ESP Biography

ARJUN CHANDRASEKHAR, UCSD PhD Student in Bioinformatics

Major: Bioinformatics

College/Employer: UCSD

Year of Graduation: G

Picture of Arjun Chandrasekhar

Brief Biographical Sketch:

I graduated with a degree from computer science in Caltech. I'm currently studying bioinformatics at UCSD. My work focuses on graph theory, probability theory, distributed computation, and ant biology.

Past Classes

  (Look at the class archive for more.)

The Mathematics of Credit Card Security in Splash Spring 2018
This course will introduce divisibility, prime numbers, modular arithmetic, format's little theorem, and Euler's theorem. It will then use this background to describe the RSA encryption scheme.

Introduction to Complexity Theory in Splash Spring 2017
How hard is it to find the cheapest set of flights that visits every state capital exactly once? How do we determine what it means for certain problems are "easy" or "hard"? This class will introduce help students answer this question through the lends of complexity theory. It will cover formal languages, decision problems, algorithms and Big-O complexity, reductions, and the P vs. NP problem.

How do we Assemble Genomes? in Splash Spring 2017
Obtaining an individual's full DNA sequence is extremely valuable for population studies, personalized medicine, disease testing, etc. However, current sequencing technology can only output small chunks of one's genome that must be pieced together. How is this accomplished? This class will go over the basics of De Brujin Assembly. It will cover graph theory, the Eulerian cycle problem, the cycle expansion algorithm, universal strings, De Brujin graphs, and their applications to genome assembly.

Beginner Yoga in Splash Spring 2017
This class will cover basic yoga postures including breathing, sun salutations A and B, vinyasa flows, and several flexibility-enhancing postures.

Introduction to Complexity Theory in Splash Spring 2016
This course will serve as a superficial introduction to complexity theory. It will review decision problems and reducibility (covered in class M46), and define big-O complexity, complexity classes, hardness and completeness, and introduce the complexity classes P and NP as well as the P vs. NP problem.