Explores data-driven modeling of haemodynamics in vascular flows, focusing on computational challenges, reduced order modeling, FSI problems, and neural network applications.
Covers wildfire susceptibility mapping using ML-Al robotics and various related topics, including experimental protocols, DFT feature engineering, SimpedCLIP, and Covid-19 detection.
Explores asymmetric cryptography basics, including encryption, signatures, and Diffie-Hellman, along with advanced topics like RSA and quantum computing implications.
Explores the intersection of machine learning and privacy, discussing confidentiality, attacks, differential privacy, and trade-offs in federated learning.
Provides an overview of time-lock encryption and its practical applications, focusing on threshold time-lock encryption algorithms and their security properties.
