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Rui MiaoView profile

Assistant Professor

• Reinforcement Learning
• Causal Inference
• Health AI
• +3 more

Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship. Education: PhD in Statistics, The George Washington University Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning . His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks. Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology . Scientific Recognition: 2021 ICSA Student Paper Award for work on wavelet-based independence testing Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.

• Fuzzy-random optimization
• Econometric analysis
• Mathematical Economics
• +3 more

Shvedov Aleksey Sergeevich is a Professor at the Faculty of Economic Sciences of the National Research University Higher School of Economics (HSE), affiliated with the Department of Applied Economics. He has been working at HSE since 1993 with 44 years of scientific and teaching experience. His office is located at Pokrovsky Boulevard, 11, office S524 in Moscow. Professor (1997) Doctor of Physical and Mathematical Sciences (1992) Specialist in Mathematics from Lomonosov Moscow State University, Faculty of Mechanics and Mathematics (1978) Additional education at London School of Economics (1998) and Sorbonne (2000) Professor Shvedov's research focuses on the intersection of fuzzy logic, probability theory, and economic applications. His work primarily explores fuzzy-random optimization, econometric analysis, mathematical economics, and random matrix theory. He has developed innovative approaches to modeling economic phenomena using fuzzy-probability analysis, particularly in financial time series, game theory applications, and regression models with fuzzy data. His research bridges theoretical mathematics with practical economic applications, especially in financial markets and decision-making under uncertainty. His recent publications (2020-2024) demonstrate a consistent focus on advancing fuzzy set theory applications in economics, with particular emphasis on game theory (Cournot and Bertrand oligopoly models with fuzzy parameters), statistical decision theory with fuzzy losses, and financial modeling using fuzzy systems. The work shows increasing sophistication in handling fuzzy-random variables and developing computational methods for economic applications. Scientific awards and recognition: Medal "Recognition - 25 years of successful work" (2023) Honorary Worker of Higher Professional Education of the Russian Federation (2016) Certificate of Honor from Ministry of Education and Science (2012) Multiple academic work allowances and publication bonuses Prize from Moscow Mathematical Society (1987) Inclusion in Who's Who in the World (2016) Professor Shvedov has supervised multiple doctoral dissertations, including works on portfolio theory and covariance matrix estimation. He has led research grants, notably an RFBR project on computational financial mathematics (2000-2002). His teaching portfolio includes advanced courses in probability theory, statistical analysis of financial time series, and specialized courses on fuzzy-probability analysis and financial market models. He has been actively presenting research at major international conferences, particularly the annual conferences named after S.A. Ayvazyan.

• Microeconometrics
• Financial Econometrics
• Semi- and Non-parametric Statistics
• +3 more

Antonio Cosma is a faculty member at the Department of Business Sciences, University of Bergamo. He holds a Doctorate in Economics and a Master’s in Financial Economics from Université catholique de Louvain. His research focuses on microeconometrics, financial econometrics, and semi/non-parametric statistical methods. Doctorate: Economics, Université catholique de Louvain Master’s: Financial Economics, Université catholique de Louvain His work analyzes conditional moment restrictions, tail dependence in global markets, and wavelet-based estimation techniques. Publications appear in journals like Journal of Financial and Quantitative Analysis and Bernoulli , with a focus on computational finance and statistical modeling for economic data. Recent articles investigate stochastic volatility in American options, stratification effects in econometric inference, and diversification risks in hedge fund markets. He teaches Elementi di Matematica and Strumenti per la Misurazione del Rischio at the University of Bergamo.

Matthew SmithView profile

Lecturer

• Geoscience Education
• Igneous Petrology
• Geochemistry
• +3 more

Dr. Matthew Smith is a Master Lecturer and Undergraduate Advisor in the Department of Geological Sciences at the University of Florida, housed within the College of Liberal Arts and Sciences. His primary roles involve teaching and advising undergraduate students. He holds a Ph.D. in Geology/Geochemistry from the University of Florida (1999). His research focuses on geoscience education and igneous petrology, particularly studying crustal accretion at mid-ocean ridges and volcanic systems, utilizing submersibles like Alvin and the Sentry autonomous underwater vehicle in collaborative projects. Education: Ph.D. Geology/Geochemistry, University of Florida, 1999 Research Interests: Construction of oceanic crust at mid-ocean ridges Volcanic systems analysis via geophysical surveys and submersible exploration Geoscience education methodologies Articles: His recent work (2023–2024) spans cosmological studies, galaxy cluster analysis, supernova observations, and Gaia mission data, reflecting a shift toward astrophysical collaborations while maintaining geological expertise. Awards: None explicitly listed in provided texts. Advising/Grants: No formal grant details or advised students listed, though his role involves undergraduate academic advising. Labs/Teams: Active in multidisciplinary groups using advanced observational tools like the South Pole Telescope and Dark Energy Survey instruments.

