Machine Learning Engineer with a strong foundation in developing predictive models and intelligent systems. Skilled in utilizing advanced algorithms and deep learning frameworks such as TensorFlow and PyTorch, I focus on transforming data into actionable insights and automating processes to enhance efficiency. My expertise spans across various applications, from natural language processing to image recognition, driving innovation and achieving impactful outcomes in diverse industries. Join me as we delve into the cutting-edge world of machine learning to solve complex challenges and propel technological advancements.
Read MoreApplied advanced machine learning techniques to analyze complex non-coding DNA sequences with 88.2% accuracy and mentored undergraduates in DNA language model applications. Utilized techniques like Bayesian inference, GPN-MSA transformers, and XGBoost to enhance data analysis precision and improve pattern recognition, increasing classification accuracy by 20%.
Read MoreManaged and resolved incidents for the BMW project, achieving 95% SLA compliance and reducing recurring issues by 50% through strategic problem-solving. Developed software in Java and Python, utilized SQL and Unix, and integrated continuous integration practices to enhance delivery and quality, earning praise for strong communication and effective cross-functional teamwork..
Read MoreInitiated the Hand Out mobile application to reduce food waste and support orphanages, using Android Studio and Git, demonstrating adept project management and collaboration. Worked in a cross-functional team using Agile methodologies for effective execution and contributed to open-source projects focusing on microservices architecture with Go and TypeScript.
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Developed a web application to help users manage career goals, job applications, and networking opportunities, featuring user authentication, job search functionality, and a personalized dashboard. Utilized React.js for frontend development and combined Node.js with MongoDB for robust backend services.
Developed and deployed an LSTM-based sentiment analysis model in Python and TensorFlow within Jupyter Notebooks, achieving a remarkable 96.32% accuracy in classifying sentiments in movie reviews. Leveraged AWS SageMaker for efficient training and deployment, showcasing expertise in NLP and cloud-based machine learning solutions, highlighting my proficiency in applying cutting-edge technologies to real-world applications.
Developed an interactive Tableau dashboard to visualize global Netflix content trends, incorporating data on ratings, genres, and geographic distribution with Mapbox integration. Enhanced user interactivity with dynamic filtering for insights on content strategy and viewer preferences, managing bi-annual updates and stakeholder feedback to significantly improve functionality and user experience.
Designed and deployed an interactive Power BI dashboard to monitor Amazon Prime Video's content distribution, analyzing viewer demographics, content ratings, and genre trends. Enabled real-time data exploration and decision-making through advanced analytics, facilitating detailed insights into user behavior and marketing strategies, with regular updates to improve accuracy and user interface based on analytics best practices.
Implemented a Convolutional Neural Network (CNN) classification system using transfer learning to categorize brand logos, enhancing accuracy by 20% and reducing classification time by 60%. Achieved 94.62% accuracy with VGG16 through hyperparameter tuning, including early stopping and the Adam optimizer.
Developed "The Third Eye," a Raspberry Pi-based smart cane for visually impaired individuals, featuring voice-based assistance, object detection, and GPS technology to enable independent travel. This innovative tool empowers users with real-time feedback, navigation guidance, and enhanced situational awareness.