Computer Science · University of Michigan

Astrid Greene

Computer Science at the University of Michigan, focused on software engineering and machine learning.

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About

Background and focus.

I study computer science at the University of Michigan, focusing on data structures, algorithms, and systems. I build software across C++ and Python, from low-level implementations to full-stack applications.

SchoolUniversity of Michigan

GPA3.7/4.0

HonorsUniversity Honors

Currently focused on

Software engineering in C++ and Python
Core CS: data structures, algorithms, systems
Designing efficient and usable applications

Experience

Roles and impact.

AI Engineering Intern

Coretek

May 2026 – Present·Farmington Hills, MI

→Built a company-wide meeting scheduling agent using Microsoft Copilot Studio, Microsoft Graph API, and Power Automate, ranking optimal meeting times across 2–10+ participants and reducing manual scheduling overhead.
→Designed cloud flows in Power Automate using HTTP requests with Microsoft Entra ID authentication to retrieve Outlook calendar availability, working hours, time zones, and scheduling constraints across 1,000+ internal users.
→Implemented AI-driven scheduling workflows using Claude Sonnet, Swagger/OpenAPI 2.0, Azure AI Foundry, and Microsoft Graph integrations to automate real-time meeting coordination across internal company teams.

Software Engineer

Tech Plus Development Team

Mar 2025 – May 2026·Ann Arbor, MI

→Designed and implemented a role-based authentication system using Supabase Auth and PostgreSQL, writing 10+ Row Level Security policies to enforce granular access control across 3 user roles (admin, member, recruit).
→Built an internal member portal in React and TypeScript with Vite, featuring a member directory, project team management, attendance tracking, and event scheduling, serving 50+ active club members across 6 project teams.
→Configured full-stack deployment pipeline using GitHub, Supabase, and Vercel, managing environment variables and API keys across both development and production environments to support continuous deployment workflows.

Technical Analyst

Tech Plus Consulting

Jan 2026 – Present·Ann Arbor, MI

→Built an AI policy chatbot by migrating 200+ client policy documents into a structured Google Drive and connecting U-M Maizey’s REST API to embed a searchable chatbot widget directly on the client’s password-protected site.
→Resolved data ingestion issues caused by inconsistent document formatting, access-restricted pages, and duplicate file versions, cleaning and standardizing source data across 15+ file types to improve retrieval accuracy and reliability.
→Conducted 6+ stakeholder meetings over a 4-week sprint to define system requirements, identify access constraints, and scope chatbot functionality, reducing the initial feature set by 40% to prioritize high-value policy retrieval

Junior Developer / Team Lead

Kode with Klossy

June 2023 – Aug 2023·New York, NY

→Led a 4-person team to design and develop a website addressing workplace discrimination
→Acted as lead debugger across front-end and back-end to support teammates and improve reliability
→Implemented a user story submission feature that collected over 100 contributions and published select narratives to spotlight underrepresented experiences

Research Assistant

Morgan State University

June 2023 – Aug 2023

→Co-authored paper ”Debunking The Curse of Dimensionality in a K-Nearest Neighbors Classification Problem” with advisor Dr. Eric Sakk, selected as a national Semi-Finalist in the Junior Science and Humanities Symposium
→Researched ”Curse of Dimensionality” in k-Nearest Neighbors, running controlled Python experiments to show that in uniform, hard-confidence data sets, increasing dimensionality can improve k-NN classification performance
→Designed k-NN experiments in Python on datasets of 1000+ points, varying k-values and dimensions (2D–15D) to test classification accuracy, with NumPy, Scikit-learn, and Matplotlib for data generation, training, and visualization

Projects

Selected technical work.

FeaturedMarch 2026

Personal Website

Personal portfolio built with modern web tooling. Responsive layout, scroll and interaction-driven animation, and structured sections for experience, projects, and contact.

Responsive design across breakpoints
Animation-heavy UI with Framer Motion
Structured content sections with clear hierarchy

Next.jsTypeScriptTailwind CSSFramer Motion

GitHub

Mar 2026

Deep Learning Dog Breed Classification

Developed deep learning architectures in PyTorch for multi-class dog breed classification, implementing convolutional neural networks, Vision Transformers, transfer learning, and multi-head self-attention across an 8,867-image dataset.

