جست‌وجوی استاد و پژوهشگر

• Extreme Value Statistics
• Financial Risk Management
• Mathematical Statistics
• +۷ مورد دیگر

Chen Zhou is a Full Professor of Mathematical Statistics and Risk Management at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He is a member of the Research Advisory Committee of Erasmus School of Economics and actively contributes to academic leadership and research governance. His research focuses on extreme value statistics and financial risk management , with significant contributions to the theoretical and applied understanding of extreme events in financial and statistical contexts. His work bridges mathematical rigor with practical applications in finance and econometrics. The recent publications highlight a strong trend in advancing methodologies for extreme value estimation, including bootstrapping techniques, tail copula modeling, dimension reduction for extremes, and semi-supervised frameworks. These works are published in high-impact journals such as the Journal of the American Statistical Association , Bernoulli , and the Journal of Finance , indicating broad disciplinary relevance across statistics, econometrics, and finance. Editorial work: Editor, Extremes (since 2015) He teaches in the Bachelor program of Econometrics and Management Science and the MSc program in Quantitative Finance, and is affiliated with the Tinbergen Institute. He has supervised multiple doctoral students, reflecting his active role in academic mentorship and research training. Chen Zhou leads a research network focused on extreme value theory, systemic risk, and statistical inference, collaborating with leading scholars in the field. His work continues to shape methodological developments in the analysis of rare and high-impact events.

Georg Stadlerمشاهده پروفایل

استاد

• Computational Mathematics
• Scientific Computing
• Uncertainty Quantification
• +۵ مورد دیگر

Georg Stadler is a Professor of Mathematics and Computer Science at New York University's Courant Institute. His research focuses on computational inverse problems, uncertainty quantification, and PDE-constrained optimization, driven by applications in climate modeling, geophysics, and plasma physics. He holds a PhD from the University of Graz (2004) and has been recognized with awards including the Gordon Bell Prize (2015) and the SIAM Computational Science & Engineering Best Paper Prize (2019). Education: Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004. M.S. (Mag.), Mathematics, University of Graz, Austria, 2001. M.S., Mathematics and Geometry Education, Graz University of Technology and University of Graz, 2001. Research Interests: Large-scale PDE solvers, Bayesian inverse problems, extreme event probability estimation, and optimization under uncertainty. Applications in climate (sea/land ice, tsunamis), plasma physics (fusion), and computational earth science (mantle flow, plate tectonics). Recent Research Trends: His work emphasizes scalable algorithms for high-dimensional Bayesian inverse problems, with applications to tsunamis, stellarator coil design, and ice sheet dynamics. Recent articles highlight advancements in extreme event probability estimation and robust multigrid solvers for incompressible Stokes equations. Awards: Gordon Bell Prize (2015) for extreme scalability of implicit solvers. SIAM Best Paper Prize (2019) for computational science contributions. Young Scientist ASCINA Award and Springer CSE Prize (2011). Advising & Grants: Current PhD student Sonia Reilly and former advisees include Chen Li and Shanyin Tong. His research is supported by NSF, ONR MURI, and the Simons Foundation. He co-leads the Computational Mathematics and Scientific Computing Seminar at Courant. Labs & Collaborations: Active in Courant’s interdisciplinary groups, focusing on high-performance computing and inverse problems. Collaborates with institutions like UT Austin on mantle dynamics and fusion energy projects.

Lillian Chongمشاهده پروفایل

استاد

• Computational Biophysics
• Biomolecular Simulations
• Protein Binding Pathways
• +۳ مورد دیگر

Lillian T. Chong is a Professor in the Department of Chemistry at the University of Pittsburgh, affiliated with the Kenneth P. Dietrich School of Arts and Sciences. She leads the Chong Lab, focusing on computational biophysics and biomolecular simulations. Her research emphasizes developing advanced simulation methods like weighted ensemble (WESTPA) for studying rare events in biomolecules, such as protein folding, binding pathways, and conformational switches. Research Interests: - Development of weighted ensemble algorithms for long-timescale simulations - Protein-protein binding kinetics and unbinding pathways - Design of switchable proteins with enhanced dynamic properties - Integration of experimental data (e.g., NMR, EPR) with simulations Recent Article Trends: Recent work explores ligand unbinding mechanisms, glycan-mediated spike protein dynamics, and force field validation. The lab’s methods are applied to drug discovery, viral entry mechanisms, and enzyme catalysis. Awards & Honors: Gordon Bell Special Prize for HPC-Based COVID-19 Research (2020) NSF CAREER Award (2009-2014) Bellet Teaching Excellence Award (2017) Advising & Grants: Advised students including Darian Yang (PhD 2023) and Jeremy Leung (PhD 2023). Funded by NSF and industry grants, including work on SARS-CoV-2 spike protein dynamics and force field development. Labs & Teams: The Chong Lab collaborates with groups at CMU and NIH, developing open-source tools like WESTPA and LPATH . Research spans Pittsburgh’s computational biophysics community, with interdisciplinary projects in drug design and protein engineering.

