An enthusiastic university student with experience in game development, programming, music production, 3D design,
aeronautics/astronautics, astronomy, and astrophysics.
About
Hi, I'm Gokul Sriramasubramanian, currently pursuing a B.S. in Computer Science + Astronomy
at the University of Illinois Urbana-Champaign.
I've always been fascinated by the intricate connections between science and the world around us.
Whether it's making Rube Goldberg Machines, building sodium-powered cars, or creating various DIY projects around
my household, my passion for building and understanding how things work has been a central part of my life. My
exhilaration at using science to explain and manipulate the everyday world has always been a part of who I am.
The idea that science is everywhere is truly invigorating. But I’m not sure science can explain why I love building
so much.
My journey into coding and technology has been equally exciting. It started with a childhood obsession with game
development, which soon led me to explore more advanced topics like artificial intelligence, machine learning, and
astrophysics. Along the way, I discovered how these disciplines could be intertwined, enhancing my projects and
revealing new possibilities. Through internships and independent study, I’ve continuously sought to push the
boundaries of my knowledge, applying my skills to real-world challenges like medical research and space exploration.
My goal is to keep building—both in the literal and metaphorical sense—and to find innovative ways to use technology
to unlock the mysteries of the universe.
Music has been a central part of my life for as long as I can remember. I've been playing the Carnatic violin for the
past 11 years, starting under the guidance of my grandfather, Sri R. Kailasam, and later learning from the renowned
Delhi Sri P. Sunderrajan. Recently, I had the honor of performing my violin arangetram, a milestone that marked a
significant achievement in my musical journey.
My passion for music extends beyond playing the violin. I've also been learning vocal Carnatic music for 13 years,
initially training with Srimathi Shubha Narayanan and later with Srimathi Indu Nagarajan and Sri Delhi P. Sunderrajan.
This vocal background has not only enriched my understanding of the art form but has also significantly enhanced my
Carnatic violin performances, allowing me to infuse greater depth and emotion into my playing.
Alongside my Carnatic training, I've also been learning Western violin for the past decade, which has expanded my musical
horizons and allowed me to perform in numerous concerts as part of my high school chamber orchestra.
In my free time, I enjoy composing orchestral and electronic/hybrid music, blending different musical styles to create
something uniquely my own. I’ve spent many years honing this skill as well, researching software, plugins, and virtual
instrument libraries, and their use in producing professional quality music. While my initial pieces were nowhere near
said threshold, I kept toiling away. Over the years, I learned about new libraries, techniques, and instruments. I fell
in love with composing music.
If you're interested in learning more, here is my resume! And here are
some of my projects!
Projects
CS Projects
These are some CS projects I've worked on over the years!
This project focuses on using ML to analyze data from the NASA exoplanet archive and predict whether a given exoplanet will be
habitable for humans. By analyzing planetary features including (but not limited to) mass, radius, surface temperature, orbital
period, and planet type, the model predicts whether a given exoplanet may be habitable for humans. As the discovery of
exoplanets continues to grow exponentially, identifying exoplanets that could potentially support life is of great importance to
both astronomers and the broader scientific community.
The search for habitable exoplanets has become one of the most exciting fields in astronomy. Astronomers estimate that there are
billions of planets in our galaxy alone, and among them, a small fraction may reside in the habitable zone, where conditions
could allow liquid water to exist, a key ingredient for life. However, the habitability of a planet depends on many other factors,
such as its atmospheric composition, stellar radiation, and geological features.
While some research focuses on discovering planets with these characteristic, this project aims to go a step further by combining
machine learning with NASA's exoplanet data to predict a planet's habitability based on multiple variables. By using known
exoplanet data and characteristics of habitable planets in our solar system, we can create a model to assess habitability more
accurately than just relying on distance from the host star and/or whether the exoplanet is in the habitable zone of its host
star.
As our knowledge of exoplanets grows, the ability to identify potentially habitable worlds becomes increasingly important for both
future exploration and the search for extraterrestrial life. This project not only provides a method for evaluating habitability
but also offers a foundation for future research, incorporating stellar activity and atmospherics into the prediction process.
This project aims to create a comprehensive model that can assist in prioritizing targets for future space missions and telescopic
observations.
This project aims to identify pulsars by analyzing time-series data collected from radio telescopes. The dataset used for this
project is from the HTRU2 dataset, a sample of pulsar candidates collected during the High Time Resolution Universe Survey.
Pulsars are highly magnetized, rotating neutron stars that emit beams of electromagnetic radiation, which can be detected as
pulses of radio waves, X-rays, or gamma rays. These celestial objects are formed from the remnants of massive stars that have
undergone supernova explosions, collapsing under their own gravity to become incredibly dense. Pulsars rotate at incredibly high
speeds, with some spinning several hundred times per second, with their beams sweeping across space like beacons.
Detecting pulsars is crucial for various reasons. They serve as precise cosmic clocks that can test the fundamental laws of physics,
such as general relativity, and provide insights into extreme states of matter. Additionally, studying pulsars helps astronomers
understand the evolution of stars and the dynamics of galaxies. Their unique properties make them valuable tools for probing the
universe, detecting gravitational waves, and even aiding in the search for extraterrestrial life.
Detecting these celestial signals presents a unique challenge, as they are often weak and buried in significant noise. To tackle
this, the project utilizes Fast Fourier Transform (FFT) to convert the time-series data into the frequency domain, enhancing the
detection of periodic signals. Additionally, wavelet transforms are implemented for time-frequency analysis, enabling the detection
of signals that may change over time. This project also employs pattern matching algorithms to identify repeating patterns within
noisy data, thereby improving the reliability of signal detection. As this project is still a work in progress, some of these
features have yet to be implemented. Here is the
Github repo for further details!
To enhance signal quality, advanced denoising techniques are integrated to clean the raw telescope data before analysis. Moreover,
machine learning techniques, such as support vector machines, are utilized to differentiate between genuine pulsar signals and
background noise. The project is primarily developed in C++ and utilizes CMake for project management, along with libraries
specialized in FFT and data processing.
Protocol XenoD is a story-based first person shooter, set in an distant future with endless technological marvels.
Here's the Github repo!
I am a solo developer who is responsible for game design, programming, storyline, artwork(2D & 3D), animation, music, and
SFX/VFX. Currently, I'm working on a "practice range" map called Chimeraspace, which provides players with a practice area
to test gunplay, movement, and abilities against enemies. The game runs on Unity as of now.
I'm also currently learning Unreal Engine 5, and am planning to port this game to it soon. This will allow me to include
more realistic graphics and physics simulations, and will help the game's performance.
During my time as an intern at Beckman Institute, I worked on a project that involved creating a minigame inside Minecraft,
aiming to help teach high schoolers cell biology by providing an immersive and hands-on interactive experience.
I contributed to sound effect design, music composition, and game design both in Python and Java and within Minecraft
itself.
Research Intern atRadiation andOncology Labat UCLA
During my time as a research intern at the Radiation and Oncology Lab at UCLA, I worked with machine learning algorithms to
help predict where cancerous cells could move in order to optimize the accuracy of radiation therapy. Radiation itself is
harmful to all types of cells, so the goal of the project was to minimize hitting healthy cells with radiation. I helped
PhD students collect data and train neural networks to help accomplish this.
I also wrote programs to perform statistical analysis/statistical significance tests on data based on different treatment
methods for COPD (chronic obstructive pulmonary disease), depending on which of the four lobes of the lung was affected.
