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we're here today with Wharton statistics Professor bashor bataria to talk about
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some of his latest research bashor thanks for being with us today could you first of all talk to us a little bit
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about give us a brief summary of your research what kind of question you were trying to answer right so my research
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interests are the intersection of Statistics probability and combinatorics so recently numerous very interesting
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combinatorial and graph theoretic problems of emerging statistics mainly because of the ubiquitous presence of
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network data and the increasing use of graph based methods in modern analytics and as a consequence many interesting
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connections have emerged between modern statistical methods and classical Concepts in geometry and probability and
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you know as a consequence you can use them to solve interesting problems in statistics great and tell us a little
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bit about in this study what were the key takeaways that you took away that you took away from the study the key
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takeaways of my research basically the interplay between computational complexity which is the time it takes to
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to implement one of these methods and its statistical performance perance which is how close it is to the
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mathematically based procedure it turns out that many of these graph based methods are computationally very
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efficient so they can be aply used to solve uh and apply in large data sets and we have also shown that in many
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situations these tests have near Optimal Performance guarantees which provide the
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theoretical justification required for using these procedures now had graph based methods is that something that has
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not been used as much in the past or had been looked at with more skepticism mean
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why is this right so so graph based methods have been used in practice for a long time
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but I think what comes out of my research is the the answer to the question that why they work so they were
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used and they were working fine before but here is so here we provide some theory behind why it works giving the
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justification of using it in real problems great and so getting to that actually like if I'm a business if I'm a
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business person or I own a business I mean how can I apply This research practically in my life right so one of
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the recent projects we're looking at is what is known as the two sample problem
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so imagine I have a situation where I want to find out whether a set of genes regulate or affect the occurrence of a
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disease so for example to just illustrate suppose I have uh uh 20 genes and I have the gene expression level
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data from 100 patients who have diabetes and I have the same expression level data for 100 patients who are healthy
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and the goal is to find out whether these 20 genes are expressed differentially so by that I mean that
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their expression levels are sign signicantly different between these two sets of patients and uh our research
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sort of aims to provide the theoretical understanding and comparison of the different methods that are deployed to
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understand or answer such questions what are some other applications for This research right so another interesting
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application of our work is in natural language processing mainly in problems which try to understand similarity
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between words so imagine the word color which can be spelled in two ways one with the letter U one without the letter
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U and they are the same words but the words for instance wolf and fox are both animals but they are very different
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words so in this case in spite of the amount of data we have the support size which is basically the collection of all
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words is far larger than the than the data set itself so one of our methods is what we are studying can be used to
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analyze such problems as well which I would think would be pretty interesting to businesses nowaday just because so
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many people are like taking social media posts and things like that trying to get
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data about customers that way right perfectly yeah yeah yeah sure and so what's next for This research right so
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currently I'm trying to understand or or analyze the methods for analyzing data
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in what is known as the high dimensional setting where you have say 10,000 genes
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and only a few hundred patients and uh you want to find out something about how the genes affect the disease or the
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patients and for these cases different new techniques are required and I'm trying to understand the theoretical uh
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the uh background of these results and how these can be used to find to get new methods and new algorithms great BR
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thanks for being with us today thank you thank you [Music]