About Me
I am a computer science professional with expertise in Machine Learning, Large Language Models, and Data Science. I specialize in turning raw data into impactful solutions and am looking for an opportunity to contribute meaningfully to a company.
Education
- University of Toronto | CGPA: 4.00/4.00, 2024/08 – Present
Master of Science in Applied Computing, Toronto, Canada
- Completed courses in Deep Learning, HCI, and Ethical Aspects of AI.
- Currently taking Visual and Mobile Computing, Data Science and Analytics, Natural Language Processing.
- Amrita University | CGPA: 9.25/10, 2020/08 – 2024/04
Bachelor of Technology in Computer Science, Kollam, India
Work Experience
- Data Engineer, 2023/01 – 2024/07
Havells Center for Research and Innovation (CRI), Bengaluru, India
- Developed and managed data pipelines in PySpark on Databricks for the Dual Mode Micro Inverter (DMMI) project, enabling alert systems, device mapping, and energy consumption analytics.
- Used Microsoft Azure services, including Databricks, DevOps, and EventHub for CI/CD and real-time data ingestion, with MongoDB for storage and APIs for seamless integration.
Projects
- Context-Aware Text Summarization and Sentiment Analysis Using NLP, 2024/09 – 2024/12
- Developed a transformer-based text summarization model using pre-trained BERT and fine-tuned it on a dataset, achieving a BLEU score improvement of 15% compared to baseline models.
- Designed an NLP pipeline using GPT-4 for real-time sentiment analysis of customer reviews, incorporating multi-label classification and contextual embeddings, resulting in a 30% improvement in precision for detecting sentiments.
- Fracture Detection Using Large Language Models and Deep Learning, 2023/02 – 2023/05
- Used CNNs and computer vision techniques for fracture detection in X-rays, using OpenCV and PyTorch to highlight detected regions.
- Integrated a GPT-4-powered LLM to generate diagnostic reports based on detected fractures, providing actionable insights to assist radiologists in accurate decision-making.
- Safety Status Prediction Using Machine Learning, 2022/08 – 2022/12
- Developed a binary classification model for safety status prediction using data cleaning, feature extraction, and preprocessing techniques to handle missing values and normalize features.
- Achieved up to 94.31% accuracy by training and evaluating models (Logistic Regression, SVM, Decision Trees, Random Forests) with hyperparameter tuning, validated through confusion matrices and classification reports.
Publications
- The Ethics of AI and ML: Balancing Innovation and Responsibility in Business Applications, European Economic Letters, 2023/12
- Conducted an analysis of ethical concerns in AI and ML for business, focusing on privacy, bias, and transparency, and proposed frameworks to balance innovation with ethical practices, prioritizing societal welfare and accountability.
- Neuro-Finance: Understanding the Brain's Role in Financial Decision-Making, Journal of Harbin Engineering University (Scopus Indexed), 2023/06
- Leveraged fMRI and EEG neuroimaging with machine learning to analyze brain activity in financial decision-making, identifying risk-reward patterns and predicting trends for personalized investment strategies.
Skills
- Programming Languages: Python, Java, JavaScript, Scala, SQL
- Machine Learning, Deep Learning, and Data Science: TensorFlow, PyTorch, Keras, Scikit-learn, Hugging Face, Numpy, Pandas, Matplotlib, Seaborn
- Big Data & Distributed Systems: Hadoop, PySpark, Kafka, MongoDB, Airflow
- Cloud Platforms & Software Development: Azure Data Factory (V2), Azure storage accounts, Docker, Azure, Agile Methodology (Azure DevOps), Git, GitHub
