I am a Machine Learning graduate student pursuing an 𝗠.𝗦. 𝗶𝗻 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝘁 𝗡𝗼𝗿𝘁𝗵𝗲𝗮𝘀𝘁𝗲𝗿𝗻 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 (San Jose), with 𝟭𝟰 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 building ETL/data pipelines and analytics platforms in banking, travel and logistic data systems.

After years of designing and optimizing data infrastructure, I am now focused on 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, where high-quality data, feature engineering, and model evaluation meet real-world impact.

𝗪𝗵𝗮𝘁 𝗜’𝗺 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗻𝗼𝘄

Coore ML: Regression(Linear/Logistic), Classification(Random Forest, XGBoost), Clustering, Dim Reduction (PCA)
ML Lifecycle: Feature Engineering, Cross-Validation, Model Evaluation (Precision-Recall, ROC-AUC, F1-score, RMSE, MAE, R2), Hyperparameter Optimization.
Libraries: Python (Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, TensorFlow, Pytorch, Keras)
𝗥𝗲𝗰𝗲𝗻𝘁 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘉𝘶𝘪𝘭𝘵 𝘢 'Huffman coding–based compression system' using min-heaps and analyzed time/space complexity. Built a reader app with chunk based decompression 𝘐𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵𝘦𝘥 'multivariate Piecewise Linear Regression (PWLR)' and compared it with Random Forest and polynomial models on the Auto MPG dataset 𝘋𝘦𝘴𝘪𝘨𝘯𝘦𝘥 𝘢 'Neural Architecture Search pipeline' to automatically discover optimal CNN architectures for MNIST classification

𝗪𝗵𝘆 𝗺𝘆 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 My industry experience in 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 gives me a strong edge in applied ML. I understand not just how models work, but how data flows, breaks, and scales in real systems.

𝗔𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝘀𝗲𝗲𝗸𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 / 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗔𝗜 / 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 (𝗦𝘂𝗺𝗺𝗲𝗿 and Fall 𝟮𝟬𝟮𝟲) where I can contribute hands-on to model development, experimentation, and data-driven problem solving.

𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 Python | NumPy | Pandas | scikit-learn | Seaborn | Matplotlib | TensorFlow | Git | SQL | Jupyter | ML Model Evaluation

Always open to connecting with recruiters, engineers, and fellow ML practitioners.