Hemanth Reddy, Ph.D.

• Developed statistical and machine learning models to predict remaining useful life of critical mechanical components, reducing prediction error by over 15%. • Engineered algorithms to optimize maintenance schedules, potentially reducing equipment downtime by over 400 hours annually and $10 million in cost savings. • Led cross-functional collaboration to design and implement various anomaly detection models, improving prediction accuracy to over 90%. • Applied natural language processing (NLP) techniques to improve pricing prediction, achieving a 25% reduction in error compared to traditional approaches.

• Implemented deep learning models for real-time monitoring of equipment health using big data, reducing critical failures and improving system reliability. • Automated data pipelines and workflows with Python and R scripts, improving operational efficiency by 300%, enabling faster data processing and insights. • Developed interactive dashboards using Grafana to visualize health and performance metrics, leading to more informed decision-making and reduced response times.

• Developed machine learning models to forecast component failures, providing 3 weeks of lead time for preventive maintenance, reducing downtime and repair costs. • Engineered deep learning solutions using telemetry data to accurately forecast time-to-failure of critical components, enhancing predictive maintenance strategies. • Conducted comprehensive statistical analyses and developed interactive dashboards to identify and diagnose performance issues in critical systems.

Awarded the prestigious DAAD scholarship to conduct a Summer Research Project focused on determining cycle time in underground room and pillar coal mining operations. Leveraging MATLAB Simulink, I developed a simulation model to analyze and optimize operational efficiency, gaining deep insights into mining processes and advanced simulation techniques. This experience strengthened my technical expertise in modeling and contributed to practical solutions for optimizing cycle time in mining environments.

Served as a Summer Intern under the Head of the Numerical Modeling Department, where I gained hands-on experience with industry-standard software tools, including 3DEC and FLAC2D. During this internship, I developed foundational skills in numerical modeling and enhanced my understanding of complex geotechnical simulations.