Jonathan Weareمشاهده پروفایل

استاد

• Monte Carlo sampling
• Multiscale analysis
• Randomized numerical linear algebra
• +۲ مورد دیگر

Jonathan Weare is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds affiliations with the Faculty of Arts and Science and the Graduate School of Arts and Science. His academic journey includes roles as an Associate Professor at the University of Chicago (2014–2019) and Assistant Professor (2011–2014), following postdoctoral work as a Courant Instructor at NYU. He earned his Ph.D. in Mathematics from UC Berkeley in 2007. His research focuses on stochastic algorithms and models, with applications in astrophysics, biophysics, computational chemistry, and climate science. Key areas include Monte Carlo methods, rare event simulation, and machine learning-driven scientific analysis. Collaborations with domain experts ensure his work addresses real-world challenges in diverse fields. Recent publications emphasize advancements in trajectory stratification, rare event prediction using machine learning, and efficient algorithms for high-dimensional problems. Notable contributions include the BAD-NEUS framework and AI-based solar system instability predictions. His group’s interdisciplinary approach bridges computational methods with scientific inquiry. Weare has advised numerous students and mentored postdocs, fostering talent in applied mathematics and computational science. His work on Mercury’s orbital dynamics and extreme weather prediction showcases the societal impact of his research. Current projects explore AI applications in weather modeling and rare event analysis, leveraging cutting-edge machine learning techniques. Labs/Teams: His research group at Courant develops stochastic algorithms and collaborates with interdisciplinary teams in computational chemistry, climate science, and astrophysics. Key collaborations include the University of Chicago and Columbia University.

Yuan Gaoمشاهده پروفایل

استادیار

• Partial Differential Equations
• Computational and Applied Mathematics
• Stochastic Analysis
• +۷ مورد دیگر

Yuan Gao is an Assistant Professor of Mathematics at Purdue University's Department of Mathematics (College of Science). His research focuses on analysis and computations of PDEs in materials science, biology, and microfluidics, with recent emphasis on optimal control, Hamilton-Jacobi equations, and non-equilibrium chemical reactions. His work is supported by NSF awards DMS-2204288 and DMS-2440651. Previously, he held the William W. Elliott Assistant Research Professor position at Duke University (2019-2021). Research interests include PDE analysis in materials science (crystal growth, dislocation dynamics), numerical methods for interface dynamics, applied stochastic analysis (Langevin dynamics, transition path theory), and mean-field games for fluid systems. He organizes the PSU-Purdue-UMD Joint Seminar on Mathematical Data Science. Key publications span topics like dislocation evolution, Wasserstein gradient flows, and stochastic algorithms for rare events. Awards include NSF CAREER funding recognizing his contributions to mathematical analysis of non-equilibrium systems.

