Kushal Gangaraju AI · ML · Systems

Building scalable machine learning systems that are fast, reliable, and useful.

I'm Kushal, a Computer Science graduate student at the University of Rochester focused on applied AI systems, GPU-accelerated deep learning, and production-grade ML infrastructure.

AI systems
ML infrastructure
CUDA
DevOps
CV
Portrait of Kushal Gangaraju
About
Rochester, NY

I build end-to-end machine learning systems — from data pipelines and model training to scalable deployment.

My work focuses on building practical AI systems that are reliable, fast, and usable in real environments. I’ve worked across medical imaging, retrieval systems, and large-scale model training, combining deep learning with cloud infrastructure and GPU-accelerated workflows.

Recently, I’ve been building systems involving diffusion models, transformer architectures, and retrieval pipelines while integrating tools like PyTorch, FAISS, Spark, and AWS to support scalable ML experimentation and deployment.

What I'm currently exploring: combining large language models, retrieval systems, and generative models to create faster, more useful AI applications.

Tech stack
Tools and libraries I use regularly.
Python PyTorch CUDA FAISS Hugging Face MONAI Spark Databricks MLflow AWS Docker Linux Git
Experience
Applied ML systems work across labs and industry.
Graduating in May 2026
Labs
Earth Imaging Lab
Built a cloud-native ML pipeline for large-scale seismic signal preprocessing using AWS Lambda, Batch, and EC2. Designed containerized workflows that automatically process trace batches and generate thousands of waveform windows per run for downstream modeling.
Global Health & Medical Device Laboratory
Developed a fetal ultrasound analysis pipeline combining video segmentation (MedSAM2) and image enhancement (CycleGAN). Evaluated performance using Dice, IoU, SSIM, and PSNR while integrating MATLAB annotation workflows to reduce manual labeling effort.
CycleGAN MedSAM2 AWS Batch Docker
Industry
Indian Space Research Organization (ISRO)
Developed machine learning models for meteorological forecasting using SARIMAX, Random Forest, and Gradient Boosting on real-time atmospheric signals. Improved forecast accuracy and reduced launch delay risk through feature engineering and model tuning.
Feature Engineering Forecasting Gradient Boosting Time Series
Projects
Highlights
Contact
I’m currently exploring full-time roles and internships in machine learning engineering, AI systems, and ML infrastructure.
Open to opportunities

If you're building products involving applied AI, generative models, or scalable ML systems, I’d be happy to connect.

Send a short note — I’ll get back quickly.

Let’s connect
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