معرفی
Shokhrukh Ibragimov is a Researcher at the Department of Mathematics and Computer Science, University of Münster. He is affiliated with the Applied Mathematics Münster: Institute for Analysis and Numerical Analysis. His research focuses on machine learning, particularly neural networks and optimization challenges in high-dimensional spaces. Collaborating with prominent figures like Arnulf Jentzen, he investigates topics such as the curse of dimensionality, gradient descent convergence, and the behavior of local minima in neural network training. His work bridges theoretical analysis and computational methods, contributing to advancements in deep learning and numerical analysis.
Research interests include the mathematical foundations of neural networks, numerical approximations for high-dimensional problems, and the theoretical underpinnings of optimization algorithms in machine learning. His recent publications address critical gaps in understanding neural network limitations and the effectiveness of gradient-based methods.
