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Qin LiView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

• Functional Analysis
• Operator Theory
• Free Probability
• +3 more

Michael Anshelevich is a Professor of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on Functional Analysis, Operator Theory, and Free Probability, with contributions to non-commutative stochastic processes, orthogonal polynomials, and operator-valued distributions. He holds a Ph.D. from the University of California, Berkeley (2000) and a B.S. from the California Institute of Technology (1994). His work bridges combinatorial methods with advanced probability theory, addressing topics like free Lévy processes and free convolution powers. Research interests span non-commutative probability frameworks, including free stochastic measures, Fock space representations, and applications of combinatorial structures to stochastic calculus. Recent articles explore exponential products in operator algebras, Hermite polynomials in Brownian motion contexts, and depth-two actions in Fock spaces. His contributions to free probability include extending classical limit theorems to non-commutative settings and analyzing multiplicative free convolutions. Publications highlight interdisciplinary connections between functional analysis and stochastic processes, with a focus on operator-valued distributions and Jacobi parameters. While no specific awards are listed, his extensive bibliography reflects sustained impact in mathematical physics and operator theory. Advising and grants sections remain unspecified, though his research often involves collaborative projects in stochastic analysis and free probability.

Oscar Meruvia-PastorView profile

Professor

• Interactive 3D Graphics
• Non-Photorealistic Rendering
• Augmented Reality
• +8 more

Dr. Oscar Meruvia-Pastor is a faculty member in the Department of Computer Science at Memorial University of Newfoundland, within the Faculty of Science. He holds a B.Sc. from ITESM-Monterrey, Mexico, an M.Sc. from the University of Alberta, and a Ph.D. from Otto-von-Guericke Universität Magdeburg, Germany. His research focuses on interactive 3D graphics, non-photorealistic rendering, and biomedical visualization, with applications in telepresence systems, augmented reality (AR), and virtual reality (VR). He has developed tools like OMARC for respiratory condition training and GeNET for gene co-expression network analysis. Dr. Meruvia-Pastor has supervised numerous graduate students and contributed to over 50 publications. His work includes evaluating stereo correspondence methods in AR, robot arm manipulation via depth sensors, and smartphone integration in immersive VR. He has been recognized with awards such as the Best HCI Poster at Graphics Interface 2014 and a semi-finalist poster at SIGGRAPH 2015. He teaches courses in computer science, including computer graphics, multimedia development, and introductory science modules. His research lab focuses on 3D telepresence, medical visualization, and human-centered VR/AR solutions. His academic contributions span software tools for medical imaging analysis, interactive visualization systems, and educational technologies. He actively collaborates with health professionals to advance telemedicine and remote procedural training through AR platforms. His work bridges computer graphics with real-world applications in healthcare, education, and environmental advocacy.

Eddie C. RedView profile

Associate Professor

• Computational Physics
• Quantum Mechanics
• Atomic Physics
• +3 more

Eddie C. Red is an Associate Professor of Mathematics and Computational Sciences at Morehouse College , where he currently serves as the Interim Dean of the Science, Technology, Engineering, and Mathematics (STEM) Division. He earned his B.S. from Morehouse College (class of 2000) , followed by his M.S. and Ph.D. from Florida Agricultural and Mechanical University . Dr. Red also completed post-doctoral education at Lawrence Berkeley National Laboratory . Interim Dean, STEM Division Former Chair, Mathematics and Computational Science Division Former Chair, Physics & Dual-Degree Engineering Department Dr. Red’s research interests bridge atomic physics, quantum mechanics, and computational modeling , with a focus on: Photoionization cross-sections Bound states in the continuum Velocity map imaging techniques Mathematical formulations for quantum operators His work has resulted in publications in Physical Review A, Communications Physics, and the Journal of Physics B , alongside numerous conference presentations. Dr. Red has led the NuMaSS (Nuclear, Materials, and Space Science) Summer Enrichment Program for K-12 students and directed the Research Experience with Diversification Laboratory , emphasizing student training and research. Scientific awards include: Principal Investigator for Department of Energy National Nuclear Security Administration awards Dr. Red has served on multiple faculty governance committees, including the Admissions Committee , Faculty Grievance Committee , and Faculty Research Committee .

• Dynamic Decision Problems
• Equilibrium Models and Games
• Optimal Learning
• +5 more

Prof. Sven Rady is a leading academic at the Department of Economics at the Hausdorff Center for Mathematics , University of Bonn. He serves as a Hausdorff Chair for Mathematical Economics and Deputy Spokesperson for Collaborative Research Centre TR224. Research Interests include dynamic decision problems, equilibrium models, optimal learning, and strategic experimentation, with significant contributions to information economics and stochastic game theory. His work bridges mathematical modeling with economic theory, focusing on markets, learning dynamics, and policy implications. Scientific Awards include Fellow of the Econometric Society (2023) Teaching Awards at the University of Bonn (2021, 2022) CESifo Outstanding Referee Award (2013) Teaching Award of the State of Bavaria (2005) Key Collaborations involve interdisciplinary research at the intersection of economics and mathematics. He leads projects in the CRC TR224 and contributes to HCM initiatives on probabilistic modeling and information economics.

• Stochastic Processes
• Queueing Theory
• Probability Theory
• +7 more

Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.

