Experience

As an Instructor for Object Oriented Programming and Introduction to Computer Programming, I design and deliver coursework that helps students move beyond syntax and into real software reasoning. I teach object-oriented problem solving through hands-on programming, guiding students in understanding memory models, abstraction, and clean program structure. My instruction emphasizes readable code, incremental debugging strategies, and the mindset required to architect maintainable systems.

• Develop learning modules covering memory layout, pointer/reference semantics, RAII, class design, inheritance, and polymorphism.
• Lead multi-file programming projects requiring separation of interface and implementation, compilation workflows, and operator overloading.
• Coach students in diagnosing segmentation faults, memory leaks, copy‐vs‐move behavior, and undefined behavior using structured debugging techniques.
• Standardize Git workflows (branching, merge resolution, commit structure) to prepare students for collaborative software engineering environments.

I analyze multi-campus student enrollment and immigration-related records to ensure data accuracy and regulatory compliance across the CUNY system. My work focuses on building reconciliation workflows, automating validation pipelines, and generating dashboards that support data-driven decision-making for student services and institutional planning.

• Analyze 10,000+ student records each term (with 60+ data attributes per record) using SQL and Python to support SEVIS reporting, enrollment planning, and regulatory compliance.
• Build automated reconciliation and anomaly-detection workflows that reduce manual data correction efforts and improve cross-campus data reliability.
• Develop Tableau and Excel dashboards that visualize enrollment movement and compliance indicators across 26 campuses, enabling faster administrative decision-making.
• Document standardized data governance procedures to ensure repeatable audit readiness and consistent reporting across distributed administrative units.

As an AI Research Assistant at the Research Foundation of CUNY, I contributed to the development of a writing proficiency assessment system aligned with ACTFL standards. My work focused on preparing structured text corpora, fine-tuning transformer models, and evaluating model performance to ensure linguistic accuracy and interpretability. I supported the research pipeline end-to-end — from dataset design and labeling workflows to model experimentation, visualization, and documentation — enabling the project to move from conceptual framework to reproducible experimental system.

• Prepared labeled text corpora and evaluation datasets for ACTFL-aligned writing proficiency assessment.
• Fine-tuned transformer-based models and evaluated performance through confusion matrices, threshold sweeps, and linguistic error clustering.
• Built an interactive Gradio-based interface to enable explainable model inspection for instructors and researchers.
• Authored reproducible training and evaluation pipelines to ensure traceable and repeatable research experimentation.

As a Research Mentor in the Department of Mathematics & Computer Science, I guided undergraduate researchers in designing secure, production-aligned web systems. I led students through full-stack workflows, emphasizing authentication, session integrity, and software reliability. The mentorship combined technical execution with research framing, preparing students to translate theory into real-world engineering practices.

• Designed a concurrency-safe session enforcement architecture using Django middleware and PostgreSQL-backed session state tracking.
• Guided students through schema design, token invalidation logic, and device-aware session lifecycle management.
• Conducted code reviews emphasizing deterministic control flow, separation of concerns, and idempotent design.
• Structured the final implementation as a reproducible research artifact with deployment documentation and test coverage.

As a Graduate Research Assistant, I architected a secure real-time intelligence platform supporting human-trafficking investigations. I built live map dashboards and analytics workflows while integrating security controls such as OAuth2, RBAC, encryption-at-rest, and audit logging to safeguard sensitive PII. The platform enabled investigators to surface geographic trends, coordinate response efforts, and analyze behavioral indicators with confidence and data integrity.

• Developed a .NET Core / API backend and React-style admin interface enabling structured tip reporting, data retrieval, and investigative workflow support.
• Implemented real-time mapping features using Google Maps API, SignalR, and Hangfire to support live geospatial updates, asynchronous case processing and event notifications.
• Enforced security and compliance controls including OAuth2 authentication via Azure AD, TLS encryption in transit, field-level encryption at rest, RBAC to safeguard sensitive PII, and auditable access patterns.
• Designed comprehensive audit-logging, anomaly-detection, and data-integrity routines to identify suspicious access, tampering attempts, and unusual behavioral trends.
• Designed modular Razor/Bootstrap dashboards for filtering, drill-down review, and structured data export.
• Collaborated with investigators and administrators to create modular Razor/Bootstrap dashboard views supporting filtering, drill-down inspection, and structured export of demographic, vehicle, and behavioral data.
• Delivered flexible querying and data export features that enabled law enforcement, social services, and policy teams to generate actionable intelligence on trafficking indicators and geographic patterns.

I oversaw the management, validation, and reconciliation of university-wide student records, implementing processes that improved data reliability across multiple campus systems. I developed automated comparison tools, reduced manual verification, and ensured alignment with institutional and regulatory requirements.

• Built a C#/.NET application to automate cross-file student record comparison, reducing manual verification time by ~30%.
• Implemented unique-identifier matching and discrepancy indexing for reliable data alignment across systems.
• Automated reporting with Excel/VBA macros to generate audit-ready exception summaries.
• Collaborated with Registrar and ISSS teams to translate regulatory rules into deterministic validation constraints.

I managed data workflows across multiple student information systems, performed large-scale data cleansing, and developed query and visualization tooling to support enrollment analysis. My work improved reporting consistency, reduced manual processing, and informed administrative planning across academic units.

• Wrote SQL queries and stored procedures to reconcile and analyze 10,000+ student records across RDBMS platforms.
• Performed predictive enrollment and retention analysis using cohort-level trend indexing.
• Developed standardized Tableau and Excel reporting templates, increasing reporting efficiency by ~80% across offices.
• Implemented structured data quality checks enabling faster resolution of discrepancies.