Survey Methodology • Data Science • Responsible AI
Designing rigorous measurement systems for AI-enabled research.
Graduate researcher with proven expertise in survey methodology, causal inference, geospatial epidemiology, and advanced statistical modeling. Published survey research in peer-reviewed venues and presented at AAPOR on transformer-based sentiment analysis methodologies. Research agenda focuses on developing trustworthy data integration frameworks that fuse survey methodology principles with generative AI, deep learning architectures, and privacy-preserving computational methods to advance automated data collection, quality assurance, and responsible measurement at scale.
Links
Research Focus
Turning complex data pipelines into reliable measurement systems.
My work centers on building trustworthy data integration frameworks that connect survey methodology with modern AI systems, so automated collection, classification, and inference stay interpretable and empirically grounded.
- Survey-aware AI workflows for data collection and quality assurance.
- Geospatial and epidemiological modeling over large-scale public datasets.
- Privacy-preserving and responsible measurement in applied ML systems.
Work + Education Snapshot
A compact pipeline from technical foundations to research systems delivery.
This view compresses the education and work journey into a readable sequence so the homepage feels directional instead of list-driven.
Birla Institute of Technology and Science (BITS) Pilani
Built the base in engineering, statistics, and machine learning through a civil engineering degree with a data science minor.
Legistify Services Private Limited
Moved into production-oriented ML and platform work across OCR, trademark similarity systems, API testing, and scalable automation.
University of Maryland, College Park
Deepened survey methodology, data collection, and causal inference while also teaching privacy and confidentiality through JPSM.
Institute for Social Research, University of Michigan
Scaled into large public-data research and infrastructure, from geospatial epidemiology at census-tract scale to CKAN-driven repository automation.
Research Interests
Explore
GitHub Spotlight
Starred repositories that best represent current work.
The GitHub showcase now refreshes from the GitHub API automatically and highlights the repositories that map most directly to active research, portfolio work, and reproducible code artifacts.
AAPOR-Project-EV-Sentiment-Analysis
Multi-source EV sentiment analysis comparing Reddit discourse, New York Times coverage, and LLM-based interpretation.
Project_Moneyball_FC
Cross-classified multilevel modeling for player market values across elite European football, linking player, club, and league context.
Portfolio
Portfolio repository of BITS Pilani project reports spanning machine learning, forecasting, experimental design, and environmental analysis.
CV Snapshot
Broadband completeness
Achieved full broadband data completeness across 129,572 U.S. census tracts through multi-source fusion.
Search discoverability gain
Improved open-data repository search discoverability through hierarchical taxonomy redesign.
Maintenance reduction
Reduced manual maintenance overhead with bulk metadata automation and schema-compliance workflows.
Blood donations mobilized
Helped organize a two-day blood donation drive with 60+ volunteers and 1,000+ donor records.
Education
Master of Science, Survey & Data Science (Data Science Track)
University of Maryland, College Park
Aug 2024 - May 2026 • GPA: 3.814/4.0 • Dean's Fellowship Award Winner AY 2025-26Bachelor of Engineering (Honours), Civil Engineering (Minor: Data Science)
Birla Institute of Technology and Science (BITS) Pilani
Nov 2020 - Jul 2024 • GPA: 3.327/4.0Highlights
- Published survey research in Springer and presented at AAPOR on transformer-based sentiment analysis.
- Built ML systems processing 2.4M images, 1.1M social posts, and 129,572 census tracts.
- Improved data discoverability by 35% and reduced metadata maintenance work by 90%.
- Delivered quantifiable outcomes across research, infrastructure, public health, and ML engineering.
Contact
Open to research collaboration, data science work, and thoughtful conversations.
Reach out via email, connect on LinkedIn, or browse code on GitHub.