MATHWORKS

• Image Processing: Segmentation, filtering, edge detection, noise reduction, contrast enhancement

• Object Detection & Video Processing

• Spatial and Quantitative Image Analysis

• Batch Image Processing and Workflow Automation

• MATLAB Programming for custom algorithm development

STANFORD UNIVERSITY

• Supervised Learning: Linear/Logistic Regression, Neural Networks, Decision Trees

• Unsupervised Learning: Clustering, Dimensionality Reduction, Anomaly Detection

• Recommender Systems & Reinforcement Learning

• Full ML Pipeline: Evaluation, Tuning, Data-Centric Improvement, Deployment

• Python ML Stack: NumPy, scikit-learn, TensorFlow

MATHWORKS

• Data Preprocessing, Cleaning & Visualization

• Statistical Analysis, Modeling & Predictive Modeling

• Machine Learning Fundamentals & Basic Model Building

• Exploratory Data Analysis (EDA) & Insight Extraction

• Applied Data Science & Real-World Analysis

• MATLAB Programming

MATHWORKS

• Advanced MATLAB Data Analysis & Visualization

• Interactive App Design, Development & Deployment

• Workflow Automation & Optimization

• AI Integration: MATLAB Copilot & Generative AI for Data Science

• Productivity-Focused Solution Engineering

ARIZONA STATE UNIVERSITY

• Technical Foundation: Mastery of additive manufacturing principles and Design for Additive Manufacturing (DfAM) methodologies.

• Process Execution: Skilled in the complete workflow from build preparation and machine setup to advanced post-processing.

• Engineering Optimization: Proficient in critical tasks like part orientation, support structure design, and process parameter optimization to control mechanical properties and enhance surface quality.

IBM

• Data Science Methodology & Tools

• Python Programming & SQL

• Data Visualization & Analysis

• Machine Learning Model Development

• Cloud Labs, Hands-On Projects, & Capstone Application

IBM

• Python Programming: Scripting and development for data-centric applications.

• Data Analysis & Visualization: Conducting analysis and creating visualizations with Python.

• Machine Learning: Applying Python to solve machine learning problems.

• Project Execution: Implementing skills in a comprehensive capstone project.

IBM

• Tools: Jupyter, R Studio, SQL

• Languages & Analysis: Python, Statistical Methods

• Experience: Full-cycle project execution using applied data science methodologies

IBM

• Data Science Principles & Methodologies

• Execution of Data Science Tasks

• Jupyter Notebooks

• Relational Databases & SQL Querying

• Hands-on Labs & Applied Projects

UNIVERSITY OF MICHIGAN

• Development: Python programming, data structures, and API integration.

• Data Engineering: Databases, data retrieval, processing, and visualization.

• Application: Integrated these skills in a practical capstone project.

DUKE UNIVERSITY

• Critical Analysis: Systematic argument analysis, evaluation, and logical fallacy recognition.

• Structured Reasoning: Application of both deductive and inductive reasoning frameworks.

• Persuasive Synthesis: Formulating evidence-based, compelling, and logically sound arguments.