جست‌وجوی استاد و پژوهشگر

Kamal Youcef-Toumiمشاهده پروفایل

استاد

• Dynamic Systems and Controls
• Robotics
• Manufacturing Automation
• +۴ مورد دیگر

Kamal Youcef-Toumi is a Professor of Mechanical Engineering at MIT. His research focuses on dynamic systems, robotics, manufacturing automation, and high-speed atomic force microscopy (AFM). He leads the Mechatronics Research Laboratory (MRL), which develops advanced instrumentation and control systems for applications in nanotechnology, biomedical engineering, and energy systems. He has pioneered innovations like the world's largest-range high-speed AFM and in-pipe robotic leak detection systems. Education: B.Sc. (University of Cincinnati), M.Sc. and Ph.D. (MIT) Affiliations: Director of Global MIT At-Risk Fellows Palestine, Co-Director of the Center for Complex Engineering Systems, and Director of the Ibn Khaldun Fellowship for Saudi Arabian Women. Research Interests Dr. Youcef-Toumi's work spans: High-speed AFM for real-time nanoscale imaging Robotics for pipeline inspection and rehabilitation Energy-water nexus and smart grid systems Biomedical applications of AFM in cartilage rheology studies Soft robotics and tactile sensing His lab's inventions include patented leak detection robots and adaptive nanopositioning systems. Recognition He has received the National Instruments Engineering Impact Award (2014) and NSF's Presidential Young Investigator Award (1987). Over 30 patents highlight his contributions to mechatronics and nanotechnology. Teaching Teaches core courses like Introduction to Robotics (2.12) and Analysis and Design of Feedback Control Systems (2.14) . His educational innovations include AFM curriculum development for engineering students. Labs & Teams Leads the Mechatronics Research Lab, collaborating with industry partners on projects like technorganic printing and autonomous underwater vehicles inspired by fish locomotion.

Ofodike Ezekoyeمشاهده پروفایل

استاد

• Combustion
• Heat Transfer
• Thermal Fluids Systems
• +۴ مورد دیگر

Ofodike Ezekoye is a Professor and holds multiple endowed chairs at the University of Texas at Austin's Cockrell School of Engineering, Department of Mechanical Engineering. His research focuses on combustion, heat transfer, and fire safety engineering, particularly in high-temperature systems like combustion engines, furnaces, and structure fires. He has led over 200 peer-reviewed publications and secured funding from agencies including the Department of Commerce, Air Force Office of Scientific Research (AFOSR), and Department of Energy (DOE). His current work addresses thermal runaway in lithium-ion batteries, fire risk modeling, and energy-efficient furnace optimization. Research Interests: Ezekoye’s expertise spans combustion fundamentals, fire dynamics, and energy systems. He leads the UT Fire Research Group, investigating fire safety in residential and industrial contexts, including firebrand ignition mechanisms, battery failure analysis, and smoke transport in high-rise buildings. His interdisciplinary approach combines experimental methods with computational modeling (e.g., CFD, Bayesian inference) to address practical challenges in fire forensics and sustainable engineering. Awards: He holds prestigious chairs such as the Cockrell Family Dean's Chair in Engineering Excellence and the John T. MacGuire Professorship. His funded projects include smart manufacturing for steam-methane reformers and multi-objective optimization of insulation materials for sustainable designs. Key Contributions: Developed methodologies for fire origin determination via Bayesian inference, ultrasonic inspection of lithium-ion batteries, and reduced-order models like Fire-STORM for high-rise fire simulations. Collaborates with industry partners on real-world applications like furnace heat integration and battery safety standards. Labs/Groups: UT Fire Research Group, with active projects in battery thermal management, fire plume modeling, and acoustic-based fire detection systems.

