جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Probabilistic Numerics
• Bayesian Inference
• +۵ مورد دیگر

Philipp Hennig is a Full Professor (W3) at the Department of Computer Science, Eberhard Karls University Tübingen, heading the Chair for Methods of Machine Learning. He previously held positions at the Max Planck Institute for Intelligent Systems, including Emmy Noether Group Leader and Max-Planck-Group Leader. His research focuses on probabilistic numerics, Bayesian inference, and uncertainty quantification, with contributions to machine learning theory and computational methods. He co-founded the field of probabilistic numerics and authored the textbook Probabilistic Numerics — Computation as Machine Learning (2022). Education: PhD in Physics from the University of Cambridge (2007), MSci in Physics from Heidelberg University (2007). Research support includes DFG Emmy Noether Programme grants, ERC Starting/Consolidator Grants, and Max Planck Society funding. He leads the ELLIS Program on Theory, Algorithms, and Computations of Modern Learning Systems and serves as Dean of Studies for Tübingen's Computer Science Department. Key research directions include probabilistic ODE/PDE solvers, Bayesian deep learning, and uncertainty-aware algorithms. His work bridges computational methods with statistical inference, emphasizing scalable and theoretically grounded approaches. Recent projects address neural operator learning, uncertainty quantification in climate models, and efficient Gaussian process methods. Awards include ELLIS Fellowship and ERC grants. Over 100 publications span top venues like NeurIPS, ICML, and JMLR. Active in academic leadership roles, he co-leads the Tübingen AI Center and Cluster of Excellence for Machine Learning in Science. His lab develops open-source tools like ProbNum for probabilistic numerical computing.

Danny Arrigoمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Reaction-Diffusion Systems
• Nonlinear Elasticity
• +۳ مورد دیگر

Danny Arrigo is a Professor in the Department of Mathematics at the University of Central Arkansas (UCA). He holds a PhD in Mathematics from Georgia Institute of Technology (1991). His research focuses on nonlinear partial differential equations (PDEs), particularly in reaction-diffusion systems and nonlinear elasticity, with applications to engineering and science. He explores exact solutions to PDEs using symmetry analysis and analytical techniques. Arrigo's recent work includes studies on Varga material deformations, hyperelastic compressible materials, and linearization techniques for complex systems. His publications span topics from symmetry classification and variable separation methods to Fourier analysis and contact transformations. He has contributed extensively to the theoretical underpinnings of nonlinear PDEs through over 70 peer-reviewed articles. No scientific awards are explicitly listed in the provided materials. His advising and grant activities are not detailed here, but his research emphasizes collaborations in applied mathematics and materials science. He maintains an active presence in academic outreach through UCA's Mathematics programs.

Selim Esedogluمشاهده پروفایل

استاد

• Applied Mathematics
• Scientific Computing
• Image Processing
• +۶ مورد دیگر

Selim Esedoglu is a Professor of Mathematics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). He holds a Ph.D. from the Courant Institute (2000) and an M.S. from the same institution (1998). His research focuses on mathematical problems in image processing, computer vision, and materials science, employing methods from calculus of variations and partial differential equations. Key areas include geometric motion of interfaces, grain growth simulations, and algorithm development for scientific computing. His work emphasizes numerical methods for evolving networks via mean curvature motion and anisotropic surface energies. Education: M.S., Courant Institute, 1998 Ph.D., Courant Institute, 2000 Research Interests: Esedoglu's research spans applied mathematics and computational science, with emphasis on: Geometric partial differential equations Algorithms for interface motion (e.g., motion by mean curvature) Grain growth dynamics in polycrystalline materials Image processing techniques (segmentation, denoising, inpainting) Numerical methods for gradient flows and phase field models Grants & Awards: His research has been supported by NSF grants (DMS-0410085, DMS-0713767, DMS-0748333) and an Alfred P. Sloan Fellowship. Projects include collaborations with Los Alamos National Laboratory on materials science simulations. Current Projects: Development of high-order schemes for gradient flows and interfacial motion Algorithms for grain boundary motion in 3D polycrystals Analysis of coarsening rates in ill-posed diffusion equations Labs/Teams: Collaborates with interdisciplinary teams in materials science and computational mathematics, including researchers at the University of Michigan and international institutions.

