Recommended medium-sized books/surveys/tutorials, rooted in engineering mathematics and highly applicable to life sciences problems:

Graph Signal Processing: Overview, Challenges, and Applications [link]
Graph isomorphism in quasipolynomial time [link]
Graph Neural Networks: Architectures, Stability, and Transferability [link]
Convex Optimization [link]
Probabilistic Graphical Models [link]
Bayesian Learning for Neural Networks [link]
A survey on domain adaptation theory: learning bounds and theoretical guarantees [link]
Group Theory in a Nutshell for Physicists [link]
Topology and Geometry [link]
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [link]
Causal Inference in Statistics: A Primer [link]
What are Diffusion Models? [link]
Differential Equations for Engineers [link]
Introduction to Quantum Mechanics [link]
Essentials of Stochastic Processes [link]
Generative modeling via Schrödinger bridge [link]
Non-equilibrium physics: Fokker-Planck equation [link]
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions [link]
Engineering Applications of Noncommutative Harmonic Analysis [link]