Rajesh Neupane

Rajesh Neupane

Applied Machine Learning Engineer · PhD Candidate · Building AI for agriculture — computer vision, sensor fusion, and predictive models for real-world livestock systems

Rajesh Neupane

🔬 Open to Applied ML/AI Engineering roles — Available starting Summer 2026 📄 Download Resume

I am an Applied Machine Learning Engineer and PhD Candidate at Texas A&M University building production-grade AI systems for agriculture. My work spans the full ML lifecycle — from sensor data pipelines and ground-truth labeling to model deployment and validation — with real-world impact across 150+ commercial dairy herds.

I combine computer vision, time-series modeling, and sensor fusion to solve high-stakes problems in animal health, welfare, and food system resilience. Whether it’s detecting lameness from accelerometer patterns, classifying hoof lesions with YOLOv11, or modeling physiological stress from multi-sensor arrays, I build models that work in the field — not just the lab.

What I Build

  • 👁️ Computer Vision: YOLOv8/v11, CNNs, segmentation models for real-time disease detection and behavioral monitoring from on-farm imagery
  • 📊 Sensor ML: Accelerometer classification, thermal imaging analysis, time-series forecasting on streaming IoT sensor data (Lely, DeLaval, AfiMilk)
  • 🧬 Big Data & Genetics: Analyzed ~80M records across 150+ commercial herds to develop novel phenotypes and genetic parameters
  • 🤖 Production Models: Deployed ML pipelines for lameness prediction, mastitis detection, and heat stress quantification — validated against clinical ground truth
  • 🛠️ Full Stack ML: PyTorch, TensorFlow, Python, SQL, HPC/Slurm, Docker — from data engineering to model serving

Impact & Experience

  • Genetics Data Analytics Intern @ Genus ABS: Built ML pipelines processing robotic-milking sensor data from 150+ commercial herds to drive genetic trait selection
  • Graduate Researcher @ TAMU Precision Dairy Lab: Developed multi-sensor fusion models (rumination, water intake, temperature) that distinguish true health events from sensor noise
  • Data Science Ambassador @ TAMIDS: Led workshops on computer vision and accelerometer modeling; organized Agro-Cybersecurity seminar on data integrity in livestock systems
  • R&D Intern @ STgenetics: Built ground-truth workflows and optimized ML pipelines for field-deployed livestock monitoring hardware
Python PyTorch TensorFlow OpenCV YOLOv8/v11 scikit-learn SQL Docker HPC/Slurm MLflow Git/GitHub R

I’m passionate about deploying applied AI at scale in agriculture — building models that reduce disease, improve welfare, and make food production more sustainable. I’m actively seeking opportunities as an Applied ML/AI Engineer where I can bring computer vision, sensor analytics, and production ML expertise to agricultural technology.

Outside of work, I explore ag-tech innovations, contribute to open-source ML projects, and think deeply about how AI can bridge the gap between smallholder farms and industrial-scale agriculture.