• Statistics
• Deep Learning
• Probability
• +7 more

Hailin Sang is an Associate Professor in the Department of Mathematics at the University of Mississippi , affiliated with the College of Liberal Arts . He holds a Ph.D. in Mathematics from the University of Connecticut (2008) and has previously held visiting assistant professor and postdoctoral research fellow positions at institutions including the University of Cincinnati, National Institute of Statistical Sciences/Duke University, and Indiana University. Dr. Sang’s research spans theoretical and applied statistics, with emphasis on deep learning , probability theory , empirical processes , time series analysis , random fields , nonparametric and robust statistics , and self-normalized statistics . His work also extends to survey sampling design and analysis . Recent publications highlight applications in generative adversarial networks , modified ReLU networks , and entropy estimation , with methodological contributions to limit theorems and deviation bounds for complex stochastic structures. His research has received partial support from the Simons Foundation . He has not been explicitly recognized for scientific awards in the provided text. Dr. Sang teaches a range of courses from elementary statistics to advanced statistics seminars , reflecting his broad educational contributions.

Marianna PenskyView profile

Professor

• Data Science
• Machine Learning
• Mathematical Statistics
• +3 more

Marianna Pensky is a Professor of Mathematics at the University of Central Florida (UCF), affiliated with the College of Sciences. She holds a PhD from Moscow State University (1988) and has been at UCF since 1995. Her research focuses on Data Science, Machine Learning, and Mathematical Statistics, with specializations in Bayesian methods, nonparametric statistics, inverse problems, and network models. She has published over 100 refereed articles in top journals and is an elected Fellow of the Institute of Mathematical Statistics, American Statistical Association, and International Statistical Institute. In 2023, she was awarded the Pegasus Professorship, UCF's highest honor. Dr. Pensky serves as Chief Editor of the Journal of Statistical Planning and Inference and Associate Editor of the Annals of Statistics. She has mentored 13 PhD and 9 MS students, all of whom secured post-degree employment. Her work spans theoretical and applied statistics, including contributions to stress-strength models, empirical Bayes estimation, and statistical inverse problems. Recent research emphasizes network models and multiplex clustering, with applications in bioinformatics and engineering. Dr. Pensky’s methodologies address high-dimensional inference, wavelet-based techniques, and robust statistical frameworks.

Charles S. PeskinView profile

Professor

• Immersed Boundary Method
• Mathematical Physiology (Heart, Neuro)
• Partial Differential Equations in Biology
• +7 more

Charles S. Peskin is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He specializes in mathematical modeling and simulation of biological systems, particularly cardiovascular physiology and neurophysiology. His work focuses on the Immersed Boundary Method, a computational framework for fluid-structure interaction problems. Peskin has authored influential textbooks such as Modeling and Simulation in Medicine and the Life Sciences (2nd ed., 2002) and developed simulation tools used in biomechanics research. His research spans mathematical biology, computational fluid dynamics, and stochastic processes in biological systems. He advises PhD students on interdisciplinary projects at the interface of mathematics and life sciences. Key contributions include modeling heart valve dynamics, neural signaling mechanisms, and biomolecular motor systems. Affiliations: Courant Institute, NYU Research: Immersed Boundary Method, Cardiac Mechanics, Neurophysiology Modeling Publications: 100+ articles, 2 books Teaching: Courses on PDEs in Biology, Biomolecular Motors, and Simulation Techniques Software: MATLAB programs for cardiovascular system simulations His work bridges applied mathematics with biomedical engineering, emphasizing computational methods for complex biological systems. Recent work includes studies on entropy principles in biology and rotary molecular motor mechanisms.

Dominik LieblView profile

Professor

• Functional Data Analysis
• Nonparametric Statistics
• Econometrics
• +3 more

Dominik Liebl is a Professor of Statistics at the University of Bonn's Department of Economics and a member of the Hausdorff Center for Mathematics (HCM), a Cluster of Excellence funded by the German Science Foundation (DFG). He also holds a visiting associate position at Colorado State University's Department of Statistics. His research spans Functional Data Analysis , Nonparametric Statistics , and Longitudinal Data Analysis , with applications in energy economics, finance, e-commerce, emotion psychology, and biomechanics. Recent methodological work focuses on simultaneous inference and statistical fairness . The articles in his profile demonstrate a strong emphasis on functional data methodologies applied to diverse domains like COVID-19 seroprevalence , electricity markets , and human movement science . Key subfields include confidence band design , biomechanical hypothesis testing , and high-dimensional econometric modeling . He actively contributes to open science through R-packages at CRAN/GitHub and serves as Associate Editor for the Journal of the Royal Statistical Society: Series C.

