Experience

I teach graduate digital forensics in the NSA-validated M.S. in Cyber and Information Security program, connecting forensic methodology, malware behavior, and defensible investigative practice through laboratory-based technical work.

• Designed DFR 711, Cyber-Forensic and Malware Analysis, from the ground up, including a reorganized syllabus, lecture content, and laboratory sequence spanning incident response, evidence acquisition, memory and storage analysis, reverse engineering, and forensic reporting across Windows, macOS, and Linux.
• Guide hands-on laboratory work with open-source and commercial forensic tools, where students apply static and dynamic analysis techniques to malware investigation.
• Build case-based assignments that require students to justify their methods, maintain chain of custody, and produce reports suitable for legal or organizational review.

Fall 2026 undergraduate teaching across three computer science courses of 28–30 students each: object-oriented programming, databases and data mining, and computer algorithms.

• Teach CSCI 272 through object-oriented class design, abstract data types, inheritance, polymorphism, recursion, multi-file program structure, and Git-based submission workflows.
• Teach CSCI 362 through relational database design, entity-relationship modeling, SQL, MySQL laboratory exercises, and data-mining projects that extract useful patterns from application data.
• Teach CSCI 377 through algorithm design and complexity analysis, including divide-and-conquer, dynamic programming, greedy methods, graph algorithms, and string matching.

Analysis and validation of international student records across the 25 CUNY colleges that enroll international students, for data accuracy and SEVP compliance, centered on reconciliation workflows, automated validation, and the dashboards and projections that support enrollment planning.

• Analyze 10,000+ SEVIS records each term (with 60+ data attributes per record) using SQL and Python to support SEVIS reporting, enrollment planning, and regulatory compliance.
• Reconcile SEVIS data against CUNYfirst, CUNY's system of record for 225,000+ students, and build Power Automate flows and validation checks that support biennial recertification.
• Develop Power BI, Tableau, and Excel dashboards and term-over-term enrollment projections that help International Student and Scholar Services (ISSS) and the Central Office plan for changes in international enrollment.
• Document standardized data governance procedures to ensure repeatable audit readiness and consistent reporting across campuses.

Development and production deployment of an ACTFL-aligned Spanish placement platform combining AI-assisted language assessment, full-stack engineering, security controls, and cloud infrastructure.

• Built assessment and administration workflows using FastAPI, React/TypeScript, PostgreSQL, and Alembic, including versioned placement rules and database migrations.
• Integrated and evaluated fine-tuned and prompted LLM scoring pipelines across OpenAI, Anthropic, and Google providers; in Phase 1, the fine-tuned model exactly matched human ratings on 55% of 60 assessments and fell within one ACTFL sub-level on all 60.
• Contributed to containerized AWS deployment and CI/CD using Docker, CDK, ECS Fargate, RDS PostgreSQL, WAF, Secrets Manager, CloudWatch, and GitHub Actions.
• Implemented privacy and exam-integrity controls, including encrypted identity data kept out of model requests, immutable scoring snapshots, append-only audit logs, TOTP authentication, and duplicate-session detection.

Taught CSCI 271, Introduction to Computing and Programming, and CSCI 272, Object-Oriented Programming, across Fall 2025 and Spring 2026 with 28–30 students per section, building students' foundations in computational thinking, C++, debugging, and software design.

• Designed and delivered C++ coursework covering algorithms, control structures, functions, arrays, class design, encapsulation, inheritance, polymorphism, file handling, and multi-file program structure.
• Organized and led an Object-Oriented Programming Hackathon & CTF built around timed debugging and design challenges.
• Wrote open learning materials, including a 17-chapter Git and GitHub ebook, an interactive source-control guide, C++ setup guides for Windows and macOS, and a guide to using AI responsibly for study.
• Developed the full curriculum for CSCI 380, Agentic AI Systems, covering LLM workflows, tool use, retrieval-augmented systems, and multi-agent design; the course is awaiting enrollment.

Research and prototype development for an ACTFL-aligned Spanish writing assessment system, focused on model experimentation, evaluation, and scoring-to-placement architecture.

• Prepared 300 ACTFL-labeled Spanish writing samples in Python and ran fine-tuned and rubric-based few-shot scoring experiments using consistent training and evaluation formats.
• Evaluated model predictions against human proficiency ratings using confusion matrices, exact-match accuracy, adjacent-level accuracy, severe-error rates, and label coverage, using the results to set acceptance criteria.
• Designed the Python/FastAPI scoring architecture, separating model-provider integrations, ACTFL level validation, and placement rules so models could be compared under the same conditions.

Mentored five undergraduate researchers building a Django and PostgreSQL authentication system that enforces one active session per user, with an emphasis on session integrity and software reliability.

• Guided the session-enforcement design, combining Django middleware, PostgreSQL-backed session state, deterministic token invalidation, and GeoIP login tracking.
• Conducted code reviews emphasizing clear control flow, separation of concerns, and testing.
• Brought the final implementation to a public, reproducible repository with deployment documentation and test coverage.

I began this project as my graduate capstone in Fall 2024 and continued it as a Graduate Research Assistant in Spring 2025. I inherited a nonfunctional multi-agency human-trafficking intelligence platform built for an industry partner and completed an end-to-end legacy system modernization: migrating the backend to .NET 9, rebuilding its mapping layer, adding the security infrastructure it had never had, and deploying the recovered system to Microsoft Azure.

• Migrated the inherited API backend from .NET 5 to .NET 9, replaced obsolete dependencies and integrations, and rebuilt failing application components.
• Replaced the platform's paid mapping service with a new real-time mapping layer built on Google Maps API, SignalR, and Hangfire for live geospatial updates and background case processing.
• Designed and deployed the platform's security layer, including OAuth2 authentication through Azure AD, TLS in transit, field-level encryption at rest, and RBAC.
• Built audit logging and anomaly detection to monitor access patterns and protect the integrity of investigative data.
• Rebuilt dashboard workflows for filtering, drill-down analysis, and export of investigative data.

Validation and reconciliation of university-wide student records with the Registrar and ISSS teams, improving data reliability across campus systems while reducing manual verification and keeping records aligned with institutional and regulatory requirements.

• Built Excelsior, a C#/.NET application that automates cross-file comparison of 1,200–7,000 student records per run, reducing manual verification time by about 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.
• Translated regulatory and institutional rules into consistent validation checks.

Beginning as a College Assistant while completing my undergraduate degree, I managed data workflows for ISSS, performed large-scale data cleansing, and developed query and visualization tooling to support enrollment analysis. The growing scope of this work led to a full-time appointment as University Student Records Coordinator.

• Built data-collection pipelines for F-1 student data across all CUNY campuses, reducing collection time from 30 days to 2.
• Wrote SQL queries and stored procedures to reconcile and analyze 10,000+ student records across RDBMS platforms.
• Performed cohort-level enrollment and retention trend analysis to inform administrative planning.
• Developed standardized Tableau and Excel reporting templates, increasing reporting efficiency by about 80% across offices.
• Cleaned and migrated legacy student records and implemented structured data-quality checks enabling faster resolution of discrepancies.