About
Highly motivated and results-oriented Machine Learning Engineer and AI Developer with a strong foundation in deep learning, computer vision, and full-stack application development. Proven ability to design, implement, and optimize complex ML models, enhance system modularity, and contribute to open-source projects. Eager to leverage expertise in PyTorch, Python, and advanced ML techniques to drive innovation in challenging technical environments.
Work
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Summary
Contributed to the Lightning AI open-source project, focusing on improving code efficiency, modularity, and addressing critical bugs to enhance framework stability and performance.
Highlights
Refactored core utility functions to enable conditional NumPy imports, enhancing adaptability and resource efficiency across the framework.
Improved code modularity by localizing imports and minimizing global dependencies, significantly boosting maintainability and reducing complexity.
Resolved a critical bug related to Distributed Data Parallel (DDP) alias usage, ensuring stable and efficient distributed training operations for users.
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Summary
Developed and implemented a machine learning solution for vehicle classification, focusing on deep learning techniques and data preprocessing for robust performance.
Highlights
Developed and trained a Convolutional Neural Network (CNN) for military vehicle classification, leveraging transfer learning with the VGG16 architecture to achieve high accuracy.
Preprocessed extensive datasets of military vehicles and utilized VGG16 for efficient feature extraction, integrating custom dense layers to optimize classification performance.
Gained hands-on experience with Python, PyTorch, Keras, VGG16, ImageDataGenerator, and Matplotlib in a real-world defense research environment.
Skills
Programming Languages
Python.
Machine Learning Frameworks
PyTorch, Keras.
Computer Vision Libraries
OpenCV, scikit-image.
Data Manipulation & Visualization
NumPy, Matplotlib, Seaborn, ImageDataGenerator.
Deep Learning Architectures
VGG16, U-Net, Generative Adversarial Networks (GAN), Convolutional Neural Networks (CNN).
Machine Learning Concepts
Machine Learning, Deep Learning, Computer Vision, Transfer Learning, Semantic Segmentation, Image Enhancement, Large Language Models (LLM), AI Agents.
Evaluation Metrics
IoU (Intersection over Union), Dice Coefficient, Pixel Accuracy, PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index Measure), Confusion Matrix, ROC Curve, Precision-Recall Curve.
Web Technologies
React, FastAPI, Full-Stack Development, Web Development.
Specialized Techniques
Adversarial Loss, L1 Loss, Perceptual Loss, Medical Imaging, Distributed Data Parallel (DDP).