Mathematical Modeling on the JVM

As programmers, we love solving problems. But sometimes we need more than programmer grit to solve many problems with no easy answer. Suppose you need to tightly schedule 190 classes in 20 classrooms, with different class durations, recurrences, and constraints throughout the week? What about minimizing the operating cost of a train schedule while maintaining a steady movement of passengers? How about anticipating an industry competitor's move? Or simply solving a Sudoku?

Mathematical modeling is the workhorse of data science, machine learning, and operations research. By effectively expressing mathematical concepts in code, you can gracefully find solutions to a broad category of problems and avoid impractical brute-force techniques.

Many practical approaches and libraries on the JVM can rise to solve this exciting problem space. Come to this session to see live examples of JVM mathematical models (using the Kotlin language) to solve real-world problems like discrete optimization, Bayesian techniques, and artificial neural networks.

Thomas Nield (author of Getting Started with SQL and Learning RxJava) is a business consultant for Southwest Airlines. Early in his career, he became fascinated with technology and its role in business analytics. After becoming proficient in Java, Kotlin, Python, SQL, and reactive programming, he became an open-source contributor as well as an author/trainer for O’Reilly Media. He is passionate about sharing what he learns and enabling others with new skill sets. He enjoys making technical content relatable and relevant to those unfamiliar with or intimidated by it.

Currently, Thomas is interested in data science, reactive programming, and the Kotlin language. You may find him speaking on these three subjects and how they can interconnect.

• https://twitter.com/thomasnield9727
• https://github.com/thomasnield/
• http://tomstechnicalblog.blogspot.com/