جستجوی استاد راهنمای دانشگاهی

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Yang Liu is a tenured Associate Professor at the Department of Management and Engineering , Linköping University, Sweden, and an Adjunct Professor at the University of Oulu, Finland. His expertise spans smart manufacturing, clean energy transition, and Industry 4.0 applications. He holds an M.Sc. and D.Sc. from the University of Vaasa, Finland. Research & Awards: Liu's work focuses on sustainable systems, decision support systems, and AI-driven energy efficiency. He has authored over 140 Web of Science publications, including top 0.1% ESI Hot Papers. He is ranked among the world's top 2% scientists (Stanford-Elsevier) and leads globally in 'big data analytics in manufacturing' and 'Industry 4.0-driven circular economy' research. Leadership & Projects: He leads projects like FlexSUS (EU Horizon 2020) and PERSEUS, developing tools for smart urban energy planning and 15-minute city models. He serves as Editor-in-Chief of Cleaner Engineering and Technology and Guest Editor for multiple journals. His research emphasizes bridging data science with sustainability challenges in manufacturing and energy systems. Key Achievements: Top-ranked in global citations, ESI Highly Cited Papers, and industry-driven sustainability frameworks. Grants: Leads EU-funded projects and collaborates with Siemens Energy on energy transition solutions. Labs & Teams: Part of the Environmental Technology and Management (MILJÖ) division and Unit for Product Service Innovation (MILJOPSI) at Linköping.

• Smart Manufacturing
• Smart Energy Systems
• Clean Energy Transition
• +۵ مورد دیگر

Rama Cont is Statutory Professor of Mathematics at the University of Oxford and Head of the Oxford Mathematical and Computational Finance Group. He holds additional positions as Senior Research Fellow at the Institute for New Economic Thinking and Director of the Oxford Martin Programme on Systemic Resilience. Cont's research focuses on stochastic analysis and mathematical finance, particularly modeling extreme market risks, systemic risk, and developing pathwise approaches in stochastic analysis. His recent work explores causal functional calculus, fractional Ito calculus, and analysis of deep neural networks. His publications demonstrate consistent development of mathematical frameworks for financial risk analysis, including recent advances in causal transport theory and market microstructure modeling. Cont received the Louis Bachelier Prize in 2010 and was elected SIAM Fellow in 2017 for contributions to stochastic analysis and financial modeling.

• Stochastic Analysis
• Mathematical Finance
• Systemic Risk
• +۲ مورد دیگر

Jin Ma is a Professor in the Department of Mathematics at the University of Southern California (USC), where he has served since 2007. He previously held professorships at Purdue University (1994–2008). His research focuses on stochastic analysis, stochastic differential equations, mathematical finance, and control theory. He directs USC's Mathematical Finance Program and serves on editorial boards for journals like Probability, Uncertainty and Quantitative Risk and SIAM Journal on Control and Optimization . Ma received his Ph.D. in Mathematics from the University of Minnesota (1992) and M.S./B.S. in Applied Mathematics from Fudan University (1985/1982). His work bridges theoretical stochastic analysis and applied domains like finance and insurance, with notable contributions to forward-backward SDEs and mean-field games. Research Highlights: Developed frameworks for stochastic control and backward SDEs in financial and insurance contexts. Advanced mean-field game models for limit order book dynamics and equilibrium analysis. Explored set-valued stochastic differential equations and their applications in risk management. Grants & Advising: Advised numerous graduate students in stochastic processes and mathematical finance. Research supported by NSF grants and industry collaborations.

• Stochastic Analysis
• Stochastic Differential Equations
• Stochastic Control Theory
• +۳ مورد دیگر

Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.

• Stochastic Analysis
• Probability Theory
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Dr Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.