Constantine Caramanisمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• High-Dimensional Statistics
• +۲ مورد دیگر

Constantine Caramanis is a Professor and holder of the Chandra Family Endowed Distinguished Professorship in Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on decision-making in large-scale complex systems, specializing in robust optimization, high-dimensional statistics, machine learning, and applications to networks including social, wireless, transportation, and energy systems. Caramanis directs a substantial research group and is affiliated with the NSF Institute for Foundations of Machine Learning and the UT Machine Learning Lab. He has made significant contributions to optimization theory, developing efficient algorithms for matrix sensing, non-convex problems, and combinatorial optimization. His recent work bridges machine learning and control theory through latent MDPs, diffusion models for inverse problems, and bandit algorithms with fairness considerations. Caramanis has received prestigious recognition including the NSF CAREER award and IEEE Fellowship for his theoretical advances in optimization and learning.

• Information Theory
• Signal Processing
• High-Dimensional Statistics
• +۳ مورد دیگر

Elad Romanov is a postdoctoral researcher in the Department of Statistics at Stanford University, hosted by David Donoho. He completed his PhD at the Hebrew University of Jerusalem's School of Computer Science and Engineering, advised by Or Ordentlich and Matan Gavish. His research focuses on information theory, signal processing, high-dimensional statistics, and mathematical foundations of data science. Key contributions include ScreeNOT—a method for optimal singular value thresholding in correlated noise—and work on Gaussian mixture models, modulo-reduced measurements, and matrix denoising. Education: PhD in Computer Science (Hebrew University of Jerusalem, advised by Or Ordentlich and Matan Gavish), followed by postdoctoral research at Stanford under David Donoho. His work bridges statistical theory and practical algorithms, with applications in optimization, matrix recovery, and information-theoretic limits. Research interests emphasize statistical learning theory, robust estimation techniques, and algorithmic methods for high-dimensional data. His publications span topics like phase transitions in PCA, optimal spectral shrinkage, and the interplay between channel capacity and learning Gaussian mixtures. The ScreeNOT algorithm, implemented in multiple programming languages, provides a mathematically grounded alternative to heuristic singular value thresholding. Collaborative projects include work on multi-reference alignment and matrix denoising with partial noise statistics. His contributions to modulo-reduced signal processing demonstrate recovery guarantees under non-traditional measurement constraints.

Andre Milzarekمشاهده پروفایل

استادیار

• Nonsmooth Optimization
• Large-Scale Optimization
• Stochastic Optimization
• +۳ مورد دیگر

Andre Milzarek is an Assistant Professor and Undergraduate Program Director for the DSBDT Major at the School of Data Science, CUHK-Shenzhen. Previously, he served as a postdoctoral researcher at the Beijing International Center for Mathematical Research (Peking University). He holds a Ph.D. (2016), M.S. (2013), and B.S. (2010) in Mathematics from Technical University of Munich. His research focuses on nonsmooth optimization, large-scale and stochastic optimization, and second-order methods. He has delivered talks at conferences like the AISM Applied Linear Algebra and International Symposium on Mathematical Programming. Education: Ph.D. Mathematics, Technical University of Munich (2016) M.S. Mathematics, Technical University of Munich (2013) B.S. Mathematics, Technical University of Munich (2010) Research Interests: Dr. Milzarek’s work emphasizes nonsmooth optimization techniques, stochastic optimization frameworks, and the theoretical underpinnings of second-order methods. His contributions bridge computational efficiency with mathematical rigor, addressing challenges in electronic structure calculations and large-scale systems. Awards: Boya Postdoctoral Fellowship (Peking University, 2017) Max-Weber Program Scholar (State of Bavaria, 2010–2012) Advising and Grants: While no formal advisees are listed, his research has been supported through collaborative efforts with institutions like the Beijing International Center for Mathematical Research. His work on optimization algorithms has led to impactful peer-reviewed publications and international conference engagements.