• Numerical Linear Algebra
• Randomized Algorithms
• Genomics
• +۵ مورد دیگر

Petros Drineas is the Department Head and Professor of Computer Science at Purdue University's College of Engineering. His research focuses on randomized numerical linear algebra (RandNLA), algorithms for large-scale data analysis, computational genomics, and machine learning. He leads the ENIGMA-TS initiative for global neuroimaging genetics collaboration and has developed tools like TeraPCA for tera-scale genotype analysis. Drineas has held leadership roles in NSF and BSF-funded projects, including studies on Tourette syndrome, Alzheimer's disease, and algorithmic efficiency. His work bridges theoretical computer science with applications in genetics, neuroscience, and optimization. Key contributions include matrix sketching frameworks, randomized algorithms for PCA, and methods for GWAS and polygenic risk score analysis. His grants include funding for RandNLA applications in inverse problems, fast least squares solvers, and collaborative research on genetic variation. He has pioneered methods for handling massive datasets in genetics, such as the MaSk-LMM framework for linear mixed models and Patch2Self2 for self-supervised denoising.

Gabriel Peyréمشاهده پروفایل

استاد پژوهشی

• Optimal Transport
• Machine Learning
• Data Science
• +۴ مورد دیگر

Gabriel Peyré is a CNRS Research Professor at the Department of Mathematics and Applications (DMA) of École normale supérieure (ENS) in Paris, France. A specialist in data science and artificial intelligence, he is renowned for his work on optimal transportation theory and its applications to imaging, machine learning, and neural network training. His research bridges mathematical theory with computational algorithm design, earning him the CNRS Silver Medal (2021) and multiple European Research Council (ERC) grants, including the 2024 Advanced Grant. Research interests include: Optimal Transport Machine Learning AI Theory Image Processing Computational Mathematics Neural Network Training His recent publications focus on advancing optimal transport methods in AI, with applications in neural network learning, spatial transcriptomics, and unsupervised data analysis. He has developed algorithms for large-scale optimal transport computations and contributed to theoretical understanding of transformer models and residual networks. Scientific awards: CNRS Silver Medal (2021) ERC Advanced Grant (2024) ERC Consolidator Grant (2016) ERC Starting Grant (2011) Blaise-Pascal Prize from the Academy of Sciences (2017) He supervises PhD students and postdoctoral researchers, including Raphaël Barboni, Valérie Castin, and Geert-Jan Huizing. His work involves collaborations with institutions like INRIA, MIT, and Heriot-Watt University. Peyré's affiliations include the Center for Data Sciences at ENS, where he contributes to interdisciplinary projects in biology and physics.

Deeksha Adilمشاهده پروفایل

استادیار

• Algorithms
• Optimization
• Machine Learning
• +۴ مورد دیگر

Deeksha Adil is a Junior Fellow at the Institute for Theoretical Studies in ETH Zurich since January 2023. Her research focuses on designing fast algorithms with provable guarantees for problems in optimization, machine learning, and theoretical computer science. In August 2026, she will transition to an Assistant Professor (Reader) position in the School of Technology and Computer Science at the Tata Institute of Fundamental Research in Mumbai. Ph.D. in Computer Science from University of Toronto (2022), supervised by Prof. Sushant Sachdeva BS-MS in Mathematics from Indian Institute of Science Education and Research, Pune (2017) Visiting Researcher at Simons Institute for Theory of Computation (2023) Research Associate at University of Michigan (2022) Visiting Student at Institute for Advanced Study, Princeton (2019) Dr. Adil's research program centers on algorithm design for optimization problems, particularly lp-norm regression and related challenges. She develops methods that leverage optimization theory and continuous analysis to create efficient algorithms with theoretical guarantees. Her work spans theoretical computer science, machine learning, and numerical analysis, with applications in network optimization and statistical learning. Her publication record shows consistent progress in developing faster algorithms for fundamental optimization problems. Recent work extends into non-convex optimization, covariate shift adaptation, and dynamic algorithms for linear algebra problems. She publishes regularly in top-tier venues including Journal of ACM, NeurIPS, ICALP, and SODA. At the University of Toronto, she served as Teaching Assistant for multiple advanced courses including Algorithm Design, Algorithmic Game Theory, Theory of Computation, and Numerical Analysis, demonstrating strong pedagogical skills alongside her research excellence.

