PhD Research Scholar | Machine Learning & Responsible AI Researcher | FinTech | Open Science | ORCID-ID: https://orcid.org/0000-0003-3016-1633
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India-sugarcane-historical-dataset
January 28, 2026 – Present
India-sugarcane-historical-dataset — GitHub repository
View ProjectSmart50-ESG
September 9, 2025 – September 9, 2025
Smart50-ESG is a reproducible, ESG-aligned ML repository that prototypes vertical-farming solutions with synthetic datasets, fairness-aware models, and SDG linkages. Includes data generation, model cards, and ethical use guides for open, responsible urban innovation.
View ProjectDeezpa
September 7, 2025 – November 18, 2025
PhD in Computer Science (Machine Learning & Responsible AI, 2025). Researcher in credit & carbon scoring, fairness-aware AI, and ESG-aligned fintech. Curator of Harvard Dataverse datasets and GitHub repos on digital credit scoring, sustainability, and open science.
View Projectdigital-data-literacy-ml
September 3, 2025 – September 6, 2025
Machine Learning pipelines for digital & financial literacy outcomes — with fairness analysis, SHAP explainability, and Harvard Dataverse integration.
View ProjectRural-Credit-Carbon-AI
July 16, 2025 – July 16, 2025
Alternate dataset for AI-based creditworthiness and soil carbon scoring in rural economies
View ProjectPyTorch-CreditScoring-ThinFile
June 15, 2025 – June 16, 2025
A PyTorch-based deep learning extension to my PhD thesis on credit scoring of thin-file consumers.
View ProjectML-Credit-Scoring-ThinFile-Consumers
May 26, 2025 – June 5, 2025
This repository presents the datasets, models, and fairness-aware algorithms developed as part of my PhD thesis.
View Projectcredit-score
April 28, 2024 – October 15, 2024
This repository serves as a centralised hub for all things related to credit scoring.
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated on Machine Learning, Responsible AI, and credit/carbon scoring, indicating a deep specialization. While this is a strength for specific roles, the lack of diversity in project domains (e.g., NLP, computer vision, time series, general data engineering) might suggest a narrower cultural fit for broader Data Scientist roles. The candidate's experience level is listed as 0, which, combined with a PhD focus, suggests a strong academic background transitioning into industry.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. Project descriptions suggest a strong research-oriented and independent work style.