Ronojoy Adhikariمشاهده پروفایل

مدرس

• Statistical Physics
• Soft Matter
• Stochastic Processes
• +۲ مورد دیگر

Dr Ronojoy Adhikari is a Lecturer in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Faculty of Mathematics. His research focuses on statistical physics, soft matter, stochastic processes, Bayesian inference, and machine learning. He has taught Mathematical Biology (2018–2021) and Electrodynamics (2021–2023). His work bridges theoretical frameworks with experimental insights, addressing phenomena such as active matter dynamics, non-equilibrium thermodynamics, and stochastic modeling of biological systems. Key contributions include studies on autophoretic particles, path probabilities in stochastic systems, and Bayesian approaches to epidemiological modeling. His research group, part of the Soft Matter program at DAMTP, explores interdisciplinary topics like colloidal crystallization and enzymatic network kinetics. Notable publications highlight investigations into fluctuating hydrodynamics, entropy production measurements, and the mechanics of rigid inclusions on curved surfaces. His interdisciplinary approach integrates computational methods (e.g., lattice Boltzmann simulations) with mathematical rigor to understand complex systems. While no awards are explicitly listed, his extensive publication record underscores sustained academic impact. Ongoing research includes projects on path probabilities, active particle dynamics, and the interplay between geometry and material behavior in Cosserat solids. Advising and grants are not explicitly detailed in the provided texts, but his role as a faculty member suggests involvement in student supervision and collaborative projects. His work frequently appears in top journals like Physical Review Letters , Journal of Fluid Mechanics , and Science Advances , reflecting high-quality contributions to theoretical and applied physics.

• Simulation
• Computational Probability
• Queueing Theory
• +۲ مورد دیگر

Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University's School of Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. Additionally, he serves as a Senior Fellow of the Hong Kong Institute for Advanced Study at City University of Hong Kong. His distinguished career spans over four decades, with significant contributions to the fields of simulation, computational probability, and stochastic modeling. Professor Glynn received his Ph.D. in Operations Research from Stanford University in 1982 and his B.S. with Honors in Mathematics from Carleton University in 1978. His academic journey began at the University of Wisconsin at Madison (1982-1987) before returning to Stanford, where he has held various leadership positions including Deputy Chair of MS&E (1999-2005), Director of Stanford's Institute for Computational and Mathematical Engineering (2006-2010), and Chair of MS&E (2011-2015). His research interests focus on simulation , computational probability , queueing theory , statistical inference for stochastic processes , and stochastic modeling . Professor Glynn's work has developed algorithms widely used across the field of Monte Carlo simulation, with applications in financial risk management, service systems engineering, logistics, and retail operations. His recent publications demonstrate continued innovation in areas such as numerical methods for stochastic systems, rare-event simulation, and analysis of queueing systems under various traffic conditions, showing a strong trajectory of advancing both theoretical foundations and practical applications. Professor Glynn's scholarly contributions have been recognized with numerous prestigious awards, including: Fellow of INFORMS (2007) Fellow of the Institute of Mathematical Statistics (1998) John von Neumann Theory Prize from INFORMS (2010) Member of the US National Academy of Engineering (2012) Lifetime Professional Achievement Award, INFORMS Simulation Society (2021) Philip McCord Morse Lecturer, INFORMS (2020) Throughout his career, Professor Glynn has mentored numerous doctoral students whose research has made significant contributions to operations research and related fields. His editorial service has been extensive, including founding Editor-in-Chief of Stochastic Systems and service on the editorial boards of leading journals in operations research, probability, and statistics. His professional service extends to numerous advisory boards and committees at national and international levels, reflecting his standing as a leader in his field.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +۴ مورد دیگر

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Doraiswami Ramkrishnaمشاهده پروفایل

استاد

• Chemical Engineering
• Biomedical Engineering
• Mathematical Biology
• +۵ مورد دیگر

Professor Doraiswami Ramkrishna is the Harry Creighton Peffer Distinguished Professor of Chemical Engineering at Purdue University's Davidson School of Chemical Engineering. His research focuses on applying mathematical methods to chemical and biochemical systems, including population balance modeling, stochastic processes, and cybernetic frameworks for metabolic networks. His work spans crystallization processes, cancer chemotherapy modeling, and personalized medicine. Education: B.S. from the University of Bombay (1960), Ph.D. from the University of Minnesota (1965). He joined Purdue in 1976 after faculty roles at Indian institutions. His awards include membership in the U.S. and Indian National Academies of Engineering, the AIChE Wilhelm and Thomas Baron Awards, and the 2021 William H. Walker Award for Chemical Engineering Literature. Research Interests: Cybernetic modeling of biological systems, population balances in particulate systems, stochastic modeling of rare events, and mathematical approaches to cancer treatment optimization. His group collaborates on projects involving metabolic networks, drug resistance mechanisms, and personalized hydroxyurea therapy for sickle cell disease. Awards: Over 30 honors including the 2021 Walker Award, NAE membership, and Platinum Award from Mumbai University. Advising: Mentored numerous graduate students and research associates, with notable work on lipid metabolism, chemotherapy-induced neuropathy, and crystallization dynamics. Labs/Teams: Leads the Ramkrishna Research Group, collaborating internationally on projects like cancer care engineering and metabolic engineering of bioethanol production.

