1) discoverer of the Turing machine, which is the basis of both the Church-Turing thesis and modern algorithm design.
Example of a Turing Machine (COURTESY: Wikipedia).
2) characterized Turing (chemical) morphogenesis, which is a leading model for explaining pattern formation in animal development and "spontaneous" pattern generation.
Example of Turing Morphogenesis (LEFT: striping patterns on fish, COURTESY: Wired Science, RIGHT: equations that govern pattern formation, COURTESY: Johannes Wilbert Blog).
This week's issue of Nature (Volume 482, Issue 7386) features a special section on Turing's legacy (see below). There are several interesting articles in this issue contemplating how Turing's work is also relevant to a number of scientific fields. In one article, Sydney Brenner draws parallels between biological cells and Turing machines. In another set of essays, four scientists (including Rodney Brooks) re-assess the brain as a model for machine intelligence. Check it out if you can.
The latest issue of Communications of the ACM also features Turing on the cover (see below), and contains an interesting article by Moshe Vardi on the current state of algorithm design. The major issue highlighted in this article involves the legacy of the Turing machine. While contemporary algorithms are used for diverse purposes, the following question is still outstanding: are algorithms most effective as state machines, or are they recursion engines? While I will not attempt, to answer that question in this post, it is a question worth pondering.