• Additive Manufacturing
• Micro Metal Forming
• Tribology
• +۴ مورد دیگر

Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +۷ مورد دیگر

Yvain Bruned is a Professor of Mathematics at Université de Lorraine, Nancy, France, where he leads research in singular stochastic partial differential equations and related fields. He serves as Principal Investigator for the ERC Starting Grant LoRDeT (2023-2028), which focuses on advancing the theory of decorated trees and Hopf algebraic structures for solving singular SPDEs and dispersive PDEs at low regularity. Previously, he was a Lecturer at the University of Edinburgh (2019-2022) and completed postdoctoral work at Imperial College London and University of Warwick under Martin Hairer. His educational background includes: PhD in Mathematics (2012-2015), UPMC (Paris 6), on "Singular KPZ type equations" under Lorenzo Zambotti Master 2 in Probability and Statistics, ENS Cachan / Rennes 1, with honors Master 1 in Mathematics, ENS Cachan, with honors Bachelor in Mathematics and Computer Science, University of Rennes 1, with honors Student at ENS Cachan Brittany extension (2009-2013) Classes Préparatoires in Mathematics and Physics (2007-2009) Bruned's research centers on singular stochastic partial differential equations, with particular focus on Regularity Structures, renormalization theory, and their connections to Hopf algebras. His work bridges theoretical mathematics with applications in quantum field theory, wave turbulence, and numerical analysis. He has developed novel approaches using decorated trees to handle renormalization procedures for singular SPDEs and has extended these methods to dispersive PDEs with random initial data. His research program aims to establish existence and uniqueness results for quasilinear and dispersive SPDEs while developing algebraic tools through deformations of Hopf algebras. His extensive publication record demonstrates consistent contributions to the field of singular SPDEs, with a clear trajectory from foundational work on Regularity Structures to more recent applications in dispersive PDEs and numerical methods. The publications reveal a strong collaborative network with leading researchers in stochastic analysis, mathematical physics, and algebra. His work shows increasing sophistication in handling renormalization procedures through algebraic structures, with recent papers exploring connections between different mathematical frameworks. His major scientific recognition includes: ERC Starting Grant LoRDeT (2023-2028) Bruned actively supervises a large group of researchers, currently advising 4 PhD students and 2 postdoctoral researchers at Université de Lorraine, with several former PhD students having completed their degrees at the University of Edinburgh. His ERC grant has enabled him to organize multiple international workshops in Nancy, fostering collaboration between researchers in singular SPDEs, algebraic structures, and numerical analysis. The grant also supports the development of software platforms for decorated trees and their Hopf algebraic structures. As Principal Investigator of the ERC LoRDeT project, Bruned leads a vibrant research team based at the Elie Cartan Institute of Lorraine, which includes postdocs, PhD students, and visiting researchers. The team regularly organizes specialized workshops on topics including operads, symmetries for quantum field theory, and normal forms for singular dynamics, creating a dynamic research environment that bridges multiple mathematical disciplines.

• Singular Stochastic Partial Differential Equations
• Regularity Structures
• Renormalisation Theory
• +۵ مورد دیگر

Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.

• Stochastic Analysis
• Rough Analysis
• Functional Ito Calculus
• +۵ مورد دیگر

Jim Gatheral is a Presidential Professor of Mathematics at Baruch College, City University of New York (CUNY), where he leads the Financial Engineering MS Program. He holds a Ph.D. in Theoretical Physics from Cambridge University (1983), advised by John C. Taylor, and a B.Sc. in Mathematics and Natural Philosophy from the University of Glasgow (1979). Research Focus: His work centers on volatility modeling, market impact dynamics, optimal execution strategies, and stochastic volatility frameworks. Key contributions include rough volatility theory, affine forward variance models, and advancements in Heston and SABR models. He authored The Volatility Surface: A Practitioner’s Guide (2006), a seminal text in quantitative finance. Professional Contributions: Gatheral has published extensively in journals like Quantitative Finance , Finance and Stochastics , and SIAM Journal on Financial Mathematics . He co-developed the arbitrage-free SVI volatility surface parameterization and contributed to market impact models under perfect competition. His work integrates theoretical physics insights with financial engineering. Engagement: He delivers presentations globally, including at the SIAM Financial Mathematics and Engineering Conference (2019) and Bloomberg Quant Seminars. His research bridges academia and industry, addressing practical challenges in derivatives pricing and risk management.