• Mathematical Physics
• Continuum Mechanics
• Financial Mathematics
• +۳ مورد دیگر

Gisèle Goldstein is a Professor in the Department of Mathematical Sciences at The University of Memphis. Her research focuses on mathematical physics, continuum mechanics, financial mathematics, quantum mechanical density functional theory, applied analysis, and partial differential equations. She has contributed to the study of boundary value problems, operator theory, and semigroups of operators, with a particular emphasis on Wentzell boundary conditions and their applications in various physical and financial models. Her work spans theoretical and applied mathematics, addressing complex equations such as the Black-Scholes model in finance, diffusion processes on Riemannian manifolds, and nonlinear parabolic equations. Goldstein has extensively explored the interplay between mathematical analysis and real-world phenomena, including heat equations, wave equations, and stochastic processes. Her research also delves into foundational topics like Hardy inequalities and spectral theory. Goldstein's contributions to operator semigroups and their applications have advanced understanding in areas such as maximal regularity, analytic semigroups, and ergodic limits. Her interdisciplinary approach bridges mathematical rigor with practical applications in financial modeling, quantum mechanics, and continuum mechanics.

Lampros Gavalakisمشاهده پروفایل

پژوهشگر

• Information Theory
• Probability
• Mathematical Statistics
• +۷ مورد دیگر

Lampros Gavalakis is a Postdoctoral Research Associate at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, affiliated with the INFORMED AI Hub. Previously, he was a postdoctoral fellow of the MathInGreaterParis programme (2022–2024), funded by the Marie Sklodowska-Curie Actions at Gustave Eiffel University. He holds a Ph.D. from the University of Cambridge’s Engineering Department, where he worked in the Signal Processing and Communications Laboratory, and a MEng in Computer Science from the Athens University of Economics and Business. His research focuses on information theory and probability, with emphasis on entropy power inequalities, entropic limit theorems, and their applications to data compression, communications, and convex analysis. Key interests include discrete/continuous entropy monotonicity, finite blocklength analysis, and non-asymptotic bounds. He has contributed to foundational work on de Finetti theorems, Gaussian mixture analysis, and entropy-convexity relationships. Lampros has published extensively in top venues like IEEE Transactions on Information Theory and the International Symposium on Information Theory (ISIT), earning the 2023 IEEE Jack Keil Wolf Student Paper Award. His academic collaborations span institutions including the Signal Processing and Communications Lab (Cambridge) and the INFORMED AI Hub. Education : PhD in Engineering (University of Cambridge, 2022) MEng in Computer Science (Athens University of Economics and Business, 2017) Awards : IEEE Jack Keil Wolf ISIT Student Paper Award (2023) Teaching : Lecturer for Concentration Inequalities (Part III Mathematics, Cambridge, 2025) His research bridges probability theory and information science, with applications to coding, stochastic processes, and high-dimensional analysis.

Jelena Diakonikolasمشاهده پروفایل

استادیار

• Large-Scale Optimization
• Machine Learning
• Networked Systems
• +۳ مورد دیگر

Jelena Diakonikolas is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, within the School of Computer, Data & Information Sciences. Her research focuses on large-scale optimization with applications to machine learning and networked systems. She explores algorithm design, convergence analysis, and robust learning techniques, with contributions to optimization theory and practical implementations in wireless networks and distributed systems. Her work spans theoretical advancements, such as analyzing convergence properties of incremental methods and variance-reduced algorithms, alongside applied research in robust learning, adversarial noise mitigation, and resource allocation in wireless networks. Notable areas include fixed-point equations, primal-dual methods, and block-coordinate optimization techniques. She has authored numerous papers on topics like stochastic gradient descent, variational inequalities, and distributionally robust optimization. Dr. Diakonikolas' research also intersects with wireless communication systems, including full-duplex networking and integrated circuit design. Her contributions address challenges in full-duplex systems, such as interference cancellation and resource allocation. She has explored fairness and delay in heterogeneous networks, as well as energy harvesting in wireless networks.

• Teaching English to Speakers of Other Languages (TESOL)
• Language Testing and Assessment
• Second Language Acquisition
• +۲ مورد دیگر

