INDRAYUDH MUKHERJEE

Dynamic and results-oriented Computer Science Engineering undergraduate with a strong academic foundation and practical experience in systems programming, data science, and advanced analytics. Demonstrated expertise in developing software solutions using Python and C, with strong command over algorithms, data structures, Object-Oriented Programming (OOP), and Unix/Linux systems. Passionate about problem-solving, collaborating in teams, and creating impactful projects through innovation and critical thinking. Recognized for success in hackathons, internships, and academic excellence.

C, POSIX Threads, Unix System Calls | Feb 2025

• Developed a multithreaded file search tool in C that recursively scans directories to locate files based on name or content.
• Utilized POSIX threads (pthreads) to parallelize search across directory levels, enhancing performance on multi-core systems.
• Implemented string pattern matching algorithms and addressed edge cases like symbolic links, file permissions, and circular paths.
• Leveraged Unix system calls such as open, read, opendir, and fork for low-level filesystem interaction.
• Followed a modular design approach, ensuring maintainability and cross-platform compatibility (Linux/Unix).

TensorFlow, Google Colab | Mar 2025

• Designed and implemented a fully connected neural network to classify 28×28 grayscale images into 10 fashion categories.
• Preprocessed data using normalization, reshaping, and one-hot encoding to optimize model performance.
• Trained the model for 100 epochs, achieving a 90.2% test accuracy on the Fashion MNIST dataset.
• Presented model architecture and evaluation metrics in a collaborative team environment.

Python, Data Visualization, Correlation Analysis | Jan 2025 – Mar 2025

• Conducted a comprehensive data-driven analysis of life expectancy across Japanese prefectures.
• Assessed the impact of factors such as education levels, income, and healthcare expenditure using correlation and statistical analysis.
• Created interactive visualizations to highlight regional longevity trends and data-driven insights.

Python, Regression Modeling, Data Analysis | Mar 2025 – Present

• Investigated Simpson’s Paradox using the Kidney Stone Treatment dataset by contrasting grouped vs. aggregate outcomes.
• Cleaned and preprocessed the dataset to expose misleading trends caused by confounding variables.
• Applied regression models and visualization libraries (e.g., Matplotlib, Seaborn) to analyze subgroup variations.
• Explored integration with real-time medical data for diagnostic improvement and actionable analytics.

• Cisco – Introduction to Data Science
  Gained foundational knowledge of data science concepts and Python programming for data manipulation and analysis.
• Cognizant – Artificial Intelligence Job Simulation
  Acquired hands-on experience in AI concepts and Python programming through real-world job simulation tasks.
• E-Cell, IIT Bombay – Campus Ambassador (CA '24)
  Demonstrated leadership, entrepreneurial mindset, and event coordination skills as a campus ambassador.
• Alteryx SparkED – Data Analytics Process
  Learned to automate analytics workflows, clean data, and design visual insights using Alteryx Designer.
• Cisco – Data Analytics Essentials
  Developed proficiency in SQL, data analysis, and Excel for efficient data handling and reporting.