Edward SUNView profile

Professor

• Data Science
• FinTech
• Mathematical Finance
• +7 more

Edward SUN is a Professor in Data Science and FinTech at Kedge Business School since 2018, with a PhD in Statistics and Mathematical Finance from the University of Karlsruhe (2007) and HDR in Management Sciences from the University of Bordeaux (2018). His research spans applied mathematics, operations research, artificial intelligence, and financial engineering, with notable collaborations with Nobel Laureate-affiliated supervisors. Key research areas: Data Science, FinTech, Mathematical Finance, Logistics 4.0, and Business Analytics Recent publications focus on spatiotemporal analysis, digital twin applications, and machine learning in financial services Teaching domains include data-driven decision-making and predictive analytics

Sayed Ahmad SalehiView profile

Assistant Professor

• VLSI Circuits
• Stochastic Computing
• DNA Computing
• +7 more

Sayed Ahmad Salehi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering, where he directs the Computing with Unconventional Technologies (CUT) Lab. His research spans energy-efficient VLSI circuits for deep learning, stochastic computing, and DNA-based molecular programming. His research focuses on unconventional computing paradigms to address limitations in traditional electronics. Key areas include stochastic and approximate computing for energy-efficient deep learning hardware targeting IoT and edge devices, and biomolecular computing using DNA for environments incompatible with silicon. His lab develops tools like FUNSC (stochastic computing) and FUNDNA (DNA computing) that enable mathematical function computation through novel encoding schemes. Analysis of his recent publications reveals strong emphasis on in-memory computing architectures (particularly for DRAM and phase-change memory), stochastic-to-binary conversion techniques , and DNA circuit synthesis for mathematical operations. His work bridges theoretical computer science with practical hardware implementations for resource-constrained environments. Award highlights include: BIOMOD competition silver award (2023) Best paper nomination at Asilomar Conference (2022) ESWEEK Student Travel Grant (2019) He actively mentors PhD and undergraduate researchers, with recent graduates joining companies like Applied Materials. His lab secures significant NSF funding for interdisciplinary projects in stochastic computing and DNA-based systems. Current initiatives include CUT Lab's Kentucky iGEM team and collaborations on photonics-integrated computing. The CUT Lab operates at the intersection of computer architecture, molecular biology, and signal processing, with research impacting edge AI hardware, biocompatible computing, and next-generation memory systems.

Jürgen FrankeView profile

Professor

• Nonlinear Time Series Analysis
• Stochastic Processes
• Statistical Methodology
• +5 more

Prof. Dr. Jürgen Franke is a renowned academic in statistics and applied mathematics. He held the position of Professor C4 (Chair) for Applied Mathematical Statistics at Technische Universität Kaiserslautern from 1988 to 2017. Since 2017, he has been a Consultant Researcher at the Fraunhofer Institute for Industrial Mathematics (ITWM) in Kaiserslautern. His research focuses on nonlinear time series analysis, stochastic processes, and their applications in finance, risk management, and biomedical data analysis. Education: - Diplom in Mathematics, Goethe-Universität Frankfurt a.M., 1974. - PhD (Dr. phil.nat.) in Mathematics, Goethe-Universität Frankfurt a.M., 1980. - Habilitation in Mathematics, Goethe-Universität Frankfurt a.M., 1985. Research Interests: Franke's research emphasizes the development of statistical methodologies for nonlinear time series, spatial data, and stochastic processes. Key areas include local smoothing techniques (kernel and wavelet-based estimates), functional data analysis, resampling methods (bootstrap), and nonparametric approaches in machine learning. His applied work addresses challenges in finance, risk management, and biomedical data analysis where traditional methods may be insufficient. Advising and Grants: While specific grants are not detailed, Franke has collaborated extensively with researchers in applied mathematics and statistics, contributing to numerous projects. His work often intersects with interdisciplinary applications, reflecting a commitment to bridging theoretical and practical domains. Labs/Teams: Affiliated with the Department of Mathematics at TU Kaiserslautern and the Fraunhofer Institute for Industrial Mathematics (ITWM), where he focuses on applied statistical research and collaboration with industry.

