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Click here or scroll down to respond to this candidateCandidate's Name
PHONE NUMBER AVAILABLE Boston, MA EMAIL AVAILABLE LinkedIn GitHub Availability Jul Dec Street Address , Jan Aug 2025 EDUCATIONNortheastern University, Boston, MA May Street Address
Master of Science in Electrical and Computer Engineering GPA - 3.8 Hardware and Software for Machine Intelligence Concentration Relevant Courses: Introduction to Machine Learning and Pattern Recognition, Natural Language Processing, Neural Networks and Deep Learning, Machine Learning Operations, Verifiable Machine Learning, Parallel Processing for Data Analytics R.M.K. Engineering College, Chennai, India May Street Address Bachelor of Engineering in Electronics and Communication Engineering GPA - 8.56/10 Relevant Courses: Problem solving and Python Programming, Fundamentals and Data Structures in C, Probability and Random Processes, Computer Architecture and OrganizationSKILLSProgramming Languages: C, C++, Python, SQL, Apache Spark, ARM Assembly Language, and Embedded C Libraries: NumPy, Pandas, Scikit-Learn, OpenCV, NLTK, Matplotlib, Keras, Pytorch, TensorFlow, Stats models Software: MS Office, Visual Studio Code, MATLAB, MySQL, Arduino IDE, Google Cloud Platform Tools: Apache Airflow, Docker, MLflow, Tableau, Vertex AI, Fast API EXPERIENCEGraduate Teaching Assistant, College of Engineering at Northeastern University, Boston, MA Jan 2024 Course - EECE7398 - Large Language Model based Dialogue Agents Supported students in designing and deploying LLM-based systems, emphasizing practical application of NLP concepts through hands-on projects, research analysis, and technical presentations Facilitated weekly office hours on model optimization, fine-tuning, and data preprocessing, while providing personalized feedback on assignments to help students improve their LLM designs and system performance PROJECTS/PUBLICATIONSOzone Level Detection (End to End Machine Learning Pipeline), [GitHub] - Boston, MA May 2024 Developed and deployed a machine learning pipeline for ozone level detection on Google Cloud Platform, integrating services such as GCS, GCP, Airflow, and Vertex AI, resulting in a 30% improvement in deployment efficiency Implemented monitoring FastAPI, linking the models endpoint to GCP for real-time tracking and achieving 99.9% uptime Automated data processing and model training with Airflow DAGs, incorporating error handling and data quality checks, which decreased data processing time by 25% and improved model accuracy by 15% Containerized the model using Docker and optimized it through hyperparameter tuning and cross-validation, improving prediction reliability by 20% and reducing false positives CNN-based Image Classification with Tiny ImageNet Dataset, [GitHub] - Boston, MA Feb 2024 Designed and implemented various CNN architectures to classify images from the Tiny ImageNet dataset Utilized image augmentation techniques and transfer learning to enhance classification accuracy Developed Python script for data preprocessing, image labeling, organization, facilitating efficient model training Optimized CNN configurations, ranging from basic three-layer networks to intricate deep neural networks, achieving a significant boost in validation accuracy to 85% using a pre-trained ResNet50 model Automated Medical Report Summarization and Terminology Extraction, [GitHub] - Boston, MA Jan 2024 Orchestrated preprocessing, optimized hyperparameters, and evaluated performance to achieve a 0.78 ROUGE score with BioBERT, although exploration of Pegasus - PubMed was undertaken Leveraged pre-trained model SciSpacy for NER, accurately extracting over 95% of medical terms from summaries Converted biomedical jargon into layman terms using Wikipedia for multi-word phrases, NLTK WordNet for single-word phrases with the ScispaCy model, enhancing accessibility and comprehension Intelligent Vehicle Damage Assessment and Cost Estimator for Insurance Companies, Chennai, India Sept 2022 Developed a project to build a VGG16 model that can detect the area and degree of damage on a car Fine-tuned the VGG16 model using a dataset of vehicle images with labeled damage areas, improving the models accuracy in detecting damage severity and location by 15% Implemented a rationale such that, the model can be used by insurance companies for faster processing of claims if users can upload pictures and the model can assess the damage |
