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Explores advanced applications of dual chains in random walks, focusing on less symmetric scenarios and statistical applications, with examples from hyperplane arrangements and non-uniform duals.
Explore applications of dual processes in Markov chains, including strong stationary times, eigenvalue interpretation, and solutions to the Peres conjecture for Birth and Death chains.
Explore PDEs for neural assemblies, analyzing their behavior through simulations and mathematical techniques with Professor Benoît Perthame from Sorbonne University.
Explore innovative swarm-based gradient descent techniques for solving non-convex optimization problems, presented by Prof. Eitan Tadmor from the University of Maryland.
Explore advanced concepts in supersymmetric gauge theories, focusing on Coulomb branches and bow varieties in 3-dimensional N=4 models with Hiraku Nakajima.
Explore orthosymplectic quiver gauge theories, focusing on Coulomb branches, Higgs branches, and integrable systems with expert Hiraku Nakajima.
Explore unipotent character sheaves and strata of reductive groups with George Lusztig, delving into advanced mathematical concepts and their applications.
Explore Coulomb branches in 3d N=4 supersymmetric gauge theories and their connection to bow varieties, covering key concepts, technical points, and applications in integrable systems.
Explore semisimple groups and total positivity theory with MIT's George Lusztig, delving into advanced mathematical concepts and their applications in group theory.
Explore advanced techniques in convex optimization, focusing on higher-order methods and their applications in mathematical problem-solving and algorithm development.
Explore polynomial-time interior-point methods and set-limited functions with Yurri Nesterov, delving into advanced optimization techniques and mathematical theory.
Explore nonconvex stochastic programs and chance constraints with Jong-Shi Pang, delving into advanced mathematical concepts for optimization under uncertainty.
Explore nonconvex stochastic programs with deterministic constraints, presented by Jong-Shi Pang from USC. Gain insights into advanced mathematical concepts and their applications.
Explore primal-dual optimization techniques for enhancing machine learning robustness, focusing on advanced mathematical approaches to improve model performance and reliability.
Explore optimization techniques and their real-world applications with insights from Stephen Wright of University of Wisconsin-Madison, blending theoretical foundations and practical implementations.
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