This page contains short descriptions of books that have been influential for me. I use Goodreads to keep track of books and find book reviews.

Sapiens, Homo Deus, and 21 lessons for the 21st-century, by Yuval Noah Harari, is three-part book series discussing the past, present, and the future of humankind and society.

The Big Picture, by Sean Caroll, explores the universe, fundamental concepts physics and the origins of life.

The Master Algorithm, by Pedro Domingos, discusses the five tribes of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem, and analogical modeling, and the challenge of unifying them into one master algorithm.


The Map of Mathematics

Logic and Proof, by Jeremy Avigad, Robert Y. Lewis, and Floris van Doorn, covers topics on logic and theorem proving including propositional logic, set theory, relations, functions, and combinatorics. They wrote the book as a companion with the Lean theorem prover.

Contemporary Abstract Algebra, by Joseph Gallian, explains the fundamental algebraic structures; groups, rings, and fields.

Introduction to Linear Algebra, by Gilbert Strang, covers properties of vectors and matrices and operations for solving linear equations.

Calculus, by Robert A. Adams and Christopher Essex, covers the fundamental topics in calculus including limits, differentiation, integration, differential equations, and series.

Nonlinear programming: Theory and Algorithms, by Mokhtar S. Bazaraa, Hanif D. Sherali, and C. M. Shetty, covers convex analysis, optimality conditions, duality, and algorithms for solving unconstrained and constrained nonlinear programming problems with their convergence.

Integer programming, by Laurence A. Wolsey, covers ways to solve optimization problems with discrete or integer variables.

Mosek Modeling Cookbook is a useful reference for formulating optimization models.

The Matrix Cookbook, by Kaare Brandt Petersen and Michael Syskind Pedersen, is a reference for matrix operations.

Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications

NIST Handbook of Mathematical Functions is a reference for mathematical functions.

Computer Science

Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, covers the fundamentals concepts of algorithms and data structures.

Computational Complexity, by Christos Papadimitriou, explains fundamental topics in computational complexity theory such as Turing machines and complexity classes.

Modern Computer Algebra, by Joachim von zur Gathen and Jürgen Gerhard, covers the design of efficient algorithms for operations on polynomials and integers, including multiplication and evaluation, and interpolation.

Modern Computer Arithmetic, by Richard Brent and Paul Zimmermann, covers the design of efficient algorithms for integer, modular, and floating-point arithmetic.

Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, covers basics of machine learning, deep learning, and the concepts in applied mathematics on which they stand.

Software Engineering, by Ian Sommerville, covers different software engineering practices, including software development methods, modeling, design, testing, and management.

Operating Systems, by William Stallings, covers the fundamentals principles of operating systems.

Computer Security, by William Stallings, covers the fundamental principles of computer security technology.

Bitcoin and Cryptocurrency Technologies, by Arvind Narayanan, Joseph Bonneau, Edward W. Felten, Andrew Miller, Steven Goldfeder, and Jeremy Clark

The Wolfram Physics Project: A Project to Find the Fundamental Theory Physics


Corporate Finance, by Jonathan Berk and Peter DeMarzo, covers core concepts for solving quantitative business problems.