Work Experience
Machine Learning Engineering Intern
Peraton Labs
Jun 2025 - Present
- Developed RL agent for IoT malware detection using PyTorch and graph neural networks, reducing exploration latency by 35% and increasing detection coverage by 25% across 500K+ daily device events.
- Built ETL pipelines and heterogeneous graph architecture with autoencoders to model device communication patterns, accelerating policy convergence by 40%.
Computer Vision Software Engineer
Memories.ai
Feb 2025 - Aug 2025
- Architected video memory framework for AR applications processing 10K+ streams using Python, Flask, and PostgreSQL, achieving 60% throughput improvement through frame sampling optimization.
- Published Python SDK on PyPI with 2K+ downloads, implementing async processing and REST API integration for video analysis workflows.
Undergraduate Robotics Researcher
IDEAS Lab, Purdue University
Mar 2025 - Jun 2025
- Built real-time SLAM pipeline in C++ and Python with sensor fusion and Kalman filtering, improving 3D reconstruction accuracy by 25% and reducing latency by 30%.
- Implemented neural radiance fields for novel view synthesis, generating photorealistic scene reconstructions for autonomous navigation.
Undergraduate Data Engineer
The Data Mine Corporate Partners, Purdue University
Aug 2024 - Dec 2024
- Built weed detection pipeline processing 200GB+ drone imagery with Python, TensorFlow, and PostgreSQL, optimizing ETL workflows for 40% faster retrieval.
- Engineered U-Net and YOLOv11 segmentation models achieving 92% accuracy on 50K+ images, reducing herbicide usage by 60% and costs by $150K annually.
ML Science & Engineering Apprenticeship
Naval Research Laboratory
Jun 2023 - Aug 2023
- Led 4-engineer team developing UNet, Transformer, and GAN models for underwater acoustics, improving transmission loss prediction accuracy by 20% over physics-based simulations.
- Architected secure RAG system with LangChain and vector embeddings for classified document retrieval, reducing query response time by 65%.