• Volatility Modeling
• Market Impact
• Optimal Execution
• +۴ مورد دیگر

Francesco Cellarosi is an Associate Professor in the Department of Mathematics and Statistics at Queen's University, within the Faculty of Arts and Science. His research focuses on the intersection of dynamics, probability theory, ergodic theory, number theory, and mathematical physics. He investigates how classical number-theoretic objects exhibit random features, employing dynamical methods such as spectral theory of group actions and analysis of flows on homogeneous spaces. Educational Background: PhD in Mathematics (2011), Princeton University MSc in Mathematics (2007), Princeton University Laurea Magistrale (Master's) in Mathematics (2006), Università degli Studi di Bologna Research Interests: Dr. Cellarosi explores probabilistic phenomena in number theory, including theta sums, quadratic Weyl sums, and k-free integers. His work bridges ergodic theory and quantum mechanics, analyzing autocorrelation functions and spectral properties of physical systems. Key themes include limit theorems, random processes of number-theoretic origin, and applications to statistical mechanics. Professional Profile: He teaches advanced courses such as MATH 892 and MATH/MTH 328. His office is Jeffery Hall 506, and he maintains a Google Scholar profile and personal website. No awards are explicitly listed, but his extensive publication record reflects scholarly contributions. Labs/Teams: While no specific labs are mentioned, his collaborations span pure mathematics and mathematical physics, often involving interdisciplinary dynamics and probability.

• Pure Mathematics
• Probability Theory
• Number Theory
• +۳ مورد دیگر

Ajay Chandra is an Associate Professor in Pure Mathematics at Imperial College London's Faculty of Natural Sciences. He specializes in stochastic partial differential equations (SPDEs) with applications to statistical mechanics and quantum field theory. His work bridges probability theory, mathematical physics, and rigorous analysis of singular SPDEs. He has affiliations with the Department of Mathematics and focuses on areas like regularity structures, renormalization, and non-linear dynamics. His research interests include the analysis of singular SPDEs, gauge theories, quantum field models, and phase transitions. Notable contributions involve stochastic quantization of Yang-Mills theory, Phi^4 models, and multi-layer KPZ equations. Chandra has collaborated with leading researchers such as Martin Hairer and Hendrik Weber, advancing the field of stochastic analysis through innovative techniques like analytic BPHZ theorems and a priori bounds. His advising record includes PhD students working on topics ranging from dynamical Yukawa models to tensor field theories. Chandra's work often intersects with applied mathematics and theoretical physics, addressing foundational questions in statistical mechanics and quantum systems. He maintains an active research program with publications in top-tier journals like Inventiones Mathematicae and Communications in Mathematical Physics.

• Pure Mathematics
• Mathematical Physics
• Quantum Physics
• +۳ 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.

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

Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۳ مورد دیگر

Amrita Basak serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at Pennsylvania State University. Her research focuses on advancing metal additive manufacturing technologies, particularly for gas turbine applications. She maintains her laboratory in 233 Reber Building at University Park, PA. Her primary research interests center on laser-based additive manufacturing processes including Laser Powder Bed Fusion (L-PBF) and Laser Directed Energy Deposition (LDED). Specific expertise spans nickel-based superalloys, melt pool dynamics, microstructure-property relationships, fatigue behavior of additively manufactured components, and AI-driven process optimization. Her work addresses critical challenges in thermal distortion control, surface roughness effects, and high-temperature performance of turbine components. Analysis of her recent publications reveals strong emphasis on integrating machine learning with experimental methods to optimize additive manufacturing processes. Key trends include Gaussian process regression for melt pool modeling, Bayesian optimization for thermal management, reinforcement learning for parameter control, and multi-fidelity modeling approaches. Her research bridges fundamental materials science with practical engineering applications in aerospace and energy sectors. Scientific Awards: NSF CAREER Award (2024) for gas turbine research DARPA Young Faculty Award (2022) for multi-laser additive manufacturing Materials Research Institute Roy Award (2023) Professor Basak actively mentors graduate students including R. Pal, N. Menon, and A. Kushwaha who appear as first authors on multiple publications. Her research is supported by significant grants including NSF CAREER funding, Office of Naval Research grants (2024), and DARPA funding. Current projects include 'On-Demand 3D Printing of Food-Grade Biopolymer-Encapsulated Ferrate(VI) for Individualized and Equitable Access to Drinking Water' and metal additive manufacturing research for gas turbine hot section components.

• Additive Manufacturing
• Metal 3D Printing
• Laser Powder Bed Fusion
• +۶ مورد دیگر