Computer Science & Applied Mathematics @ Smith College
Building reliable AI, data, and software systems across research, product, and campus communities.
I’m interested in the systems behind intelligent applications: data pipelines, evaluation workflows, AI agents, dashboards, infrastructure, and the messy real-world constraints that determine whether a tool actually works outside of a demo.
Currently focused on:
- AI/data engineering and ML systems
- Reproducible experimentation and benchmarking
- Full-stack data products and workflow automation
- Data infrastructure, validation, and monitoring
- Making AI/ML education more accessible through mentorship and campus community-building
I’m looking for Software Engineering, AI Engineering, Data Engineering, ML Engineering, and Infrastructure/Data Platform internships where I can build practical systems that are reliable, well-tested, and useful.
Languages
Data, ML & AI
Backend, Full-Stack & Data Tools
Developer Tools & Infrastructure
Python, TypeScript, FastAPI, Gemini API, Slack API, PostgreSQL, Docker
A full-stack AI workflow assistant that converts Slack messages into structured tasks, calendar actions, and team-facing updates. Designed around tool orchestration, schema validation, API routing, persistent task state, and debugging multi-step AI execution failures.
Focus: AI agents, backend systems, workflow automation, API integration, reliability, state management.
Python, C++, Benchmarking, Optimization, Reproducibility, HPC
Research project evaluating optimization frameworks for machine learning workflows under controlled experimental conditions. Built benchmarking and validation workflows to distinguish true system behavior from runtime noise and improve reliability in hyperparameter tuning experiments.
Focus: ML systems, experimental design, performance benchmarking, reproducibility, high-performance computing.
Python, Streamlit, Feature Engineering, Monitoring, Data Validation
An analytics and monitoring workflow for identifying operational bottlenecks across thousands of workflow events. Transformed noisy operational data into structured indicators and built real-time dashboarding tools to improve visibility into system behavior and data health.
Focus: data engineering, monitoring, feature engineering, operational analytics, dashboarding.
Python, scikit-learn, Librosa, openSMILE, Streamlit
Machine learning pipeline for classifying cardiovascular disease from phonocardiogram recordings. Built feature extraction workflows, benchmarked models, and developed a Streamlit prototype to make model outputs more accessible in a diagnostic workflow.
Focus: applied ML, signal processing, model evaluation, health AI, interpretable workflows.
Python, NumPy, Monte Carlo Simulation, Parallelization, Combinatorics
Simulation and brute-force search tooling for studying the probability of quads in k-card layouts over binary vector spaces. Explores combinatorial structure, probability estimation, and scalable computation for mathematical research.
Focus: algorithms, probability, combinatorics, simulation, mathematical computing.
- AEMES Peer Mentor @ Smith College — mentor first-year students and help build supportive STEM learning communities.
- Calculus TA — support students in developing mathematical intuition, problem-solving habits, and confidence.
- AI/ML Club Builder @ Smith — creating beginner-friendly spaces for students to learn AI, machine learning, data workflows, and responsible technical practice.
- Student Coordinator — organize programming and outreach for BIPOC communities in STEM and broader campus spaces.
- Break Through Tech AI Fellow @ Cornell Tech — national applied AI learning community focused on machine learning, collaboration, and industry-relevant AI projects.
I care deeply about making technical work feel less intimidating. A lot of my work sits at the intersection of building systems and helping people understand them.
I’m currently seeking internship opportunities in:
- Software Engineering
- AI Engineering
- Data Engineering
- ML Engineering
- ML Systems / AI Infrastructure
- Data Platform / Developer Tooling
- Full-Stack Data Products
I’m especially excited by teams working on reliable AI systems, data infrastructure, experimentation platforms, workflow automation, developer tools, and products that turn complex data into clear decisions.
Always building, always learning — and always trying to make technical systems more reliable, understandable, and accessible.