. Key contributions include electron density measurements , test gas substitution for Saturn entry simulations, and high-speed thermography using near-infrared cameras. The 15 most recent articles (2018–2025) span journal publications in Acta Astronautica , AIAA Journal , and Experiments in Fluids , alongside conference proceedings at major aerospace symposia. Topics emphasize hypersonic flow diagnostics , plasma radiation , and planetary mission testing . Dr. Liu is available for supervision and has participated in 2024 AEDS Sarl grant for radiation reconstruction in ExoMars capsule flowfields.

Amit Sawantمشاهده پروفایل

استاد

• Radiation Oncology
• Medical Physics
• Image-Guided Radiotherapy
• +۴ مورد دیگر

Amit Sawant is a Professor and Vice Chair of Medical Physics in the Department of Radiation Oncology at the University of Maryland School of Medicine. His research focuses on advancing image-guided radiotherapy techniques, particularly for stereotactic body radiotherapy (SBRT) and motion management in cancer treatment. His academic background includes: B.E.(Hons.) in Biomedical Engineering from University of Mumbai (1996) M.S. in Biomedical Engineering from University of Tennessee (1999) Ph.D. in Biomedical Engineering from University of Michigan (2006) Dr. Sawant's research centers on two interconnected domains: Small Animal Image-Guided Radiotherapy (SA-IGRT) and Next-Generation Motion Management. In SA-IGRT, he develops orthotopic tumor models for prostate, lung, and pancreatic cancer using the SARRP platform, investigates thermally-modulated radiotherapy to widen therapeutic windows, and creates advanced imaging techniques for mapping post-radiotherapy inflammation. His motion management research addresses 4D anatomical changes through patient-specific volumetric motion modeling, GPU-accelerated particle swarm optimization for 4D treatment planning, and real-time tumor tracking with dynamic multileaf collimators (MLC) that compensate for translation, rotation, and deformation. His publication record (2008-2016) demonstrates consistent innovation in motion-adaptive radiotherapy, with emphasis on lung SBRT. Key themes include 4D treatment planning optimization, real-time MLC tracking systems, motion phantom development, and MRI-based guidance – all converging toward submillimeter targeting accuracy for moving tumors. His scientific recognition includes: John R. Cameron Young Investigator Award (AAPM 2004) ASTRO Basic Science Research Grant Award (2008) Multiple 'Best in Physics' selections at AAPM/ASTRO meetings (2010, 2014, 2015, 2016) ICCR Young Investigator Award (2007) Dr. Sawant has secured over $4.5 million in research funding as Principal Investigator, including two NIH R01 grants on personalized motion management and radiation injury to pulmonary structures. His industry collaborations with VisionRT and Varian Medical Systems have translated theoretical advances into clinical motion management solutions. He leads active research teams developing orthotopic tumor models and implementing 4D radiotherapy workflows for precision cancer treatment.

Jae-Kwang Kimمشاهده پروفایل

استاد

• Survey Sampling
• Missing Data Analysis
• Semiparametric Estimation
• +۳ مورد دیگر

Jae-Kwang Kim is a Professor in the Department of Statistics at Iowa State University's College of Liberal Arts and Sciences. He holds Fellow status in both the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS), was named LAS Dean's Professor (2020-2022), and currently serves as President of the Korean International Statistical Society (KISS) for 2023-2024. His educational background includes: PhD in Statistics, Iowa State University (2000) MS in Statistics, Seoul National University (1993) BS in Computer Science and Statistics, Seoul National University (1991) Professor Kim's research spans survey sampling, missing data analysis, semiparametric estimation, and causal inference. He has pioneered methods for handling nonignorable nonresponse, developed fractional hot deck imputation techniques, and advanced data integration approaches combining probability surveys with big data sources. His work bridges theoretical statistics with practical survey methodology challenges. His recent publications (2022-2025) reveal a concentrated focus on semiparametric imputation using Gaussian mixture models, functional calibration under non-probability sampling, and bootstrap inference for complex survey designs. Key themes include handling multivariate missing data, addressing selection bias in voluntary samples, and developing robust estimation techniques under informative sampling frameworks. His major recognitions include: Gertrude M Cox Award (2015) LAS Dean's Professor (2020-2022) Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Professor Kim has held academic appointments at Hankook University of Foreign Studies (2002-2003), Yonsei University (2004-2008), and KAIST (2016-2018), alongside his primary position at Iowa State University (2008-present). His pre-academic career included roles as Mathematical Statistician at the US Census Bureau (1999-2000) and Senior Statistician at Westat (2000-2001).