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I’m a data science and software engineering professional with extensive hands-on experience building scalable ML platforms, real-time analytics, and AI-driven learning tools across healthcare, research, and education. I focus on production-grade MLOps, computer vision, and clinical analytics, delivering systems that are fast, secure, and HIPAA/FDA-compliant, while enabling cross-functional collaboration and measurable impact. I enjoy bridging research and production, leading cross-disciplinary projects in medical imaging, predictive maintenance, and AI-powered education. I stay curious about new AI models, AI safety, and data visualization to empower teams to make data-informed decisions and accelerate innovation.…I’m a data science and software engineering professional with extensive hands-on experience building scalable ML platforms, real-time analytics, and AI-driven learning tools across healthcare, research, and education. I focus on production-grade MLOps, computer vision, and clinical analytics, delivering systems that are fast, secure, and HIPAA/FDA-compliant, while enabling cross-functional collaboration and measurable impact. I enjoy bridging research and production, leading cross-disciplinary projects in medical imaging, predictive maintenance, and AI-powered education. I stay curious about new AI models, AI safety, and data visualization to empower teams to make data-informed decisions and accelerate innovation.

Madhukar Reddy Musku

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I’m a data science and software engineering professional with extensive hands-on experience building scalable ML platforms, real-time analytics, and AI-driven learning tools across healthcare, research, and education. I focus on production-grade MLOps, computer vision, and clinical analytics, delivering systems that are fast, secure, and HIPAA/FDA-compliant, while enabling cross-functional collaboration and measurable impact. I enjoy bridging research and production, leading cross-disciplinary projects in medical imaging, predictive maintenance, and AI-powered education. I stay curious about new AI models, AI safety, and data visualization to empower teams to make data-informed decisions and accelerate innovation.…I’m a data science and software engineering professional with extensive hands-on experience building scalable ML platforms, real-time analytics, and AI-driven learning tools across healthcare, research, and education. I focus on production-grade MLOps, computer vision, and clinical analytics, delivering systems that are fast, secure, and HIPAA/FDA-compliant, while enabling cross-functional collaboration and measurable impact. I enjoy bridging research and production, leading cross-disciplinary projects in medical imaging, predictive maintenance, and AI-powered education. I stay curious about new AI models, AI safety, and data visualization to empower teams to make data-informed decisions and accelerate innovation.

Available to hire

I’m a data science and software engineering professional with extensive hands-on experience building scalable ML platforms, real-time analytics, and AI-driven learning tools across healthcare, research, and education. I focus on production-grade MLOps, computer vision, and clinical analytics, delivering systems that are fast, secure, and HIPAA/FDA-compliant, while enabling cross-functional collaboration and measurable impact.

I enjoy bridging research and production, leading cross-disciplinary projects in medical imaging, predictive maintenance, and AI-powered education. I stay curious about new AI models, AI safety, and data visualization to empower teams to make data-informed decisions and accelerate innovation.

Skills

Experience Level

Expert

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Intermediate

Work Experience

Applied Machine Learning Engineer at University of Iowa Hospitals & Clinics

May 1, 2025 - Present

Architected enterprise ML platform serving 280+ healthcare institutions with 300+ real-time analytics, supporting 1000+ active users with <3s latency and 99.9% uptime across distributed AWS (EC2, S3, RDS, CloudWatch) infrastructure. Engineered production MLOps pipelines with Apache Airflow (ETL orchestration), MLflow (model versioning), Docker, Kubernetes, and GitLab CI/CD, enabling zero-downtime deployments and lifecycle management. Developed computer vision pipeline using TensorFlow/Keras, PyTorch, nnU-Net, and ResNet-50 for nasogastric tube positioning in chest X-rays, achieving 0.998 AUC on 2600+ DICOMs with Grad-CAM explainability for FDA compliance. Deployed end-to-end PTGQ survey analytics website with Flask, ReactJS, PostgreSQL, and Pandas, integrating ETL pipelines, correlation models, and interactive Plotly/Seaborn dashboards for longitudinal healthcare data. Designed enterprise-grade security and compliance architecture with OAuth2, RBAC, HIPAA/FDA encryption, and AWS KMS, e

Machine Learning Engineer at McArdle Lab, University of Wisconsin-Madison

September 1, 2024 - May 1, 2025

Built production-scale computer vision pipeline for cell cycle classification using CNNs and 3D U-Net architectures with TensorFlow and PyTorch, achieving 23% F1-score improvement through transfer learning and data augmentation. Automated ML experimentation across 120+ hyperparameter optimization runs using distributed GPU training and MLflow experiment tracking, with reproducible workflows through Docker containerization. Developed advanced image segmentation algorithms combining OpenCV watershed segmentation with custom post-processing, enabling automated multi-cell instance detection and phase classification for high-throughput analysis. Architected microservices deployment with REST APIs, confidence scoring algorithms, and human-in-the-loop validation, reducing false discovery rates by 25% while integrating with laboratory information management system (LIMS).

AI/ML Research Engineer at University of Iowa

July 1, 2023 - May 1, 2025

Directed cross-departmental AI research initiatives across UI Hospitals, Physical Therapy, Molecular Physiology, and Biomedical Imaging, delivering machine learning in computational biology, ed-tech, and clinical research. Developed scalable, production-ready educational AI system using GPT-4, LangChain RAG, Whisper transcription, Stable Diffusion, Google TTS, and Flask+ChromaDB backend to deliver personalized domain-specific learning support. Built statistical pipelines in Python, MATLAB, OpenCV, and SimpleITK to quantify bias/variance in 3D tumor segmentation by benchmarking physicist annotations against JEI outputs and generating consensus ground truth for validation. Designed protein structure pipelines using AlphaFold, RoseTTAFold, and Graph Neural Networks, enabling molecular visualization with Cytoscape integration and fine-tuning scGPT for automated literature mining with 70% time reduction.

Data Scientist at Artificial Intelligence Research Laboratory, NITT

January 1, 2021 - May 1, 2023

Led industrial predictive maintenance initiative using CNNs and LSTMs with ensemble techniques (stacking, feature fusion), integrating IoT sensor data to reduce equipment failure rates and maintenance costs in production environments. Conducted Bayesian hyperparameter optimization and statistical analysis for model fine-tuning, deploying predictive systems with measurable cost reduction and improved equipment longevity across industrial facilities. Advanced genomics research applying genetic programming and centrality measures within neural networks to analyze gene interactions and identify disease-associated genes using Python, TensorFlow for medical breakthrough applications. Developed Hepatitis B risk prediction model using NHANES dataset, engineering demographic and clinical features for high-risk population stratification with privacy-preserving statistical methods and targeted screening optimization.