The social sciences are crucial for deciding billions in spending, and yet are often starved for data and badly underserved by modern computational tools. Building data-intensive systems for social science workloads holds the promise of enabling exciting discoveries in both computational and domain-specific fields, while also making an outsized real-world impact.
Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe.
Reasoning with equations is a central part of mathematics. Typically we think of solving equations but another role they play is to define algebraic structures like groups or vector spaces. Equational logic was formalized and developed by Birkhoff in the 1930s and led to a subject called universal algebra. Universal algebra was used in formalizing concepts of data types in computer science. In this talk I will present a quantitative analogue of equational logic: we write expressions like s =_ε t with the intended interpretation "s is within ε of t".