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.
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نمایش ۱۶۶-۱۷۲ از ۱۷۲ استاد راهنما
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- Applied Mathematics
- Statistics
- Strategy
- +۷ مورد دیگر
Prof. Hanqin Zhang is a Professor in the Department of Analytics And Operations at the National University of Singapore (NUS Business School). He holds a Doctor of Science from the Chinese Academy of Sciences (1991) and a Master of Science from Xian Jiaotong University (1985). His research focuses on applied mathematics, statistics, strategy, transportation logistics, control engineering, and numerical methods. He has served as an associate editor for journals like European Journal of Operational Research and TOP , and has reviewed for top-tier journals including Operations Research and Queueing Systems . His recent work addresses optimal inventory policies, queueing systems, and stochastic control, with notable contributions to dual-sourcing strategies, staffing optimization in ticket queues, and fluid models of parallel service systems. He has supervised students in stochastic processes and inventory management, reflecting his expertise in bridging theoretical advancements with practical applications in operations research. Notable research trends include leveraging order-tracking information for inventory systems, analyzing ergodicity in Markov chains, and optimizing multi-source supply chains under uncertainty. His publications span high-impact journals such as Operations Research and Queueing Systems , showcasing a focus on stochastic systems and operations management.
Can Deha Karıksızمشاهده پروفایل
استادیار
Can Deha Karıksız is an Assistant Professor of Mathematics at Özyeğin University's Faculty of Engineering. He holds a PhD in Mathematics from Sabancı University (2014), with prior degrees from Sabancı University (2007, MSc) and Middle East Technical University (2005, BSc). His academic journey includes a postdoctoral fellowship at Adam Mickiewicz University (2014-2015) supported by a TÜBİTAK grant and an instructorship at the University of Zurich (2019-2020). Assistant Professor, Özyeğin University (current) Postdoctoral Researcher, Adam Mickiewicz University Instructor, University of Zurich His research spans three primary domains: Applications of functional analysis to complex analysis Dynamics of linear operators Mean-field games and stochastic control Combining mathematical rigor with computational approaches, his work bridges theoretical analysis with practical implementation in multi-agent systems and reinforcement learning frameworks. Recent publications focus on mean-field games (2019-2024), particularly through algorithmic approaches like Q-learning, value iteration, and inverse reinforcement learning. Earlier work (2018) examines linear operator dynamics on real analytic function spaces. Key trends show increasing specialization in stochastic control and game theory applications to machine learning and engineering. Scientific achievements include: TÜBİTAK Postdoctoral Research Fellowship He has taught diverse courses across institutions and levels since 2005, including Linear Algebra, Calculus sequences for engineering/social sciences, Differential Equations, and graduate-level Numerical Linear Algebra and Analysis II. His teaching portfolio demonstrates expertise in both foundational mathematics and advanced computational methods.
CNV Krishnan is a Professor in the Banking and Finance Department at the Weatherhead School of Management, Case Western Reserve University. He holds the Henry Payne McIntosh Fellowship and has served as Department Chair and Finance Programs Director since 2001. His expertise lies in empirical corporate finance, focusing on how financial intermediaries create or destroy value. Education: PhD (University of Wisconsin-Madison, 2001), MBA (Indian Institute of Management-Ahmedabad) Research Interests : His work bridges corporate finance and law, analyzing banks, regulators, courts, and investment advisors. He has over 25 publications in top journals like the Journal of Finance, with research cited by the U.S. SEC and discussed in the Wall Street Journal and Reuters.com. Teaching & Awards : Krishnan has been recognized for teaching excellence at Weatherhead School of Management and through the Wolstein Society's 'Amazing Mentor and Guide Award'. He trains master's students in academic research and has been nominated for teaching awards repeatedly (2004, 2007, 2009, 2010, 2011, 2013, 2015, 2024). He is a top 10% author on Social Science Research Network (SSRN) by downloads. 2004-2024: Teaching Excellence Award Nominations 2024: Case Western Reserve University Pedagogical Innovation Award 2017-2023: Top 10% SSRN Author by Downloads
Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.
Lyonell Boulton is a Professor at the School of Mathematical and Computer Sciences, Heriot-Watt University, specializing in the spectral theory of linear operators and its applications to mathematical physics and functional analysis. His work bridges theoretical and applied mathematics, with recent contributions to eigenvalue approximation, non-self-adjoint operators, and pseudospectral theory. Research Interests: Spectral theory of self-adjoint and non-self-adjoint operators Applications to mathematical physics (e.g., supermembranes, Schrödinger's equation) Dispersive partial differential equations Numerical linear algebra and eigenvalue computations Recent Publications highlight trends in spectral analysis of Dirac operators, revival effects in dispersive PDEs, and fractal solutions to periodic equations, reflecting his interdisciplinary focus.
- 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.