ESP Biography

SUNIL PAI, Stanford Junior Premed/Engineer

Major: Physics

College/Employer: Stanford

Year of Graduation: 2015

Picture of Sunil Pai

Brief Biographical Sketch:

I am a Stanford student interested in interdisciplinary research applied to important medical problems. I am currently doing research here at Stanford, working on developing imaging tools and a programmable electronic device for more effective stem cell therapies. I enjoy watching sports (Go Stanford!) and playing squash, basketball, and tennis is my spare time. I am very excited to teach, and hopefully all of you can take something away from my classes! :)

Past Classes

  (Clicking a class title will bring you to the course's section of the corresponding course catalog)

E3682: Make Your Own Digital Circuit! in Splash! Spring 2014 (Apr. 12 - 13, 2014)
In the fall, I had taught a class on basic digital electronics, but I didn't have the opportunity to teach students how to actually build and debug circuits. Fret no longer! This time, I plan to teach you not just basic digital electronics, but also how exciting it is to build and test your own clocks, counters, and logic circuits! Prior attendance at the fall class not required.

E3226: Digital Electronics in Splash! Fall 2013 (Nov. 02 - 03, 2013)
Learn the basics of digital circuit design. I will teach students about switches, boolean operations and boolean gates, the basics of digital electronics. I will also teach students about how counters (like the decimal counter on a wristwatch) can be made using boolean gates and a clock pulse. I will then transition to more complex tools like multiplexers/demultiplexers and flip-flops, the latter of which is a crucial tool in computer hardware.

M3241: Practical Machine Learning in Splash! Fall 2013 (Nov. 02 - 03, 2013)
Can machines learn? Will they ever achieve a level of sentience that rivals that of humans? These are great questions that we will *not* answer in this class. Instead, we'll layout some of the foundations for classic machine learning techniques. Starting with the maximum likelihood approach, we will cover topics such as binary classification, regression, or fitting to a mixture of gaussians, and will show you how to derive their update rules. We'll end with real world examples, potentially in biology.