Hello. My name is Gautam Kowshik. I'm currently pursuing my Masters at the University Of Southern California, studying Computer Science.
3D Computer Graphics, Large-scale Systems, Data Analytics and Data Visualization are areas that interest me.
Before this, I spent most of my time building and managing Grid Computing infrastructure for companies including Yahoo! Inc, InMobi.
I also work with the Computational Behaviour Group at the Information Sciences Institute of University of Southern California. Here are some pictures of Marina Del Rey from where I work, taken by photography wizard and colleague Pedro Szekely.
In this course, particle and continuum simulations are used as a vehicle to learn basic elements of high performance scientific computing and visualization. This project was to showcase the learnings in the course. We implement a fast fluid solver that simulates fluid motion and garuntees stability .We present a simple approach to simulate interaction between fluid and thin shell. Instead of solely considering one way coupling, we take both forces from fluid to thin shell and from thin shell to fluid into account, which is regarded as strong coupling. We borrow idea from Immersed Boundary Method (IBM) to solve this problem. Here is a poster we presented as part of the presentation.
The course focuses on theory, algorithms and applications of modern statistical machine learning. Topics include parametric and nonparametric methods for supervised, unsupervised learning and other paradigms. It particularly focuses are on the theoretical understanding of these methods, as well as their computational implications. We worked on a project that focused on NBA games data, aiming to shed light on the "Rebound Problem" i.e. which team will possess the ball after a missed shot has been taken. Our efforts have been on defining attributes that would aid in discriminating the data points. In this process, we encountered missing data issues which lead us to using variations of the standard classification methods and also statistical imputation to overcome this obstacle. The workflow has been a back and forth procedure of introducing features, and getting feedback from classification algorithms as we tweaked them. Here's a paper we presented for the final project.
This project implements particle systems to model a simple chain, with an emphasis on "hard" constraints. It uses the Lagrangian dynamics approach to apply suitable constraints to the particle system using Lagrange multipliers.
Implemented three interpolation schemes to interpolate human motion data obtained from an optical mocap system viz. Bezier interpolation using Quaternions, Linear interpolation, Spherical Linear Interpolation using Quaternions.
A modeled jello cube that is made of a network of mass springs. The cube will stretch, contract, oscillate, change velocity, bounce off the walls of the bounding box, based on the physical laws for a mass-spring system, made up of structural, shear and bend springs. Euler integration was used to solve differential equations for the animation. The cube bounces off the wall and responds to external forces applied as a force field within the bounding box.
A 3D roller coaster simulation in OpenGL using Catmull-Rom splines along with OpenGL lighting and texture mapping. The simulation runs in a first-person view, allowing the user to "ride" the coaster in an immersive environment. This included creating a 3D world view around the coaster using a Sky sphere and terrain.