Machine Learning Mechanical Engineering

NIKHIL NAVEEN CHANDRA

716-***-**** ********@*******.*** LinkedIn GitHub Portfolio

EDUCATION

Master's: Robotics, University at Buffalo, The State University of New York, February 2024 Master's in Robotics- Robotics II, Machine & Deep Learning Bachelor's: Mechanical Engineering, Manipal Institute of Technology, July 2021 Bachelor's in Mechanical Engineering- Mechanical Vibrations, CAD-CAM, Turbo Machines SKILLS

Languages: Python, SQL (Postgres), AWS, MATLAB, HTML/CSS Developer Tools: Visual Studio, Git, GitHub

Libraries: NumPy, Matplotlib, Pandas, TensorFlow, Pytorch EXPERIENCE

Project Intern, Redemptrix, Bangalore, IN: February 2022 - July 2022

• Analyzed data using SQL, identifying key patterns, trends, and anomalies, which informed strategic decisions, enhancing company's data-driven decision-making process

• Developed and implemented efficient data collection and entry processes, achieving a 100% accuracy rate, thereby eliminating potential errors and improving operational efficiency

• Introduced rigorous quality control protocols, enhancing data integrity and reducing reporting errors by 30%, significantly impacting the reliability of business insights PROJECTS

Custom GPT Deployment Pytorch, Ngrok

• Developed a sophisticated AI model integrating BERT/BiLSTM and Explainable Boosting Machine using PyTorch and HuggingFace, and deployed it within Docker containers for enhanced portability and scalability

• Streamlined model training, deployment, and monitoring through ClearML, incorporating ClearML- Serving and NVDIA Triton for scalable, real-time AI applications Hotel Price Comparison JavaScript, Django, Rest API

• Developed a Chrome Extension for hotel price comparison, integrating JavaScript for user interface interactions and leveraging Django REST API for real-time data retrieval

• Engineered a Django-based backend, implementing RESTful APIs for data handling, including dynamic queries for date-based hotel availability and pricing CNN Performance Enhancement Initiative TensorFlow Python

• Led a CNN optimization project, achieving 85% training and 70% testing accuracy through advanced TensorFlow and Python techniques, setting new benchmarks in model optimization and feature detection

• Addressed CNN underfitting and overfitting by integrating Python and Tensor Board, enhancing model robustness and pioneering innovative machine learning methodologies Classification and Regression Python, Machine Learning

• Utilized statistical methods and data visualization tools to uncover data patterns, leading to a 20% increase in operational efficiency through informed strategic decisions

• Developed predictive models using machine learning, reducing manual analysis time by 40% and enhancing the accuracy of strategic decisions by leveraging advanced analytic Handwritten Digits Classification Python, Machine Learning

• Implemented rigorous training, validation, and testing protocols using Python, achieving a 95% accuracy rate in machine learning classifiers, thus improving customer segmentation precision

• Employed a data-driven approach to enhance model performance, achieving a significant 10% improvement in accuracy, thereby enhancing the effectiveness of customer analytics

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