Building Products At The Intersection Of Innovation And Impact.

Starting May 2026. Check back for updates.

• Co-founded an interdisciplinary student organization of 300+ members built to bridge the gap between academia and industry, giving students real teams, real products, and skills that translate directly to corporate environments
• Led market research to identify skill gaps and high-impact project opportunities, scoping team engagements accordingly
• Managed roadmap and execution of an internal platform, defining system architecture, data models, and cross-team workflows
• Produced PRD-style problem definitions by analyzing startup requirements, market context, and user workflows

• Guide 150+ students implementing core AI algorithms in Python, conducting code reviews and debugging training pipelines
• Translate complex AI concepts into accessible explanations for students at varying technical levels

• Built NLP pipelines that extracted sentiment insights from real client calls and text, enabling users to surface actionable intelligence from voice data
• Maintained structured knowledge bases documenting AI agents, use cases, and workflows to support cross-functional alignment
• Managed JIRA board for backlog prioritization, sprint planning, and execution tracking
• Iterated on product features using user interaction data, improving performance and reducing failure cases by 20%

• Built a product that allowed users to dynamically visualize and query data, surfacing insights faster through interactive dashboards
• Wrote a Business Requirements Document defining system scope, user needs, and technical constraints
• Led a 5-member team through agile sprints, delegating deliverables and maintaining sprint velocity
• Influenced product patent strategy and client adoption by presenting findings to C-suite and external stakeholders

• Developed a structured AI governance and risk framework aligning emerging regulations with enterprise product design
• Presented AI transparency case studies to leadership, influencing adoption of responsible AI practices

• Improved model inclusivity by 15% using LLM bias-mitigation pipelines combining text mining and predictive analytics
• Built dashboards visualizing bias trends, enabling faster identification of systemic risk in AI-generated outputs