I work at the intersection of distributed systems, applied research, and production engineering, with a focus on making complex systems practical to operate.
Building applied-AI and platform capabilities on large-scale, distributed learning systems used by teachers and students worldwide.
- Architected and shipped an AI-driven incident-analysis system that mines historical incident data across 20+ microservices to model failure patterns, auto-generate diagnostic reports, and surface actionable insights — cutting incident triage, tracking, and resolution time by 50%+ and preventing recurring outages.
- Partnered with Data Platform Group leadership to design and evaluate architectural and process improvements across distributed microservices powering remote-learning platforms serving 25M+ digital users worldwide.
- Led a cross-functional root-cause investigation of systemic content-licensing failures; architected and deployed a remediation service under tight production constraints to correct backdated licensing at scale.
Delivered production applications with Java/Spring and React and helped move the organization to the cloud; worked with the Big Data team on large-scale financial datasets.
- Designed and deployed Penn Mutual’s first cloud-native application, establishing architectural standards later adopted across teams.
- Built production internal applications with Java Spring and React, integrating AWS services (Lambda, SQS, SAM) into scalable, production-grade systems.
- Engineered large-scale financial data pipelines with Scala and Cassandra — analysis, preprocessing, and validation — supporting downstream analytics.
Technical co-founder and CTO for end-to-end product development, building data-driven systems using Node.js and AWS to power AI-based hair-health recommendations.
- Designed backend data pipelines and APIs that enabled collection, processing, and interpretation of fine-grained user data, informing recommendation logic.
- Led agile planning and technical direction, balancing experimentation with production delivery of scalable, user-facing systems.
Taught Java and the Spring Framework to adult learners and professional engineers, building instructional tools and helping shape CS curricula across Delaware.
- Developed hands-on labs and instructional materials grounded in real-world computing problems.
- Built internal educational tools using Java, Spring, and TypeScript to support instructional delivery and learner progress.
- Collaborated with leadership to design and adapt computer science curricula used across schools in Delaware, emphasizing accessibility and learner retention.
Led full-stack development for community-centered STEM initiatives and technology-driven outreach.
- Led development of multiple full-stack applications supporting community-centered STEM initiatives.
- Co-designed and executed technology-driven outreach events (e.g., a City Streets race and a technology-based fashion show) that integrated computing into culturally relevant contexts to increase STEM engagement.
Doctoral research in Human-AI Interaction and AI-assisted cognition, funded by the NSF CSGrad4US Fellowship.
Major in Computer Science.
3-Year doctoral fellowship funding a Ph.D. in Information Science at Drexel University, focused on Human-AI Interaction and increasing the number of diverse, domestic graduate students pursuing research careers in the CISE fields.
Listed among 20 influential technologists rising to meet today’s challenges through building, shipping, mentoring, and community organizing.
A system to help people intentionally manage their lives, goals, and responsibilities with AI, designed around human context rather than raw automation.
- Exploring how LLMs and agents can reduce complexity in everyday life management instead of adding to it.
- Built as a dogfooded personal operating system, with myself as the first user, and connected to my research in human-centered AI.
An AI-driven system that analyzes, structures, and contextualizes educational content for personalized learning.
- Uses large language models and embedding-based retrieval to adapt explanations, examples, and pacing based on inferred learner context and prior knowledge.
- Investigates human-centered evaluation for interpretability, trust, and equitable access to learning resources.
An AI reflective-journal and co-therapy tool that generates context-aware, emotionally grounded responses for self-reflection and learning readiness.
- Built an end-to-end input-to-insight pipeline using AWS Transcribe, retrieval-augmented generation, and LLMs, grounding outputs in curated psychological and educational knowledge.
- Embedded structured thematic knowledge using vector search (FAISS) to shape consistency and accountability in AI-generated guidance.
- Serves as an applied evaluation environment for my doctoral research on cognitive-state-aware AI.
Program Management: SDLC, Systems Architecture, Jira, CI/CD pipelining
AI / ML: LLMs, Retrieval-Augmented Generation, embeddings & vector search (FAISS), AI evaluation, AWS Transcribe
Engineering: Java, Python, TypeScript, Scala, Spring, React, Angular, Django, Node.js, Docker, AWS, Azure, PostgreSQL, Cassandra, MongoDB