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Hello, World.

I'm Rushabh Musthyala.

Engineer @ Goldman Sachs | NYU | BITS Pilani

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About

Let me introduce myself.

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I'm a data engineer in the Risk Division at Goldman Sachs. I attended graduate school at the Courant Institute of Mathematical Sciences (New York University) where I studied Computer Science, with a focus on Data Science and Applied AI. I completed my undergaduate degree in Computer Science (with a Minor in Data Science) from BITS Pilani (Hyderabad). I have a keen interest in Machine and Deep Learning applications and have work spanning Computer Vision, NLP, Multi-Modal Learning, Predictive Analytics and LLM based workflows. I also enjoy dabbling in development related projects during my free time and have experience working with MERN stack. Academics aside, I enjoy playing the piano (Trinity College certified), debating and baking.

Profile

Here's some information about me that probably isn't very useful.

  • Full Name: Rushabh Musthyala
  • Birth Date: March 29, 2001
  • Curently at: Goldman Sachs
  • Website: You're on it.
  • Email: rushabhmusthyala@gmail.com

Skills

My main area of interest is Machine and Deep Learning, but I also have experience with full-stack development.

PyTorch
Keras
SQL
Python
Java
JavaScript
MongoDB
React
Node
HTML
LangGraph
Django
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Resume

More of my credentials.

It ain't much, but it's honest work. :)

Work Experience

Risk Engineer

July 2024 - Present

Goldman Sachs

  • Developed an efficient program for validating the quarter to quarter changes in positions for a quarterly report, ensuring the accuracy of the analytics presented to the stakeholders.
  • Developed Python scripts to automate the creation of regulatory reports, leading to a 10x increase in efficiency and eliminating human error.
  • Established data cleaning and monitoring protocols, ensuring reliable data for downstream tasks and decision making
  • Led the development of credit risk monitoring systems leveraging large-scale data pipelines and ad-hoc analytics

    • Deep Learning Researcher

    August 2021 - January 2022

    Computer Vision Center, UAB

  • Worked under the supervision of Dr Luis Herranz and Dr Javier Corral.
  • Studied Deep Learning and mathematical approaches to low-light image enhancement.
  • Exploring the use of GANs for the task and the introduction of new loss functions aimed at maintaining colour constancy.
  • Developed a CycleGAN-inspired pipeline achieving near state-of-the-art image enhancement,

    • Software Engineering Intern

    May 2021 - July 2021

    Salesforce

  • Contributed to building a scalable online Enterprise Risk Management (ERM) platform, supporting a seamless remote work transition
  • Created live dashboards linked to the Risk Management portal to enable concerned personnel make informed decsions in a timely manner.
  • Authored onboarding documentation and guides, streamlining adoption of new platforms

    • Coding Instructor

    May 2020 - July 2020

    CampK12

  • Curated and delivered courses aimed at introducing school children to the world of programming.

    • Education

    Masters Degree

    September 2022 - May 2024

    Courant Institute of Mathematical Sciences (New York University)

    • GPA: 3.93/4
    • Relevant Courses: Operating Systems, Programming Languages, Deep Learning Systems, Big Data

    Bachelors Degree

    August 2018 - May 2022

    BITS Pilani Hyderabad Campus

    • CGPA: 9.04/10
    • Relevant Courses: Object Oriented Porgramming, Database Systems, Data Structures and Algorithms, Machine Learning
    • Clubs and Departments: Debate Society, Music Club, Journal Club, MUN Society

    Projects

    Check Out Some of My Work.

    Here are a few of the interesting things I've worked on as a part of my courses along with some fun stuff I've done on the side.

    Data Analysis AI Agent

    An AI-powered data analysis agent using Retrieval-Augmented Generation (RAG) and LLM-based autonomous agents to translate natural language queries into executable SQL for real-world datasets

    Check it out!
    Foundational Model for Head-CTs

    A 3D self-supervised vision model for head CT scan analysis, leveraging advanced techniques like Masked Auto-Encoders and DINO for enhanced representation learning

    Check it out!
    Neural Style Transfer

    PyTorch implementation of the technique presented in this paper. NST is a method that lets use style and structre extracted by CNNs to combine 2 images - to transfer the "style" of one image to another.

    Check it out!
    DC-GAN

    PyTorch implementation of DC-GAN, a Generative Adversarial Network that uses convultional layers to be able to generate realistic looking images. Trained on the wiki faces dataset, this implementation can generate new faces.

    Check it out!
    Fashion Analysis and Recommender

    Developed for the Flipkart GRiD 2.0 hackathon. This is an application that uses a convultional autoencoder style architecture to extract features from clothing items, using them categorization and trend analysis. Ratings of numerous clothing items were scraped from various e-Commerce sites to train a model to be able to predict the performance of a new clothing item before it went out to market.

    Check it out!
    Chaos Theory for Image Encryption

    Attempted to use the chaotic nature of the Lorenz Curves and Bifurcation Fractal to generate keys for the purpose of image encryption.

    Check it out!
    Multi-GPU training for Question-Answering using T5

    Compared multiple training techniques to find the optimum configuration to maximize performance while minimizing compute time and price.

    Check it out!
    Radiology VQA

    VQA model with multiple text and image encoders trained on a dataset created by modifying existing X-Ray datasets.

    Check it out!
    Publications

    Some stuff is published.


    An AI Framework for Predicting the Winner of the Grammys

    Presented at IEEE ICBDA '24 (Best Presentation)

    Check it out!
    ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

    Presented as a poster at ACM COMPASS '22

    Check it out!
    Anomaly Identification using Multimodal Physiological Signals on the Edge

    Presented at HealthDL'21, a part of ACM MobiSys.

    Check it out!
    Blog posts

    I like to write about things I do. Hopefully you like reading them.


    Flipkart GRiD 2.0

    In this post, I document my experience participating in the annual hackathon conducted by Flipkart and also explain my submission.

    Check it out!
    My time in Barcelona - Part 1

    This is travel blog of sorts where I talk about all the day trips I went on while pursuing my thesis in Barcelona.

    Check it out!
    My time in Barcelona - Part 2

    This is travel blog of sorts where I talk about some of the places I visited in the city while pursuing my thesis in Barcelona.

    Check it out!
    Scaling the training of large neural networks — a look into parallelism

    A gentle introduction to model and data parallelism in Deep Learning and how they can be combined to achieve even better results.

    Check it out!
    Radiology Visual Question-Answering

    A foray into multimodal learning - a VQA model trained on a (somewhat) custom dataset in the field of radiology. The model can identify problems in X-Rays, body parts present, orientation of scans and more!

    Check it out!
    Music

    Want to hear me play?

    I have been learning the piano for over 10 years. While I am trained as a Western Classical pianist, I regularly cover a variety of popular songs and post them on Instagram, you can follow me @high.on.keys. Here's a sneak peak!

    View this post on Instagram

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