Slides and videos from the DIMACS workshop “Big Data through the Lens of Sublinear Algorithms” are now available (link). In case you missed it, this was a great opportunity to catch up on the latest and hottest results in the field. We were lucky to have a healthy mix of speakers from both academia and industry (represented by researchers from Microsoft, IBM, Google and Yahoo!). I was particularly excited to see talks on both traditional models for sublinear computation (streaming, property testing, etc.) as well as more recent ones (here my own favorites are MapReduce and other modern distributed models).
All keynotes, tutorials and regular talks were great. Among regular talks let me highlight two that were in some ways outliers:
- Vahab Mirrokni talked about problems and frameworks for large-scale data mining at Google Research NYC (video). I really wish this could be a longer talk.
- Jelani Nelson from Harvard gave a quick tutorial on chaining (video). From this tutorial you can also learn about applications of chaining to instance-dependent Johnson-Lindenstrauss dimensionality reduction using Gaussian mean width which I didn't know and found really cool. Jelani is organizing a workshop on related topics at Harvard that will take place on Jun 22–23 (after STOC).
Kicking off 2016 is another sublinear algorithms workshop at Johns Hopkins University (Jan 7–9, right before SODA in Arlington, VA).