I name you three metamorphoses of the spirit: how the spirit shall become a camel, and the camel a lion, and the lion at last a child|
-- Friedrich Nietzsche, Of the Three Metamorphoses of the spirit, Thus Spoke Zarathustra
Typical of today's world, my career has been very inter-disciplinary. For example, I can think like a Computer Scientist and like a Statistician. From what I have seen, practitioners of these two sciences usually do not think alike. A Statistician spends a lot of time making sure the model is practical where a Computer Scientist cares most about speed and worst-case performance. And they use different terminologies and nomenclature. As you'd expect, they have different sorts of friends as well. This difference, however is considerably smaller than the smoldering war between Machine learning community and the Classical Statistics canon. Though most trained Statisticians (frequentists dominate here) are pragmatic and quite careful about the suitability of a certain technique, and though most such criticisms betray a lack of knowledge about L-estimators (percentile-based) and M-estimators (Robust estimation), the battle rages on. In the age of Big data, the disconnect has grown but there must be a reckoning by the end of 2016 after likely epiphanies on part of both camps.
Of course, every skill set has its place and that's what made Bell labs a hotbed of innovation was precisely the varied background of scientists they employed. A previous model, though on a much more focused area, was Bletchley Park during World War II.
Please navigate through the tabs above if you're interested in learning more about my applied work. The rest of the page is more philosophical and research-oriented.
|The Cosmic Soup of Data: Variation, Statistics and Physics|
Today's age is one of rediscovering and and in some cases, reinventing. The wealth of data available today is unprecedented, but it is only through the application of the scientific method that meaningful insights can be derived. Classical Statistics was developed to deal with 20-30 measurements and it was hard work doing those measurements. One simply needs to read about the rigor and deep thinking applied by William Sealy Gosset in deriving the t-distribution for small samples; and the work of legendary Ronald A. Fisher on permeutation tests and fastidiousness with designed experiments. In those days, a degree of freedom was blood, sweat and tears and not a just-because transaction of purchasing chewing gum from an airport newsstand or an impulse purchase of commodity item in a world like that of the movie The Minority Report. Having said that, however, the cosmic soup of data is more amenable to transport phenomena as in heat and mass transfer alongside fluid mechanics.
If you are interested in the research I have been involved in over the past 15 years, ranging from academic to industry practitioner, please visit my Research page, which is deliberately kept separate.
|The importance of mechanics|
The ancients considered mechanics in a twofold respect; as rational, which proceeds accurately by
demonstration, and practical. To practical mechanics all the manual arts belong, from which
mechanics took its name. But as artificers do not work with perfect accuracy, it comes to pass that
mechanics is so distinguished from geometry, that what is perfectly accurate is called geometrical;
what is less so is called mechanical. But the errors are not in the art, but in the artificers. He that works
with less accuracy is an imperfect mechanic: and if any could work with perfect accuracy, he would be
the most perfect mechanic of all; for the description of right lines and circles, upon which geometry is
founded, belongs to mechanics. Geometry does not teach us to draw these lines, but requires them to
be drawn; for it requires that the learner should first be taught to describe these accurately, before he
enters upon geometry; then it shows how by these operations problems may be solved.
|(c) Copyright 2015 Sandeep Rajput. All Rights Reserved.|