Saptansu Biswas Personal Portfolio

Saptansu Biswas

Data Science & Quantitative Methods Specialist · Kolkata, India

Data science, quantitative research, and analytical leadership.

Creative thinker with an intense passion for data science and quantitative methods, focused on building analytical systems with Python, advanced statistical modeling, and research-driven problem solving.

Work with me View projects
Class XII score
87.6%
Class X score
96.8%
Academic timeline

Education

Current degree and academic performance

BS in Data Science

Indian Institute of Technology Madras · 2026–2030
IIT Madras BS

B.Tech in Civil Engineering

Vellore Institute of Technology, Vellore · 2026–2030
VIT Vellore

All India Senior School Certificate Examination (Class XII)

Aditya Academy Senior Secondary School, Kolkata · 2023–2025
CBSE: 87.6%

All India Secondary School Examination (Class X)

Aditya Academy Senior Secondary School, Kolkata · 2022–2023
CBSE: 96.8%
Selected work

Projects

Engineering and AI implementation
Oct 2023 – Nov 2023

Autonomous Multi-Tier Security Framework for Strategic Defense

Associated with Aditya Academy Senior Secondary
  • Led a multidisciplinary team to design, prototype, and program an autonomous military base defense system that won 1st Prize at a hackathon.
  • Engineered custom circuits with Arduino, sensor arrays, servo actuators, and automated roadblock mechanisms.
  • Implemented RFID-based identity protocols and fail-safe state machines for unauthorized access lockdown.
  • Built a cloud-based image classification flow to identify and track unregistered intruders for autonomous response.
  • Programmed secure admin override and authenticated post-breach reset controls.
Embedded Systems Arduino Robotics Circuit Design Computer Vision
Jul 2024 – Aug 2024

K-Nearest Neighbors (KNN) Facial Recognition System

Associated with Aditya Academy Senior Secondary
  • Architected and deployed an end-to-end KNN classifier for biometric facial recognition.
  • Used Principal Component Analysis (PCA) to reduce dimensionality and build an Eigenfaces-style compressed feature space.
  • Engineered preprocessing and distance-metric logic to improve robustness across lighting and exposure changes.
  • Demonstrated real-time, low-latency identification at an inter-school institutional fest.
Python Scikit-Learn PCA Machine Learning Computer Vision
Core inquiry areas

Research Interests

Quantitative methods driven

My research direction is centered on rigorous quantitative thinking, especially where economics, finance, and intelligent systems intersect.

I am particularly interested in designing models that capture nonlinear behavior, temporal structure, systemic risk, and market mispricing through statistical and machine learning frameworks.

Neuro-Fuzzy Systems Econophysics Stochastic Calculus LSTM Architectures Statistical Arbitrage Macro Policy Research