To highlight the Year of Open Science, we spoke to acquisitions editor Jermey Matthews about what open access means for the field of physical sciences
The Biden-Harris administration declared 2023 the Year of Open Science in the United States, offering an opportunity to advance national open science policy and provide greater and more equitable access to research in key areas of scientific study.
The MIT Press centers open access in much of the work we do; we take pride in making high quality, well-researched scholarship freely available to the public. In honor of the Year of Open Science, we spoke to Jermey Matthews, senior acquisitions editor of physical sciences, engineering, and mathematics, about the impact OA scholarship has had in his fields.
“The physical sciences, engineering, and math communities have long been pioneers in the open access movement, alongside the computer science field,” Matthews said. “They all recognize that access to knowledge improves the quality and pace of innovation. In particular, physicists and mathematicians often approach me to make their books open access, and I’m proud that the MIT Press is leading the way in making that possible.”
Read on to explore several books from Jermey’s list, and discover even more physical science titles on our website.
Topology: A Categorical Approach by Tai-Danae Bradley, Tyler Bryson and John Terilla
This graduate-level textbook on topology takes a unique approach: it reintroduces basic, point-set topology from a more modern, categorical perspective. Many graduate students are familiar with the ideas of point-set topology and they are ready to learn something new about them. Teaching the subject using category theory—a contemporary branch of mathematics that provides a way to represent abstract concepts—both deepens students’ understanding of elementary topology and lays a solid foundation for future work in advanced topics.
Holographic Quantum Matter by Sean A. Hartnoll, Andrew Lucas and Subir Sachdev
This book, written by pioneers in the field, offers a comprehensive overview of holographic methods in quantum matter. It covers influential developments in theoretical physics, making the key concepts accessible to researchers and students in both high energy and condensed matter physics. The book provides a unique combination of theoretical and historical context, technical results, extensive references to the literature, and exercises. It will give readers the ability to understand the important problems in the field, both those that have been solved and those that remain unsolved, and will enable them to engage directly with the current literature.
Computational Imaging by Ayush Bhandari, Achuta Kadambi and Ramesh Raskar
Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques.
Sheaf Theory through Examples by Daniel Rosiak
Sheaves are mathematical constructions concerned with passages from local properties to global ones. They have played a fundamental role in the development of many areas of modern mathematics, yet the broad conceptual power of sheaf theory and its wide applicability to areas beyond pure math have only recently begun to be appreciated. Taking an applied category theory perspective, Sheaf Theory through Examples provides an approachable introduction to elementary sheaf theory and examines applications including n-colorings of graphs, satellite data, chess problems, Bayesian networks, self-similar groups, musical performance, complexes, and much more.