• Probability Theory
• Mathematical Statistics
• Characterizations of Distributions
• +۷ مورد دیگر

Prof. Dr. hab. Jacek Wesołowski is a faculty member at Warsaw University of Technology, affiliated with the Faculty of Mathematics and Information Science, where he serves as Head of the Department of Probability and Mathematical Statistics. He is actively involved in research and teaching, with a focus on probability theory and mathematical statistics. His research interests include probability theory, mathematical statistics, characterizations of distributions, stochastic processes, survey sampling, multivariate analysis, free probability, and quadratic harnesses . These areas are evident from his extensive publication record in top-tier journals such as the Annals of Probability, Journal of Multivariate Analysis, and Stochastic Processes and their Applications. The trends in his recent publications reveal a strong focus on distributional characterizations (e.g., gamma, Kummer, Wishart), regression properties of order and record statistics , quadratic harnesses and martingale structures , and methodological contributions to survey sampling and imputation . His work often bridges theoretical probability with statistical applications. No scientific awards were mentioned in the provided text. He advises graduate students, as indicated by his organization of probabilistic workshops for PhD and master’s students. He has collaborated extensively with researchers such as W. Bryc, G. Letac, H. Massam, and G. Rempała. While no specific grants are listed, his sustained research output suggests active funding. He teaches core courses such as Probability Calculus I/II and Survey Sampling. There is no mention of specific labs or research teams, but his departmental leadership and collaborative publications suggest an active research group environment.

Michael Zabarankinمشاهده پروفایل

استاد

• Optimization
• Inverse Optimization
• Risk Analysis
• +۱۵ مورد دیگر

Michael Zabarankin is a Professor and Department Chair in the Department of Mathematical Sciences at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. His research bridges applied mathematics, optimization, and real-world applications in finance, engineering, and environmental systems. His research interests include optimization under uncertainty, risk analysis, inverse optimization, machine learning, threat detection, network optimization, and fluid dynamics. He has made significant contributions to the theory of deviation and risk measures, particularly in portfolio optimization, and has developed mathematical models for electrohydrodynamic drop deformation and shape optimization in viscous flows. The recent trend in his publications shows a strong focus on risk modeling in finance (e.g., deviation measures, drawdowns, entropy), machine learning under adversarial conditions, and mathematical modeling of multiphase fluid systems. His work often combines theoretical rigor with practical applications in security, environmental science, and financial engineering. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Advising and Grants: While specific students and grants are not listed, Dr. Zabarankin has extensive collaborative research, particularly with Bogdan Grechuk, Stan Uryasev, and Alexander Molyboha, suggesting active mentorship and grant-funded projects in optimization and applied mathematics. Labs and Research Teams: Although no formal lab name is provided, his work in optimization, risk analysis, and fluid dynamics suggests leadership in research groups focused on mathematical modeling, decision science, and computational mechanics within the Department of Mathematical Sciences.

Shuchin Aeronمشاهده پروفایل

دانشیار

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +۸ مورد دیگر

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Herbie Leeمشاهده پروفایل

استاد

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +۷ مورد دیگر

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

• Applied Analysis
• Computational Probability
• Statistics
• +۵ مورد دیگر

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

Huey-Wen Linمشاهده پروفایل

دانشیار

• Lattice Quantum Chromodynamics (QCD)
• Hadron Structure
• High-Performance Computing
• +۳ مورد دیگر

