Answer by Greg Price:
Well, let’s see.
- Bloom filters are a clever technique that gives practical solutions for a wide variety of problems. The basic idea was first invented in 1970 and saw practical application throughout the decades since then, but theorists picked it up again around 2000 and have devised a long line of variations that have made Bloom filters useful (and in fact used) for many more diverse tasks. See  for a survey.
- Streaming algorithms is an area of CS theory that has produced a large number of novel algorithms that make analysis of large datasets tractable. The name was coined about 15 years ago in a paper of Alon, Matias, and Szegedy; much of the work was done in the past decade. Network routers and other devices processing giant streams of data use these algorithms by necessity. See the Wikipedia article  for an overview and many links.
- Lattice-based cryptography and post-quantum cryptography generally — this is an area whose major real-world impact has not yet happened, but it’s unusual in that we can be pretty confident it will happen. Quantum computers are coming within the next few decades. When they do, they will make it easy to factor numbers and to compute discrete logarithms, destroying the security of many of the most widely-used cryptosystems that protect the Internet. The last decade has seen a great deal of work on other families of cryptosystems, with a focus on bringing them all the way to practice so that as quantum computing develops we’ll be ready to use them. See http://pqcrypto.org/ and in particular . As a bonus, some of these cryptosystems have practical application today, even without the threat of quantum computers.
- Distributed hash tables, as Nelson pointed out, are another good example. Rather than duplicate his description, I’ll just add a pair of links: the Wikipedia article  for lack of a better survey, and one of the early papers .
These are just some examples that I happen to know about because of the people I know and the things I was interested in when I was in academia; someone who’s really followed the field well could doubtless produce a much longer list.
So, turning to your follow-up questions, does theoretical computer science matter? Yes, some of it definitely does. Some machine learning research matters too.
Should you go into it as a career? If you’re interested in practical impact, probably not. The incentives in academia don’t reward practical impact, and you have to constantly move against the grain if you want to work toward such impact. I’ve observed this not only in theory but in systems, where grad students do things like write operating systems from scratch in C and assembly, and which might seem as far from theory as it’s possible to get. So I doubt it’s any different in machine learning either.
Instead, if your goal is to have a big impact, you should probably just head for Silicon Valley. Here it’s all about the practical impact, there are plenty of people ambitious to change an industry or the world, and some of them do.
 Andrei Broder and Michael Mitzenmacher. “Network Applications of Bloom Filters: A Survey” (2005). http://www.eecs.harvard.edu/~michaelm/postscripts/im2005b.pdf
 D. J. Bernstein. “Introduction to post-quantum cryptography” (2009). http://pqcrypto.org/www.springer.com/cda/content/document/cda_downloaddocument/9783540887010-c1.pdf
 Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan. “Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications” (2001). http://pdos.csail.mit.edu/papers/chord:sigcomm01/