Applied computer vision
Robust visual detection systems for real-world documents and data—building on production work with newspaper-image analysis.
Python
FastAPI
Research direction
Where lived practice
meets inquiry.
After years building and operating technology in the real world, I am moving deliberately toward doctoral study—bringing questions shaped by infrastructure, biology, and the human consequences of technical systems.
Research statement
My interests sit at the boundary of applied computing and biological information.
I am especially interested in how dependable cloud systems and machine learning pipelines can make scientific and public-interest data easier to interpret, validate, and use. This is an emerging programme of inquiry, grounded in my work across AI deployment, digital publishing, and bioinformatics.
Areas of inquiry
Questions I want to
take further.
Python
FastAPICloud-native research systems
Reproducible, cost-conscious infrastructure for model deployment, data processing, and collaborative science.
Docker
AWS
Kubernetes
Docker
AWS
KubernetesComputational biology
Using computational approaches to understand biological sequence, structure, and health-related information.
Responsible technical practice
How practical constraints—access, sustainability, and maintenance—should inform the systems researchers build.
Academic pathway
A return to
first principles.
My education reflects a sustained curiosity across biology and computing—now converging in a research-focused next chapter.
-
2025—2026
CurrentM.Tech, Cloud Computing
Indian Institute of Technology, Patna
-
2022—2024
CompletedBachelor of Computer Applications
Chandigarh University
-
2014—2016
CompletedM.Sc., Bioinformatics
Jamia Millia Islamia
-
2010—2013
CompletedB.Sc., Botany
University of Delhi
Potential collaborations