Explores data-driven modeling of haemodynamics in vascular flows, focusing on computational challenges, reduced order modeling, FSI problems, and neural network applications.
Explores the trends and challenges in modeling complex molecular systems using hierarchical multi-scale approaches, covering length-time scales, atomistic simulations, and force matching techniques.
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
Delves into the mathematical foundations and importance of directional cues in image processing, exploring computational challenges and selectivity to orientation.
Explores the symbiotic relationship between neuroscience and information technology, emphasizing the need for new computational paradigms and innovative applications.