Professor Aek Phakiti is affiliated with the Sydney School of Education and Social Work at The University of Sydney , where he serves as a leading scholar in language testing and assessment and second language acquisition . His research emphasizes strategic competence , metacognition , and the application of generative artificial intelligence (AI) in education . Phakiti also acts as Editor-in-Chief for the University of Sydney Journal of TESOL and holds editorial roles for multiple international journals. Phakiti earned his PhD in Applied Linguistics from the University of Melbourne in 2005, focusing on cognitive, metacognitive, and affective strategies in EFL reading. His work has been recognized with awards such as the TOEFL Outstanding Scholar Award (2010), FASS Excellence in Teaching Awards (2010, 2017, 2019), and the SUPRA Supervisor of the Year Award (2024). His research explores the intersection of language assessment and educational technology , including automated feedback systems. Key themes include appraisal calibration , state-trait strategy use , and linking language proficiency to academic achievement . Phakiti’s recent publications span quantitative methods , AI-driven assessment tools , and curriculum design . Scientific Awards TOEFL Outstanding Scholar Award (2010) FASS Excellence in Teaching Awards (2010, 2017, 2019) FASS Research Mentor Award (2023) SUPRA Supervisor of the Year Award (2024) Teaching and Supervision Phakiti coordinates the Master of Education (TESOL) program, teaching units in second language acquisition, language testing, and research methods. He supervises PhD and master’s students in TESOL and applied linguistics, fostering their understanding of cognitive processes and assessment design.

Michał Derezińskiمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Randomized Numerical Linear Algebra (RandNLA)
• Stochastic Optimization
• +۳ مورد دیگر

Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Previously, he held postdoctoral positions at UC Berkeley (Department of Statistics) and the Simons Institute for the Theory of Computing, and completed his Ph.D. in Computer Science at UC Santa Cruz under Manfred Warmuth, alongside Master's degrees in Mathematics and Computer Science from the University of Warsaw. He teaches courses such as Foundations of Computer Science (EECS 376), Algorithms for Data Science (EECS 474), and specialized RandNLA courses. His work emphasizes bridging theory and practice in RandNLA, with applications to ML and optimization. Key achievements include an NSF CAREER Award and a NeurIPS 2020 Best Paper Award for work on column subset selection. Current advisees include Sachin Garg, Jiaming Yang, and Shabarish Chenakkod. His research explores topics like sketching algorithms, stochastic optimization, and random matrix theory, with publications in top venues such as NeurIPS, COLT, and SIAM journals. He actively contributes to the RandNLA community through tutorials and software development efforts like RandBLAS/RandLAPACK initiatives.

Madeleine Udellمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Madeleine Udell is an Assistant Professor of Management Science and Engineering at Stanford University, with affiliations at the Institute for Computational and Mathematical Engineering (ICME) and a courtesy appointment in Electrical Engineering. She holds a PhD from Stanford University in Computational and Mathematical Engineering and completed a postdoc at Caltech. Her research focuses on accelerating optimization and machine learning, with applications in healthcare, finance, and engineering. Udell's work bridges algorithmic efficiency, interpretability, and practical deployment in data-driven systems. Education: BS in Mathematics and Physics, Yale University (2009) PhD in Computational and Mathematical Engineering, Stanford University (2015) Research Interests: Her research emphasizes optimization algorithms, scalable machine learning techniques, and their applications in real-world domains. Key areas include low-rank matrix methods, automated machine learning (AutoML), and interpretable models for healthcare. She develops tools like OptiMUS for natural language-based optimization modeling and explores second-order methods for improving deep learning efficiency. Awards: Kavli Fellowship (2023) Alfred P. Sloan Research Fellowship (2021) NSF CAREER Award (2020) ONR Young Investigator Award (2020) Grants & Advising: Udell leads projects funded by ONR and NSF, focusing on randomized numerical linear algebra and automated machine learning. She advises PhD students in optimization modeling, deep learning, and causal inference. Her group collaborates on tools like PINNs for scientific computing and OpenReviewer for bias-aware academic reviews. Labs & Teams: She directs the Udell Research Group at Stanford, emphasizing interdisciplinary work in optimization, machine learning, and data science. Collaborations span academia and industry, including work with Aurora Solar on photovoltaic array design and Google Brain on scalable AutoML.