Tobias Grafkeمشاهده پروفایل

دانشیار

• Rare Events
• Fluid Dynamics
• Large Deviation Theory
• +۳ مورد دیگر

Dr. Tobias Grafke is Associate Professor of Mathematics at the University of Warwick, specializing in applied and computational mathematics. His research develops tools to analyze stochastic systems in fluid dynamics, climate science, and active matter. Current projects focus on predicting rare events like AMOC collapse and turbulence proliferation using large deviation theory and numerical methods. Recent articles (2023-2025) investigate noise-induced climate tipping points, rogue wave mechanics, and scalable algorithms for stochastic PDEs. Grants include an EPSRC New Investigator Award (2020) and NSF/EPSRC joint funding. Teaches MA3J4 (Mathematical Modelling with PDE) and MA2K4 (Numerical Methods).

Christian Weinheimerمشاهده پروفایل

استاد

• Neutrino Physics
• Dark Matter Research
• Astroparticle Physics
• +۵ مورد دیگر

Professor Christian Weinheimer is a leading experimental physicist at the University of Münster's Institute of Nuclear Physics, where he holds a full professorship and serves as the Managing Director of the Institute. His research focuses on fundamental questions in particle and astroparticle physics, particularly neutrino mass measurements and the search for dark matter. He plays key roles in major international collaborations including KATRIN (neutrino mass experiment at Karlsruhe Institute of Technology) and XENONnT (dark matter search experiment at the Italian LNGS underground laboratory). Weinheimer's research interests span neutrino physics , dark matter detection , precision measurement techniques , and detector development . His group develops cutting-edge technologies for the KATRIN experiment's precision high-voltage system and electrode components, while also pioneering cryogenic distillation techniques for the XENON experiments to remove radioactive contaminants. His work extends to medical applications through the BOLD-PET project, developing novel detectors using trimethylbismuth for positron emission tomography. Analysis of his recent publications reveals a strong focus on pushing the boundaries of neutrino mass measurements, developing next-generation dark matter detectors capable of reaching the 'neutrino fog' sensitivity limit, and exploring innovative detector technologies. His work consistently combines theoretical insight with experimental ingenuity to address fundamental questions about the universe's composition and fundamental particles. Scientific awards: ERC Advanced Grant (2022) Helmholtz-Preis (2001) Dissertationspreis from Vereinigung der Freunde der Universität Mainz (1993) CERN Fellowship (1995-1996) Weinheimer actively mentors PhD students working on KATRIN background reduction, dark matter searches with XENON, precision energy measurements, and novel PET detector development. His research is supported by major grants including the ERC Advanced Grant LowRad project (2022-2027), multiple DFG-funded Collaborative Research Centers, and international collaborations with CERN, DESY, and research institutions worldwide. He also leads the development of technologies for the future DARWIN/XLZD observatory, which aims to be the most sensitive dark matter detector ever built. His laboratory operates specialized facilities including a large xenon purification system, detector development labs for the BOLD-PET project, and precision measurement equipment for high-voltage and low-background applications. Weinheimer's group collaborates extensively with other research teams at Münster University, particularly with the Cells in Motion initiative and the European Institute for Molecular Imaging.

Xianghong Jasmine Zhouمشاهده پروفایل

استاد

• Bioinformatics
• Genomics
• Computational Biology
• +۳ مورد دیگر

Dr. Xianghong Jasmine Zhou is a Professor in the Department of Pathology and Laboratory Medicine at the University of California, Los Angeles (UCLA) School of Medicine . Her work focuses on integrating genomic , epigenetic , and bioinformatic approaches for non-invasive cancer detection and monitoring through cell-free DNA analysis . Principal Investigator for multiple NIH grants (R01CA246329, U01CA230705, etc.) Developed software tools ( cfSNV , cfTools , CancerDetector ) for cell-free DNA mutation and methylation analysis Co-developed CancerLocator and HCC EV ECG score for tumor-of-origin prediction and hepatocellular carcinoma monitoring Research Interests: Specializes in 3D genome modeling , epigenetic regulation , and machine learning for liquid biopsy applications. Her work bridges computational biology with clinical translation , particularly in cancer diagnostics and treatment response assessment . Scientific Contributions: She has pioneered deep learning and population-based modeling approaches for cell-free DNA deconvolution , isoform-level functional annotation , and multi-omics integration . Her 2016 Cell paper on alternative splicing has been highly influential, with over 266 mentions.

