👤 About Me
I am a student focused on applying artificial intelligence and computational modeling to real-world systems, particularly in public policy and data-driven decision-making. The ANCHOR Impact Simulation Platform was developed as part of this work to analyze large-scale government programs through simulation, behavioral insights, and scenario testing.
My experience includes research in AI and financial systems with IEEE publications, as well as building independent projects that explore applied intelligence. I also compete as a squash athlete, where structured training and performance analysis complement my systems-based approach to learning and problem-solving.
I am interested in designing scalable, data-driven systems that improve how complex programs are understood and implemented.
🏛️ About the ANCHOR Impact Simulation Platform
The ANCHOR Impact Simulation Platform is an AI-driven system designed to model and evaluate how public policy programs perform in real-world conditions—starting with New Jersey's ANCHOR property tax relief initiative.
Rather than relying solely on retrospective analysis, the platform enables forward-looking simulation, allowing policymakers and stakeholders to explore how program design interacts with human behavior before decisions are implemented.
What happens when policy meets behavior—and how can we design for better outcomes?
🎯 Purpose
Public programs often fall short not because of flawed intent, but because of unmodeled behavioral and access barriers. This platform was built to address that gap by providing a decision-support system that helps:
Increase Participation
Boost enrollment in public benefit programs by understanding barriers
Improve Vertical Equity
Ensure benefits reach households across all income groups fairly
Identify Hidden Barriers
Discover non-obvious behavioral obstacles to program access
Optimize Design
Improve program outcomes without increasing cost
🧠 What the Platform Does
Simulation Inputs
The platform simulates how households respond to public programs by combining:
- Behavioral modeling — trust, complexity aversion, decision bias
- Income & demographic distributions — across 21 NJ counties
- Policy design variables — benefit structure, application process
Evaluation Dimensions
It evaluates outcomes across multiple dimensions:
- Participation rates — who enrolls and who doesn't
- Distributional equity — fairness across income groups
- Fiscal efficiency — cost per successful enrollment
- Behavioral access barriers — trust, complexity, awareness gaps