I have extensive Research and Practical experience in Applied Machine Learning, Natural Language Processing, Deep Learning, Computer Vision, Digital/Embedded System Data Acquisition (DAQ), and Signal Processing which includes:
□ Semantic Risk Analysis using Large Language Model (LLM), Computer Vision (CV), Deep Learning; Knowledge Graph use cases in Cybersecurity and query Big data from Graph Database (Neo4j) and mitigate Supply Chain Attacks using Visual Language Models (VLM);
Python Frameworks (TensorFlow, Keras, PyTorch, OpenCV, NLTK, XGBoost, MxNet, Theano, CNTK).
□ Cosmic Ray Muon (CRM) Data Analysis using Python3 (Pandas, Numpy, Matplotlib, Seaborn, SciPy), work on Reconstruction Algorithm (C++), write Production level codes using Python3; Quality Assurance (QA) check of CRM Detectors.
□ Using Matlab, Python, and SOTA ML Algorithms to Identify Patterns and Anomalies in Electronic Health Records (EHR), Automate Emergency Signaling in the Hospital ICU (Intensive Care Unit), and use RNN, LSTM to analyze Time Series Medical Data.
□ Classify Acoustic Signals to Identify Defects and Discrepancies, and Map Physical structures for in-depth analysis, using Matlab, Simulink, and Python.
□ Applying Supervised ML (Regression, Decision Tree, Random Forest, KNN) in Predictive Analytics and Unsupervised ML (Apriori algorithm, K-means) in finding Trends, and Monte Carlo Simulation of Complex Systems.
□ Performing Data Analysis, Transformation, Exploration, Modeling, and Evaluation to provide -> Actionable Insights and deploy and Maintain Data Pipelines.
□ Training models, running Experiments, building ML- pipelines, and defining Measurements and Metrics.
□ Feature Engineering of raw data into a format suitable for ML models.
□ Tuning hyperparameters to optimize the ML model.
□ Implementing Natural Language Processing (NLP) techniques such as Text Preprocessing, Sentiment Analysis, Named Entity Recognition (NER), Topic Modeling, and Text Classification to extract insights from unstructured data.
□ Fine-tuning and deploying Large Language Models (LLMs) (e.g., GPT, BERT, LLaMA) for tasks such as Chatbots, Text Summarization, Q&A systems, and Document Analysis.
□ Performing Big Data Query to extract data from large datasets, ETL (Extract, Transform, and Load) data from Databases, and use Apache Spark for HPC (High-Performance Computing).
□ Deploy ML models in Cloud Platforms: AWS, Azure, GCP, and Containerization using Docker.
□ Creating Dashboard Visualizations and Reports in PowerBI, and Tableau.
□ Project Management & Task Tracking in Trello, Jira & Notion.