→Implemented CNN and Vision Transformer architectures with scaled dot-product attention
→Trained models across 10-class, 8,867-image computer vision dataset
→Built transfer learning pipelines with checkpointing and Adam optimization

PyTorchCNNsVision TransformersPython

Feb 2026

ICU Mortality Prediction Model

Engineered a clinical machine learning pipeline to predict ICU mortality risk using multivariate EHR time-series data, feature engineering workflows, and kernelized classification models across 12,000+ patient admissions.

→Processed 12,000+ ICU admissions and 40+ physiological variables
→Executed 1,000+ bootstrap resampling iterations and 5-fold cross-validation
→Benchmarked logistic regression, kernel ridge regression, and RBF models using AUROC

PythonScikit-learnNumPyPandas

Nov 2025

Sudoku Solver

C++ solver for 9x9 Sudoku. Represents the board as a 9x9 grid, validates rows, columns, and 3x3 boxes, tracks candidate values for open cells, and solves via repeated constraint checks and possibility elimination.

→81-cell board with row, column, and box validity checks
→Candidate tracking and constraint propagation
→Solves NYT Easy puzzles in under a second; runtime measured with chrono

C++STLchrono

GitHub

Oct 2025

Order Book Simulator

Built a price-time priority order-matching engine in C++ using priority queues to model bid and ask books. Achieves O(log n) complexity for both order insertion and matching, while enforcing strict price-time ordering to guarantee deterministic execution. The design focuses on efficient data structures and predictable performance under sustained, high-frequency order flow.

→Processed 1M+ orders and executed 760K+ trades in under 10 seconds
→74K+ orders/second throughput
→O(log n) trade matching efficiency

C++Priority QueuesData Structures

June 2025

BST-Based Map Container

Binary Search Tree with sorting invariants and an ordered Map ADT for efficient key-value storage.

→Sorting invariants, traversal logic, functors, templates, and recursion
→O(log n) insertion and lookup performance

C++BSTTemplates

May 2025

Naive Bayes Text Classifier

Multivariate Bernoulli naive Bayes classifier to classify posts by topic using log-probability scores.

→Determined labels using highest log-probability score
→Abstract data types for efficient file parsing and word-frequency detection

C++ProbabilityADTs

Oct 2021

Tic-Tac-Toe

Interactive tic-tac-toe game in Python using turtle graphics. Keyboard-controlled for both players.

→Board, Xs, and Os drawn with turtle; win and tie detection
→Scoreboard, reset, and replay with basic error checking

Pythonturtle

GitHub

Education

University of Michigan

B.S. in Computer Science, Minor in French

GPA 3.7/4.0 · University Honors

Relevant coursework

Data Structures and AlgorithmsMachine LearningComputer OrganizationObject Oriented ProgrammingDiscrete MathematicsLinear AlgebraCalculus I-II

Skills

Languages, tools, and focus areas.

Languages

C/C++
Java
Python
JavaScript/TypeScript
HTML/CSS
SQL
R

Tools & Libraries

Git
Matplotlib
NumPy
Scikit-learn
Pandas
Linux

Core Areas

Data Structures & Algorithms
Machine Learning
Retrieval-Augmented Generation
Software Development
Technical Research

Resume

Summary and download.

BS Computer Science (Minor: French) at University of Michigan. Experience in full-stack development, technical analysis, and research. Strong foundation in data structures, algorithms, and ML.

B.S. Computer Science, Minor in French, University of Michigan
Full-stack and systems-level development
Research and data-driven projects
Data structures, algorithms, and ML

Download Resume (PDF)→

Campus Involvement

GEECS

Community for women and non-binary students in EECS.

Instructional Aide

Support for Computer Organization coursework.

Math Exam Proctor

Monitor exam sessions, verify identities, and coordinate sign-in for 25–30 students per session.

Contact

Open to internships, research opportunities, and collaborative projects.