Our projects reflect our commitment to using research, data, and technology to solve real-world problems and advance health equity, innovation, and community well-being. Each initiative combines rigorous methodology with practical application, delivering insights and solutions that inform policy, strengthen systems, and improve outcomes.
From artificial intelligence and software engineering to public health, environmental research, and community-based studies, our work spans disciplines and sectors. We collaborate with academic institutions, public agencies, and community partners to ensure our projects are impactful, ethical, and grounded in real-world needs.
Together, these projects showcase our expertise, adaptability, and dedication to translating evidence into meaningful action.
This project demonstrated our strength in advanced machine learning and software engineering research by developing an optimized supervised decision framework for defect prediction. By focusing on class- and method-level data preprocessing, we significantly improved prediction accuracy and reduced noise in large software datasets. The work highlights our ability to design intelligent preprocessing pipelines that enhance model performance, reduce development costs, and improve overall software reliability an essential contribution to quality-driven software development.
At the height of the COVID-19 pandemic, our team applied predictive modeling techniques to estimate infection spread and mortality trends using real-world rate factors. This project showcased our rapid response capability, data modeling expertise, and public-health insight. The outcomes provided evidence-based projections to support planning, awareness, and intervention strategies, reinforcing our commitment to applying data science for urgent societal and health challenges.
This enterprise-level system demonstrated our capacity to design secure, scalable, and user-centric financial software solutions. Built with ASP.NET and layered security architecture, the platform ensured role-based access control, data integrity, and operational efficiency. The project reflects our strong foundation in secure system design, database management, and real-world application development for regulated industries.
This ongoing initiative focuses on improving software quality through machine learning and artificial intelligence–driven defect prediction models. By integrating predictive analytics into the software development lifecycle, we help organizations proactively identify vulnerabilities, minimize post-deployment failures, and reduce maintenance costs. The project underscores our long-term expertise in AI-enabled quality assurance and sustainable software engineering practices.
In this project, we addressed one of healthcare analytics’ most pressing challenges, bias in big data. Using efficient feature-selection techniques applied to Electronic Health Records (EHRs), we improved model fairness, interpretability, and performance. This work highlights our strength in responsible AI, healthcare informatics, and data governance, ensuring analytics solutions that are both technically sound and ethically robust.
This research provided a comprehensive analysis of structural, economic, and access-related challenges within the U.S. healthcare system. By combining data-driven insights with policy-oriented evaluation, we identified actionable solutions aimed at improving efficiency, equity, and patient outcomes. The project demonstrates our interdisciplinary capability to bridge research, policy analysis, and real-world healthcare improvement.
This community-focused study systematically examined substance use trends among adults in Staten Island, providing critical insights into behavioral patterns, risk factors, and service gaps. The project strengthened evidence-based advocacy and informed local stakeholders, reinforcing our commitment to public health research that directly benefits communities and supports targeted intervention strategies.
We led the redesign of a large-scale adult attitudinal survey to improve data quality, relevance, and usability for policy and program planning. Through rigorous methodological refinement, stakeholder engagement, and validation, the redesigned instrument produced more reliable insights into community perceptions and behaviors. This project highlights our expertise in survey design, evaluation research, and community-driven data solutions.
This project applied rule-based automated methods to extract and characterize pulmonary nodules from unstructured radiology reports. By transforming clinical text into actionable insights, we improved early detection workflows and supported clinical decision-making. The work showcases our strengths in natural language processing, clinical informatics, and practical AI applications in healthcare.
Leveraging the OMOP Common Data Model (CDM) and clinical data dictionaries, this project examined the relationship between air pollution trends and asthma-related respiratory outcomes. Our analysis provided population-level insights into environmental health risks, demonstrating our ability to integrate environmental data with clinical datasets to inform public health policy and prevention strategies.
This project explores how artificial intelligence can be leveraged to reduce stigma surrounding mental health by improving awareness, early understanding, and supportive engagement. Using AI-driven tools such as natural language processing and sentiment analysis, the initiative examines how public narratives, social attitudes, and misinformation influence perceptions of mental health conditions. By transforming data into compassionate, evidence-based insights, the project aims to support education, encourage help-seeking behavior, and promote inclusive mental health conversations—advancing equity, dignity, and access to care.
This flagship project investigates the hidden connections between environmental pollution, cancer risk, and mental health outcomes, with a strong focus on health equity. By integrating environmental exposure data, clinical records, and advanced analytics, the project aims to uncover disparities affecting vulnerable populations. It represents our core strength in interdisciplinary research, environmental health, data science, and social impact—advancing evidence-based solutions for a healthier and more equitable future.
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