Soumi Das

Computer Science & Electronic Systems Student

Profile

I am a dual-degree student pursuing a B.Tech in Computer Science and a BS in Electronic Systems. Through personal projects, I have gained experience in embedded systems, electronics, and machine learning. I am fascinated by the intersection of software and hardware, and I enjoy building practical systems that solve real problems.

Projects

Home Security System

Arduino Nano-based, multi-module home security system · Arduino Nano, C/C++, RFID, I²C, SPI, sensors
  • Built RFID-based authorization using an MFRC522 module over SPI.
  • Displayed real-time system status on an LCD1602 screen over I²C, with a buzzer alert for intrusions.
  • Implemented a relay-driven solenoid door lock, laser tripwire window security, and flame-sensor fire safety with emergency door release.
Repository Video Demo

Interactive LED Animation System

Touch-controlled animations on an 8×8 LED matrix · Arduino Uno, Embedded C/C++, MAX7219, SPI
  • Designed an interactive display system to render custom animations on an 8×8 LED dot matrix.
  • Integrated a TTP223 capacitive touch sensor as the user interface.
  • Programmed dynamic cycles so users can rotate through three distinct animations with a touch.
Repository Video Demo

PRISM — Severity Prediction & Response Strategies

Predicting disaster severity to inform response · Python, supervised learning, linear regression
  • Analyzed casualties, economic losses, and emergency-response data from the Global Disaster Response Analysis dataset.
  • Used supervised linear regression to predict a severity index for global disasters.
  • Achieved 97.51% accuracy and an RMSE of 0.4432 to support resource allocation and proactive disaster recovery.
Repository Poster Paper

Global Disaster Severity Prediction Model

Natural-disaster severity prediction and model evaluation · Python, scikit-learn, NumPy, Pandas
  • Predicts disaster severity using factors including casualties and economic loss.
  • Applied linear regression for prediction and KNN to visualize results.
  • Evaluated tolerance and accuracy using MAE, MSE, and RMSE metrics.
Repository

Fitsphere Model

Personalized exercise recommendations from health metrics · FastAPI, Python, scikit-learn, Joblib, Uvicorn
  • Created an app feature that recommends personalized exercises by analyzing user health metrics, including BMI, height, and calorie intake.
  • Used ensemble learning to train a Random Forest classifier for recommendations.
  • Built a FastAPI service to handle user data and deliver exercise suggestions.
Repository

Education

Indian Institute of Technology, Madras

Foundation Level, Bachelor of Science in Electronic Systems

Chennai, Tamil Nadu
Institute of Engineering and Management, Kolkata

2nd year, Bachelor of Technology in Computer Science and Engineering

Kolkata, West Bengal

Technical Skills

Languages
C/C++, Python, SQL (MySQL), JavaScript, HTML, CSS, PHP
Frameworks
React, Node.js, FastAPI, Arduino
Libraries
Pandas, NumPy, Matplotlib, scikit-learn, Joblib, Uvicorn
Developer tools
Git, VS Code, PyCharm, Google Colab

Co-curricular Activities

Spectrum Conference

Institute of Engineering and Management, Kolkata · Feb. 2025 – Present

Submitted a research paper on machine learning and presented a research poster.

Extra-curricular Activities

Poetry Writing

Portfolio: instagram.com/show.mee

Illustration & Painting

Portfolio: instagram.com/grizzzlie_bear