Huey-Wen Lin is an Associate Professor at Michigan State University with joint appointments in the Department of Computational Mathematics, Science and Engineering and the Department of Physics & Astronomy. She joined MSU in 2016 as a high-energy particle theorist specializing in lattice Quantum Chromodynamics (QCD). Her research utilizes high-performance computing to study proton/neutron structure and fundamental particle interactions. Education She earned her Ph.D. in Physics from Columbia University in 2006. Research Focus Her work bridges theoretical particle physics and computational mathematics, with emphasis on: Lattice QCD simulations of hadronic structure Parton distribution functions and gluon dynamics Machine learning applications in particle physics Quantum computing algorithms for QCD Precision studies of nucleon properties Recent publications demonstrate strong focus on gluon distributions in hadrons, lattice-informed machine learning frameworks, and theoretical tools for neutrino scattering experiments. Her work consistently integrates high-performance computing with fundamental physics. Awards and Leadership NSF CAREER Award (2017) for research on "Constraining Parton Distribution Functions for New-Physics Searches" Organizing Chair of the 36th International Symposium on Lattice Field Theory Contributions She has co-edited foundational texts ( Lattice QCD for Nuclear Physics ) and develops computational methodologies advancing precision in hadronic physics. Her leadership in large-scale collaborations (e.g., CTEQ-TEA) strengthens synergy between lattice calculations and experimental particle physics.

Dimitrios Pappasمشاهده پروفایل

پژوهشگر

• Statistics
• Linear Algebra
• Image Processing
• +۵ مورد دیگر

Dimitrios Pappas is a faculty member at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business . His research spans multiple disciplines, including Statistics , Linear Algebra , Image Processing , and Financial Mathematics . His work focuses on generalized inverses , matrix factorization , and quadratic optimization , with applications in image deblurring , credit risk modeling , and smart local energy systems . Recent publications highlight his contributions to network intrusion detection , Markov chain lumpability , and symbolic computation methods . While no specific scientific awards or student advising details are provided in the available text, his extensive publication record demonstrates expertise in applied mathematical modeling and computational techniques . He can be contacted at dpappas@aueb.gr .

• Epistemology
• Ethics
• Foundationalism and Coherentism
• +۵ مورد دیگر

Timm Triplett is an Associate Professor in the Department of Philosophy at the University of New Hampshire, specializing in epistemology and ethics with significant contributions to the foundationalist-coherentist debate, moral justification, and philosophy education. His educational background includes: B.A. in Philosophy from Antioch College M.A. from the University of Massachusetts - Amherst Ph.D. in Philosophy from the University of Massachusetts - Amherst Triplett's research explores epistemological frameworks (particularly Wilfrid Sellars' work), ethical theory (focusing on Bernard Gert's moral system), computer ethics, ancient philosophy, and metaphilosophy. He actively promotes philosophical inquiry in K-12 education through the Philosophy for Children (P4C) movement, advocating for a universally shared morality and developing pedagogical approaches for classroom implementation. His publication trends (2014-2021) reveal sustained engagement with moral philosophy (especially Gert's framework), Sellars' epistemology, and foundationalism, consistently bridging theoretical analysis with practical applications in computer ethics and educational contexts. Triplett served as Principal Investigator for the HYPE Program (2018-2020), funded by the Charles Koch Foundation. His extensive teaching portfolio includes Ancient Philosophy, Critical Thinking, Eastern Philosophy, Ethics and Society, and specialized courses on teaching philosophy in K-12 settings, reflecting his commitment to both undergraduate education and pre-college philosophical engagement.

