About Me
Hi, I'm David Ren! I'm a junior Data Science student at the University of Michigan, and my interests lie in ML/AI and Data Analytics. I'm currently seeking a full time position after my graduation in Summer 2026 — let's connect!
Hi, I'm David Ren! I'm a junior Data Science student at the University of Michigan, and my interests lie in ML/AI and Data Analytics. I'm currently seeking a full time position after my graduation in Summer 2026 — let's connect!
Agentic contract parsing workflow using RAG and LangChain. read more
Image classifier using CNN and ViT architecture in Pytorch. read more
Scalable search engine using tf-idf and PageRank with parallel index processing using MapReduce. read more
Instagram clone with static page generation, client-side and server-side dynamic pages, and account functionality. read more
Real-time banking simulation system with functionalities like wire transactions, transfers, and fraud checking.
YoLO model and custom evaluation pipeline using Python, Pytorch, and YoLO, in collaboration with the MTR Corporation. read more
Solar-powered automatic plant waterer with moisture sensor, weight sensor, and bluetooth integration using Arduino. read more
Machine learning algorithm using Naive Bayes in C++ that predicts the topics of Piazza posts.
Automatic short-form TikTok "brainrot" video generator pipeline using Python, Reddit, Beautiful Soup, and gTTS.
As a part of my EECS445 project, I built a dog breed classifier to distinguish Golden Retrievers from Collies using a dataset of thousands of real-world dog images. I implemented and compared the performance of convolutional neural networks (CNNs), CNNs with transfer learning, and vision transformers (ViTs) to find the most generalizable approach. Tested on a heldout challenge set, I achieved over 81% AUROC by using transfer-learning-based CNN architecture with feature engineering and data augmentation.
This project was done for my EECS 485: Web Systems class, where I built a scalable search engine similar to Google. I used Python and Hadoop Streaming to implement a series of MapReduce programs that created a segmented inverted index of web pages, supporting efficient full-text search with tf-idf scoring and PageRank-based link analysis. I then built an Index server API following RESTful protocol that queries the index and returns ranked search results in JSON format. Finally, I created a Search server using HTML/CSS/JavaScript to render a responsive frontend that communicates with the backend via REST calls, providing functionality like keyword search, dynamic page rendering, and Google-style result formatting. The architecture followed a Service-Oriented pattern to ensure scalability and separation of concerns between indexing, querying, and rendering components.
This project was done for my EECS 485: Web Systems class, where I used HTML/CSS (Bootstrap) to create website templates, Python (Click for CLI interface and Jinja for template behavior generation) to generate static site pages from JSON files with data, and shell scripting to automatically clean up and generate files. I then added server-side dynamic page generation and account functionality using Flask and SQLite3, client-side dynamic pages using JavaScript (with functionality like double-click to like and infinite scroll), and a RESTful API for client-side backend.
This project was a collaboration with the MTR Corporation, where we were asked to create a YoLO model that detects exit stairs from train cameras. I also created a script that evaluates the efficacy of YoLO models by calculating predictions' intersections over unions using Python, Pytorch, and YoLO.
In this project, I designed and built a solar-powered automatic plant waterer from scratch using Arduino and TinkerCAD. The device uses moisture sensors to determine when to water the plant, weight sensors to determine when the tank needs more water, and a bluetooth device to notify the user when the water needed to be refilled. This would ensure that they receive the right amount of water without human intervention.
I'll get back to you as soon as possible!