This blog will cover theoretical aspects of algorithm design for large data processing. You can subscribe via RSS.
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This Friday we had the official kickoff event for the new Center for Algorithms and Machine Learning here at IU.
The Center for Algorithms and Machine Learning (CAML, http://caml.indiana.edu) at Indiana University Bloomington invites applications for a postdoctoral rese...
I will be talking at a few workshops (mostly on sublinear algorithms and compelxity this year), check them out and consider attending.
A quick announcement of the BeyondMR18 workshop.
Collection of interesting papers on algorithms for big data from 2017.
As a follow-up on the previous post I am describing my setup for video recording screencasts of lectures and talks.
Looking for advice on video recording lectures.
This post links to a spreadsheet that collects information about new jobs in theoretical computer science in 2017.
67th Midwest Theory Day Weekend took place two weeks ago here at Indiana University, Bloomington organized by Qin Zhang, Yuan Zhou and myself.
Collection of interesting papers on algorithms for big data from 2016.
This post discusses some of my recent work on linear compression for binary data.
Discussion of the class on Foundations of Data Science that I am teaching at IU this Fall.
A quick announcement of the ALGO'16 symposium and the ESA'16 conference.
In this blog post I want to suggest a simple reason why you should study your algorithms really well if you want to design algorithms that deal with big data.
In this post I share my experience teaching a class on algorithms for big data at the University of Pennsylvania.
Slides and videos from the DIMACS workshop “Big Data through the Lens of Sublinear Algorithms” are now available.
Two events that might be of interest to the readers of this blog are happening on the East Coast next week.
This is announcement of an upcoming class at Penn which I am going to teach in Fall'15.
Adding my two cents to the discussion of the new format for STOC/FOCS conferences.
As promised in the New Year's post this year there are a lot of activities related to sublinear algorithms and big data.
In this post I offer some suggestions about possible changes in the modern algorithms curriculum.
In this post Sergei Vassilvitskii and I describe the most commonly used models for massively parallel computation and the relationships between them.
In this post I discuss differences between RDBMSs and MapReduce addressing Michael Stonebraker's criticism.
The second “Sublinear Algorithms and Big Data Day” will take place at MIT on April 10.
In this post I share some advice and experience about getting a research internship at industrial research labs.
Clustering is one of the main vechicles of machine learning and data analysis. In this post I will introduce three algorithms for clustering massive data.
This semester I am running a reading group on algorithms for big data at UPenn.
In this post I will introduce a theoretical model for computation in centralized distributed massively parallel computational systems (or in short clusters l...
This blog will cover theoretical aspects of algorithm design for large data processing.