Julian Bradfieldمشاهده پروفایل

مدرس ارشد

• Concurrency
• Modal Logic
• Temporal Logic
• +۶ مورد دیگر

Julian Bradfield is a Reader in Computer Science in the School of Informatics at the University of Edinburgh. He serves as Director of the Laboratory for Foundations of Computer Science (LFCS) and has been on the teaching staff since 1992. His academic journey began with a Mathematics degree from Cambridge (B.A. 1985, M.A. 1988), followed by a Ph.D. in Computer Science from the University of Edinburgh (awarded 1991). His educational background includes: B.A. in Mathematics from Cambridge (1985) M.A. in Mathematics from Cambridge (1988) Ph.D. in Computer Science from the University of Edinburgh (1991) Bradfield's research has evolved significantly over his career. Initially focused on computer science topics like fixpoint logics and concurrency, his interests have increasingly turned to linguistics, particularly phonology, since the mid-2000s. He has made notable contributions to the study of the Khoisan language !Xoon, applying his expertise in concurrency to linguistic analysis. His interdisciplinary approach bridges computer science, logic, and linguistics, creating innovative frameworks for understanding both computational systems and natural language phenomena. His recent publications show a clear shift toward linguistic research, particularly in phonology, while maintaining connections to his earlier work in logic and concurrency. The articles demonstrate a progression from theoretical computer science to applied linguistic analysis, with several papers focusing on the phonological structures of Khoisan languages, especially !Xoon. His work often combines formal logical frameworks with empirical linguistic data, creating bridges between disciplines that are typically separate. Bradfield actively supervises Ph.D. students and has contributed to various research projects throughout his career. He has developed connections between logical frameworks and linguistic analysis, particularly in applying concepts from concurrency theory to phonological processes. His role as Director of LFCS indicates significant administrative responsibilities alongside his research and teaching duties. He teaches "Informatics 1 - Introduction to Computation" and has developed materials for courses on Formal and Computational Approaches to Phonology. His teaching reflects his interdisciplinary expertise, connecting foundational computer science concepts with applications in linguistics.

• Machine Learning
• Computational Neuroscience
• Natural Language Processing
• +۳ مورد دیگر

Michael Hahn is a Tenure-Track Professor (W2) at Saarland University's Saarland Informatics Campus, directing the Language, Computation, and Cognition Lab (LaCoCo). He holds dual affiliations with the Department of Language Science and Technology and the Department of Computer Science. Hahn earned his PhD from Stanford University in 2022, advised by Judith Degen and Dan Jurafsky. Education: PhD in Computational Linguistics, Stanford University (2022) Research Interests: His work bridges machine learning and cognitive science, focusing on information processing in machines and the brain. Key areas include understanding transformer architectures, computational cognition, and the theoretical foundations of neural networks. He investigates how models like GPT-2 simulate human cognitive limitations, explores biases in perception, and develops methods for mechanistic interpretability. Recent Achievements: Best Paper Award at ACL 2024 for 'Why are Sensitive Functions Hard for Transformers?' Outstanding Area Chair at EMNLP 2024 Recipient of the Second Prize at ICML 2024's Mechanistic Interpretability Workshop Lab & Collaborations: The LaCoCo Lab focuses on advancing theoretical and applied research in NLP and machine learning, with recent work on chain-of-thought reasoning limits, state space models, and evolutionary pressures in word order.

• Art Education
• Art Pedagogy
• Artistic Practice
• +۳ مورد دیگر

Stefan Hölscher serves as Vice Rector for Studies and Teaching at the Münster Academy of Fine Arts since 2021. He has been a scientific staff member for art education at the academy since 2003, focusing on teacher training reforms and practical phases coordination. Previously, he led Bologna Reform implementation for art academies in Düsseldorf and Münster (2010–2015). His academic roles include trusted lecturer (2016–2020) and committee memberships in national art education initiatives. He holds a Diplom in Mathematics (Ruhr-Universität Bochum, 1996), First State Exam in Secondary Education (Arts and Math, Westfälische Wilhelms-Universität, 2002), and studies in painting and graphics at his current institution. His research explores art pedagogy, image didactics, and learning through artistic practice, integrating pragmatist theory and self-organizing processes. Key research areas include: art didactics frameworks, dynamics of artistic practice, interdisciplinary teaching (e.g., Open Frame ), and educational policy analysis. His publications span art education strategies, public art dialogues, and critical analyses of contemporary art. Awards include the Gotland Scholarship (2002) and German Academic Scholarship Foundation support (1985–1990). He advises on teacher training programs, coordinates practical education phases, and collaborates with institutions like the University of Münster’s teacher training center. His work bridges artistic practice and educational theory, emphasizing reflexive teaching and second-order praxis. He actively contributes to national committees shaping art education policy and curricula.