We, along with our friends at Foundation Capital, are pleased to announce a 1 day mini-conference on streaming and sketching algorithms in Big Data. We have gathered an amazing group of speakers from academia and industry to give talks. If you are a reader of this blog we would love to have you come! The conference will be on 6/20 (Thursday) from 10 AM to 5:30 PM at the 111 Minna Gallery in San Francisco and attendance is limited. Use this Eventbrite link to reserve your spot. Breakfast and lunch included!
There will also be a happy hour afterwards if you cannot make the conference or just want a beer afterwards. Here is the (free) reservation link.
The speaker list includes:
The Count-Min Sketch, 10 Years Later
The Count-Min Sketch is a data structure for indexing data streams in very small space. In a decade since its introduction, it has found many uses in theory and practice, with data streaming systems and beyond. This talk will survey the developments.
Muthu Muthukrishnan is a Professor at Rutgers University and a Research Scientist at Microsoft, India. His research interest is in development of data stream theory and systems, as well as online advertising systems.
David P. Woodruff
Sketching as a Tool for Numerical Linear Algebra
The talk will focus on how sketching techniques from the data stream literature can be used to speed up well-studied algorithms for problems occurring in numerical linear algebra, such as least squares regression and approximate singular value decomposition. It will also discuss how they can be used to achieve very efficient algorithms for variants of these problems, such as robust regression.
David Woodruff joined the algorithms and complexity group at IBM Almaden in 2007 after completing his Ph.D. at MIT in theoretical computer science. His interests are in compressed sensing, communication, numerical linear algebra, sketching, and streaming.
Summingbird: Streaming Map/Reduce at Twitter
Summingbird is a platform for streaming map/reduce used at Twitter to build aggregations in real-time or on hadoop. When the programmer describes her job, that job can be run without change on Storm or Hadoop. Additionally, summingbird can manage merging realtime/online computations with offline batches so that small errors in real-time do not accumulate. Put another way, summingbird gives eventual consistency in a manner that is easy for the programmer to reason about.
Sam Ritchie works on data analysis and infrastructure problems in Twitter’s Revenue engineering team. He is co-author of a number of open-source Scala and Clojure libraries, including Bijection, Algebird, Cascalog 2 and ElephantDB. He holds a bachelor’s degree in mechanical and aerospace engineering.
Similarity Search Algorithms
Nearest Neighbor Search is an ubiquitous problem in analyzing massive datasets: its goal is to process a set of objects (such as images), so that later, one can find the object most similar to a given query object. I will survey the state-of-the-art for this problem, starting from the (Kanellakis-award winning) technique of Locality Sensitive Hashing, to its more modern relatives, and touch upon connection to the theory of sketching.
Alexandr Andoni is a researcher in the Microsoft Research at Silicon Valley since 2010, after finishing his PhD in MIT’s theory group and year-long postdoctoral position at Princeton University. His research interests revolve around algorithms for massive datasets, including similarity search and streaming/sublinear algorithms, as well as theoretical machine learning.