Tokunbo OgunfunmiView profile

Professor

• Deep Learning
• Artificial Neural Networks
• Adaptive Signal Processing
• +7 more

Tokunbo Ogunfunmi is a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning (IPML) Research Lab at Santa Clara University's School of Engineering. He served as Associate Dean for Research and Faculty Development from 2010-2014 and as Associate Dean for Mission, Culture and Inclusion from 2021-2024. Dr. Ogunfunmi holds a Ph.D. and M.S. from Stanford University (1990, 1984) and a B.S. from the University of Ife, Nigeria (1980). His research interests span deep learning, artificial neural networks, adaptive/nonlinear signal processing, digital signal processing, and multimedia signal processing with VLSI/DSP/FPGA implementations. Dr. Ogunfunmi has published 4 books and over 200 refereed journal and conference papers in these areas. His recent publications demonstrate strong focus on deep learning applications, hardware acceleration for neural networks, quaternion-based adaptive algorithms, and efficient video/speech processing techniques. Dr. Ogunfunmi has received numerous awards including the SCU Presidential Special Recognition Award (2020), IEEE Meritorious Service Awards (2021, 2013), Carnegie Foundation Visiting Professorships (2019, 2015), and multiple Best Paper Awards. He has served in significant editorial roles including Senior Associate Editor for IEEE Signal Processing Letters and Associate Editor for several IEEE Transactions. As an active member of the professional community, he is a Senior Member of IEEE, Member of Sigma Xi, and Member of AAAS. His industrial experience includes consulting for major companies such as Broadcom, AMD, NEC, AT&T Bell Labs, and NIKON Precision Research & Development. Dr. Ogunfunmi is also a registered professional engineer in California.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

• Ionospheric Physics
• Radio Wave Propagation
• Space Weather
• +2 more

Dr. Esat Güzel serves as an Associate Professor in the Department of Physics at Fırat University's Faculty of Science, where he has maintained continuous employment since September 3, 1993. His academic career spans over three decades with sustained research activity through 2025. His research focuses on Ionospheric Physics and Radio Wave Propagation , particularly examining electromagnetic wave behavior in mid-latitude ionosphere, HF radio wave characteristics, lightning-ionosphere interactions, and space weather impacts. Key investigation areas include electron temperature dynamics, Schumann resonance correlations, solar flare effects on VLF signals, and ionospheric modeling validation using IRI models. His work bridges theoretical electromagnetics with atmospheric physics through extensive analysis of wave propagation phenomena. While no formal awards are documented in the source material, his scholarly output demonstrates consistent contributions to ionospheric research through peer-reviewed publications in journals like Journal of Atmospheric and Solar-Terrestrial Physics and Il Nuovo Cimento C. His research methodology combines experimental measurements with theoretical modeling, particularly evident in studies of solar eclipse effects on radio propagation and lightning-induced electron precipitation events. Dr. Güzel's collaborative work appears across Turkish maritime education policy analysis and international space physics research. His recent 2025 publication indicates ongoing engagement with interdisciplinary applications of geospatial analysis in maritime safety compliance. Laboratory work likely involves VLF monitoring systems and ionospheric sounding equipment based on his published methodologies.

Peter OswaldView profile

Professor

• Approximation Theory
• Function Spaces
• Applied Harmonic Analysis
• +7 more

Peter Oswald is a Professor and holds a Bonn Research Chair at the Hausdorff Center for Mathematics, affiliated with the Institute for Numerical Simulation at the University of Bonn. His research lies at the intersection of approximation theory, function spaces, and numerical methods for partial differential equations, with a focus on wavelets, splines, and multiscale computational techniques. University: University of Bonn School: Hausdorff Center for Mathematics Department: Institute for Numerical Simulation Email: oswald@ins.uni-bonn.de His research interests include Approximation Theory, Function Spaces and Applied Harmonic Analysis, Multiscale Methods in Scientific Computing, Numerical Methods for PDEs, Finite Elements, Splines, Wavelets, and Mathematical Modelling. These areas reflect a deep commitment to both theoretical foundations and computational applications in modern applied mathematics. The analysis of his recent publications reveals a consistent focus on iterative and subspace correction methods, preconditioning in sparse grid contexts, function space theory (especially Besov and Hilbert spaces), and the mathematical underpinnings of finite element and wavelet-based discretizations. His work often bridges pure and computational mathematics, with increasing exploration of stochastic and randomized algorithms in numerical linear algebra and high-dimensional problems. Peter Oswald has not been mentioned as having formal advisees in the provided texts, and no scientific awards are listed. However, his extensive collaboration with Michael Griebel and publication in prestigious journals and book series (e.g., Springer Series in Computational Mathematics) underscores his active and influential role in the mathematical community. He is involved in advanced research on space splittings, iterative solvers, and high-dimensional function approximation, contributing to both theoretical developments and practical computational frameworks. His work supports broader efforts in scientific computing, uncertainty quantification, and the numerical solution of complex physical models.