Bio

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Biography

I am a FARE Fellow (Postdoctoral Researcher) at the Indian Institute of Technology Kanpur. My research focuses on traffic flow theory, disordered traffic systems, continuum traffic modeling, physics-informed machine learning, and data-driven transportation analytics.

Research Interests

• Traffic Flow Theory
• Disordered Traffic Systems
• Macroscopic Traffic Modelling
• Physics-Informed Neural Networks
• Machine Learning & Artificial Intelligence
• Traffic Simulations
• Trajectory Data Analytics

Education

Doctor of Philosophy (Ph.D.)

Transportation Engineering
Indian Institute of Technology Kanpur
2020 – 2026
CGPA: 8.85

Bachelor of Technology (B.Tech.)

Civil Engineering
National Institute of Technology Raipur
2016 – 2020
CGPA: 8.91

Professional Experience

FARE Fellow / Postdoctoral Researcher

Indian Institute of Technology Kanpur
January 2026 – Present
Advisor: Prof. Venkatesan Kanagaraj

• Physics-informed deep learning for traffic state estimation.
• Integration of trajectory data with macroscopic traffic flow models.
• Development of physics-based loss functions for traffic dynamics.

Doctoral Research Scholar

Indian Institute of Technology Kanpur
August 2020 – January 2026
Advisor: Prof. Venkatesan Kanagaraj

• Development of a second-order two-dimensional traffic flow model.
• Modeling longitudinal and lateral vehicle interactions.
• Drone-based trajectory data collection and analysis.

Research Associate

TU Dresden, Germany
April 2024 – September 2024
Research Hosts:
Prof. Ostap Okhrin,
Dr. Martin Treiber

• Development of higher-order continuum traffic models.
• Implementation of numerical schemes for PDE-based traffic systems.

Selected Projects

• Large-Scale Trajectory Dataset Development
  Development of UAV-based vehicle trajectory datasets containing more than 2.5 million trajectory points collected under heterogeneous traffic conditions.


• Physics-Informed Deep Learning for Traffic State Estimation
  Development of PINN frameworks integrating traffic flow equations with trajectory observations.


• Machine Learning-Based Pavement Crack Detection
  Implementation of CNN, ResNet50, and U-Net models for crack classification and segmentation.

Fellowships & Awards

• FARE Fellowship
• SPARC Project Fellowship
• Institute Research Assistantship
• Summer Research Project Fellowship (INSA)

Technical Skills

• Python
• MATLAB
• R
• QGIS
• Machine Learning
• Numerical Simulation
• Traffic Data Analytics

Last Updated: June 2026