Gudrun P. KiesmüllerView profile

Professor

• Supply Chain Management
• Inventory Management
• Spare Parts Inventory Management
• +3 more

Prof. Dr. Gudrun P. Kiesmüller is a full professor of Operations Management at TUM Campus Heilbronn since 2019. Previously, she held full professorships at Kiel University (Supply Chain Management) and Otto von Guericke University Magdeburg (Operations Management). She studied mathematics at Julius-Maximilians-University of Würzburg and later worked as a postdoc and assistant professor at Eindhoven University of Technology. Her research focuses on supply chain management, inventory management (particularly spare parts), maintenance process planning, and manufacturing system design. She develops optimization approaches for decision support, with publications in journals like IISE Transactions and Production and Operations Management . Key awards include an Honorary Doctorate (2023), ISIR Service Award (2022), and multiple teaching and reviewer awards. She has contributed to advancing stochastic inventory models and operational efficiency in complex systems. Her work integrates theoretical rigor with practical applications, addressing challenges in inventory routing, buffer allocation, and component reliability optimization. Current research emphasizes dynamic maintenance planning and capital goods design.

Garvesh RaskuttiView profile

Associate Professor

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +5 more

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

Shunichi IshiharaView profile

Professor

• Forensic Voice/Text Comparison
• Computational Linguistics
• Intonational Modelling
• +6 more

Shunichi Ishihara is a Professor at the School of Culture, History & Language, The Australian National University, where he leads research in forensic linguistics and computational linguistics. His work focuses on forensic text and voice comparison, authorship attribution, and Japanese linguistic studies. He holds qualifications including a PhD (ANU), MSc (Macquarie), MA (ANU), and BEd (Shizuoka). Research Interests: Forensic Voice/Text Comparison Computational Linguistics Intonational Modelling Japanese Language Processing Stylometric Analysis Research Trends: Recent work emphasizes likelihood ratio-based systems for authorship verification, fusion of acoustic and text features for forensic analysis, and applications of deep learning in text evidence evaluation. His studies often explore cross-lingual comparisons (e.g., Japanese, English, Vietnamese) and system validation methodologies. Grants & Projects: "Likelihood project on author recognition" (2024-2026) "Big Australian Speech Corpus" (2010-2015) Multiple forensic voice/text comparison initiatives Labs & Teams: Director of the Speech and Language Lab, collaborating on speech corpus development and forensic linguistic systems.

Shayan AziznejadView profile

Researcher

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +3 more

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

Mawuli Kouami SegnonView profile

Researcher

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +5 more

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

Stephen PankavichView profile

Professor

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +4 more

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

Lutz WarnkeView profile

Professor

• Combinatorics
• Probability Theory
• Random Graphs
• +5 more

Lutz Warnke is a Professor of Mathematics at the University of California, San Diego, with prior affiliations at Georgia Institute of Technology (where he received tenure in 2021) and Peterhouse, Cambridge University (Junior Research Fellow until 2016). His research focuses on probabilistic combinatorics, random graphs, phase transitions, and combinatorial probability, with applications to extremal combinatorics and Ramsey theory. Education : Ph.D. in Mathematics from the University of Oxford (2012), supervised by Oliver Riordan. Dr. Warnke's research explores the structure and evolution of random graphs and processes, including Achlioptas processes, Ramsey numbers, and extremal problems. His work often bridges probabilistic methods with algorithmic applications and theoretical computer science. His publications from 2022–2025 reveal trends in random graph isomorphisms, clique coloring thresholds, extremal subgraph counts, and hardness of online algorithms. Key subfields include percolation, phase transitions, and probabilistic methods applied to combinatorial structures. Scientific Awards : Dénes König Prize (2016), Alfred P. Sloan Research Fellowship (2018), NSF CAREER Award (2020), Richard Rado Prize (2014). Dr. Warnke actively supervises PhD students and postdocs, including Matthew Cho (PhD ongoing), Erlang Surya (PhD 2025), Emily Zhu (PhD 2025), and He Guo (PhD 2021). He has received teaching accolades at Georgia Tech and contributes to graduate courses in probabilistic combinatorics, random graph theory, and stochastic processes.

Scott ArmstrongView profile

Professor

• Partial Differential Equations
• Calculus of Variations
• Probability Theory
• +3 more

Scott Armstrong is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on partial differential equations, calculus of variations, and probability theory, with a specialization in stochastic homogenization of PDEs in random media and related statistical mechanical systems. He holds a Ph.D. from UC Berkeley (2009) and a B.S. from Texas A&M University (2002). Education: Ph.D. in Mathematics, University of California, Berkeley, USA (2009) B.S. in Mathematics, Texas A&M University, USA (2002) Research Interests: Scott's work addresses fundamental questions in homogenization theory, including quantitative estimates for elliptic and parabolic equations in random media, renormalization group methods, and applications to statistical mechanics. His contributions bridge analysis, probability, and mathematical physics, with a focus on rigorous mathematical frameworks for understanding macroscopic behavior from microscopic models. Publications: His recent work includes studies on anomalous diffusion, renormalization group techniques, and quantitative homogenization in high-contrast media. Over 50 peer-reviewed articles highlight his expertise in stochastic PDEs, elliptic regularity, and variational methods. Awards: No specific awards listed in the provided text. Advising & Grants: No student advisees or grant details explicitly mentioned in the text. Labs/Teams: No dedicated labs or collaborative teams explicitly noted, though his research likely involves interdisciplinary collaborations within the Courant Institute.

Patrick T. BrandtView profile

Professor

• Political Dynamics
• Time Series Analysis
• Machine Learning
• +8 more

Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)