“Pratik is unarguably one of the best people that I have ever worked with. Anyone who has worked with him (or has read his blogs), probably already knows that he is an excellent problem solver with awesome quantitative aptitude. But what makes him extraordinary are his two rare super-powers: 1.> He is always, ALWAYS learning. Whenever he is talking to anyone, irrespective of his relationship with that person, he is always looking to learn more. This means that he is scaling his skill set (and by extension himself), faster than almost anyone else that I know. 2.> He has an uncanny ability of making people feel good about themselves. This has dual affect; one that the person himself / herself does better because of the extra motivation and second that they are a lot more likely to trust and help Pratik, thus helping him build a large and valuable network. If he can hold on to his super-powers, I believe we have a superman to look out for in whatever field he chooses to work in.”
About
Experience & Education
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Nexus Venture Partners
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Publications
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Optimizing Elliptic Curve Scalar Multiplication with Near-Factorization
International Conference on Security and Cryptography - SECRYPT
Elliptic curve scalar multiplication (kP where is an integer and P is a point on the elliptic curve) is widely used in encryption and signature generation. In this paper, we explore a factorization-based approach called Near-Factorization that can be used in conjunction with existing optimization techniques such as Window NAF (Non Adjacent Form). We present a performance model of Near-Factorization and validate model results with those from a simulation. We compare Near-Factorization with w-NAF…
Elliptic curve scalar multiplication (kP where is an integer and P is a point on the elliptic curve) is widely used in encryption and signature generation. In this paper, we explore a factorization-based approach called Near-Factorization that can be used in conjunction with existing optimization techniques such as Window NAF (Non Adjacent Form). We present a performance model of Near-Factorization and validate model results with those from a simulation. We compare Near-Factorization with w-NAF for a range of scalar sizes, window sizes, divisor lengths and Hamming weights of divisor. The use of Near-Factorization with w-NAF results in a considerable reduction in the effective Hamming weight of the scalar and a reduction in overall computation cost for Koblitz curves.
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Test Scores
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CAT (Common Admission Test)
Score: 99.72 percentile
CAT is the entrance exam for Indian Institutes of Management (IIMs) taken by approximately 250,000 students
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AIEEE (All India Engineering Entrance Examination)
Score: All India Rank 2
AIEEE is the entrance exam for engineering institutes all over the country taken by approximately 500,000 students
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IIT JEE (Joint Entrance Examination)
Score: All India Rank 3
IIT-JEE is the entrance exam for Indian Institutes of Technology (IITs) taken by approximately 300,000 students
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