Alon Kipnis

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I am a postdoctoral research scholar in the Statistics department at Stanford University. Previously, I graduated with a PhD in electrical engineering from Stanford and completed an M.Sc in mathematics, a B.Sc in electrical engineering and a B.Sc in Mathematics, all from Ben-Gurion University (BGU). Between 2011-2012 I developed data compression algorithms for flash memory devices at SanDisk. I have spent the summer of 2015 at Huawei working on rateless codes and coding over multiple radio access technologies. My current research combines information theory, data compression, and signal processing with data science and statistics.

My primary research goals are directed toward understanding the effects of bit-level compression on signal processing and data science techniques. These constraints become the limiting factor in many emerging technologies such as self-driving cars, sensor networks, and large-scale information retrieval.

My PhD work provided a unified treatment of sensing and acquiring information under the following three information inhibiting processes: sampling, compression and additive noise. While the effect of each of these processes has been well-understood before, my work shows that the combination of them undermines standard conceptions. For example, I have shown that sampling at a rate smaller than Nyquist is optimal when the bitrate of the digital representation is restricted, and that sampling above the Nyquist rate in most cases reduces performance of signal estimation and other inference procedures.

My current research focuses on techniques for detecting changes and rapidly extracting information in large datasets. Specifically, I develop techniques for distinguishing between distributions over large alphabets; I apply these techniques to analyze large text corpora.

You can read more about my past work and future plans on my Research Statement page.


Room 208 at Sequoia Hall. My email address is alonkipnis at gmail.