Iason Papaioannouمشاهده پروفایل

استاد مدعو

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +۵ مورد دیگر

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

• Computational Chemistry
• Molecular Simulation
• Path Sampling Methods
• +۳ مورد دیگر

Professor Titus Sebastiaan van Erp is affiliated with the Department of Chemistry at the Norwegian University of Science and Technology (NTNU), where he has worked since 2016. His research focuses on advancing molecular simulation techniques to study complex biological and industrial processes without approximations, particularly through path sampling methods for rare events. 2016 – Present: Professor, NTNU 2012 – 2016: Associate Professor, NTNU 2006: Centre-of-Excellence Fellow, Leuven 2004: Marie Curie Fellow His research develops innovative methodologies like RETIS and REPPTIS to enhance simulation accuracy and expand accessible time/system scales. He has supervised students in DNA denaturation, electron transfer reactions, and protein folding studies. Recent publications analyze NaCl dissociation, ABL-imatinib kinetics, and permeation mechanisms. His work involves Python-based PyRETIS software development and collaborations across computational chemistry, biophysics, and materials science. 2025: NaCl Dissociation via Predictive Power Path Sampling 2025: RETIS/REPPTIS for Biomolecular Kinetics 2024: PyRETIS 3 for Boundary-Free Rare Events Scientific recognitions include: Centre-of-Excellence Fellowship (2006) Marie Curie Fellowship (2004) He has advised multiple students in masters theses on molecular simulation, including projects on DNA unwinding, electron transfer, and protein folding. His lab integrates algorithm development with applications in chemical reactions, biomolecular systems, and nanoscale materials.

• Cyber-physical systems
• Green and sustainable computing
• Mobile and pervasive computing
• +۷ مورد دیگر

Sandeep Gupta is a Professor and Director of the School of Computing and Augmented Intelligence at Arizona State University's Ira A. Fulton Schools of Engineering. He also serves as a Senior Global Futures Scientist. His work bridges computer science, engineering, and healthcare applications with a focus on creating reliable cyber-physical systems that interact safely with humans. Education: Ph.D. from The Ohio State University (1995) Research Interests: Professor Gupta's research spans cyber-physical systems, green and sustainable computing, mobile and pervasive computing, and parallel and distributed computing. His work increasingly focuses on human-in-the-loop systems where AI and humans collaborate safely, particularly in healthcare contexts. Recent research integrates large language models with cyber-physical systems to enhance safety and operational effectiveness in critical applications like medical monitoring and industrial automation. Research Trends: Analysis of Professor Gupta's recent publications reveals a strong focus on operational safety in human-AI collaborative systems, particularly in healthcare applications. His work combines physics-guided models with machine learning to detect "unknown-unknowns" in safety-critical systems, develops LLM-based approaches for medical image classification, and creates frameworks for ethical human-AI collaboration. The research increasingly addresses real-world challenges in diabetes management, epilepsy diagnosis, and industrial automation. Professional Service: IEEE Communication Letters Editorial Board, Area Editor (2008-Present) IEEE Journal on Special Areas in Communications - Issue on Body Area Network, Co-editor (2006-Present) Elsevier COMNET - Computer Network Journal, Reviewer (2008-Present) Technical Advisory Committee Chair for Networks (2005-Present) Advising and Grants: Professor Gupta has secured numerous research grants from NSF, NIH, Intel, Raytheon, and other organizations, totaling millions of dollars. His research portfolio includes projects on smart stadiums, mobile ECG sensing, power-aware scheduling, medical device verification, and sustainable data center management. He actively advises PhD and Master's students through thesis courses and research supervision, focusing on cyber-physical systems and healthcare applications. Labs and Research Groups: Professor Gupta leads research in cyber-physical systems with applications in healthcare, sustainable computing, and mobile networks. His work involves interdisciplinary collaboration across engineering, computer science, and medical domains, particularly through ASU's Global Futures initiatives.