Polona Oblakمشاهده پروفایل

استاد

• Matrix Theory
• Graph Theory
• Spectral Graph Theory
• +۷ مورد دیگر

Polona Oblak serves as a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, where she is an integral member of the Laboratory for Mathematical Methods in Computer and Information Science. Her teaching responsibilities span foundational courses including Linear Algebra, Mathematical Modelling, and multiple levels of Mathematics instruction, reflecting her dual expertise in theoretical mathematics and computational applications. Her research centers on advanced Matrix Theory and Graph Theory, with pioneering contributions to Spectral Graph Theory and Inverse Eigenvalue Problems. She investigates structural properties of commuting matrices, nilpotent matrix centralizers, and tropical semiring algebra, extending theoretical frameworks to practical applications in computer vision and statistical analysis. Recent interdisciplinary projects like "DeepBeauty" demonstrate her ability to bridge pure mathematics with industry-relevant solutions in fashion technology. Analysis of her 15 most recent publications (2021-2025) reveals a dominant focus on spectral graph phenomena, particularly the inverse eigenvalue problem across diverse graph structures including trees, block graphs, and unicyclic graphs. Her work on tropical matrix factorization (e.g., Faststmf algorithm) provides efficient computational tools for sparse data, while theoretical breakthroughs like the "liberation set" concept redefine boundaries in spectral graph theory. This research trajectory shows increasing integration of algebraic methods with machine learning applications. Professor Oblak has secured substantial research funding through the Slovenian Research Agency (ARRS) and international collaborations, including the ongoing "Computer Vision" program (2019-2024) and bilateral projects with Bosnia and Herzegovina on nilpotent orbits. Her leadership in computationally intensive statistical methods (2016-2019) and deep generative models for the beauty industry (2020-2023) demonstrates consistent ability to translate theoretical advances into funded research initiatives, though specific student supervision details remain unlisted in available sources. Within the Laboratory for Mathematical Methods in Computer and Information Science, she contributes to a synergistic research environment where algebraic techniques directly inform computational solutions. Her work on Laplacian-integral graphs and tropical factorization algorithms exemplifies the laboratory's mission to develop mathematical foundations for next-generation information systems, with recent outputs showing heightened emphasis on algorithmic efficiency for real-world data challenges.

Eugene Demidenkoمشاهده پروفایل

استاد

• Biostatistics
• Statistical Inference
• Data Science
• +۵ مورد دیگر

Eugene Demidenko, PhD, is a Professor with multiple appointments at Dartmouth College, holding positions in Biomedical Data Science, Community and Family Medicine, Mathematics, and Engineering at the Geisel School of Medicine. His academic career spans several decades with significant contributions to statistical methodology and applications. Dr. Demidenko earned his PhD from the Central Economics-Mathematics Institute of Academy of Sciences in 1975 and an MSD from Moscow Pedagogical University in 1971. His educational background laid the foundation for his interdisciplinary approach to statistics and data science. His research focuses on developing exact optimal statistical inference methods for small samples, challenging traditional approaches that rely on asymptotic approximations. Dr. Demidenko's work bridges theoretical statistics with practical applications in biomedical research, epidemiology, and engineering. He has pioneered the M-statistics framework, which combines maximum concentration (MC) and mode (MO) approaches under a single methodological umbrella. His research extends to statistical analysis of images, tumor regrowth modeling, ill-posed inverse problems, and optimal portfolio allocation. Dr. Demidenko's publications demonstrate a consistent focus on improving statistical methodology across diverse fields. His work shows particular strength in developing exact inference procedures that avoid the limitations of traditional methods when sample sizes are small. The progression from his earlier work on mixed models to his recent M-statistics framework reveals an evolving research trajectory focused on addressing fundamental limitations in statistical practice. Ziegel Book Award in Statistics 2022 for "M-statistics: Optimal Statistical Inference for a Small Sample" Ranked among Top 2% World scientists according to Stanford University database Dr. Demidenko teaches a range of courses including QBS 124 (Advanced Biomedical Data Science), QBS 180 (Data Visualization), QBS 177 (Methods of Statistical Learning for Big Data), and mathematics courses on probability and statistical inference. While specific grant information isn't detailed in the provided text, his research output suggests substantial funding support for his methodological developments and applications. His work has significant implications for biomedical research where small sample sizes are common. His laboratory and research team focus on developing and implementing novel statistical methodologies, with a GitHub presence showing active development of R code for statistical methods. This computational approach enables practical implementation of his theoretical advances for researchers across disciplines.