Hello! I'm Jordan, a recent BSCS graduate and searching for full time employment starting January 2025. I am currently working towards my MSCS (Computational Perception and Robotics Specialization). Explore my portfolio to learn more about my education, skills, experiences, and affiliations.
Under my leadership as the Team Leader and Product Owner, a team of 6 students developed the Live Sports Display project with a focus on delivering a real-time sports visualization experience. This project, built on a Django framework and developed in Visual Studio Code, is hosted on Heroku, a cloud-based application service. Utilizing tesseract-OCR for intelligent image analysis, our web application processes user-uploaded screenshots of fantasy basketball teams. This data is then conveyed through SSH to a Raspberry Pi, driving the 3 ws2812b LED panels. To manage this process efficiently, we employed Celery with Redis to handle asynchronous task execution, ensuring a responsive and scalable system. While the project's core functionality is operational, we continue to work and aim to have this finished by May 2024. We will enhance the user interface and construct a 3D printed case for the hardware components. Please contact me for a longer video or explanation as GitHub only supports files less than 25 MB.
Developed a web-based application to assist law enforcement in locating missing individuals using live video analysis and machine learning-based clothing detection. Features include a user-friendly interface, advanced image recognition, and attribute-based search.
Tech Stack: YOLOv11m, Python, Firebase Authentication, Firestore, Django, Roboflow
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CrowdCTRL uses a Raspberry Pi 4 and a lightweight YOLOv5n model to estimate occupancy by capturing frames every 15 seconds. The data is uploaded to Firestore and visualized in a Node.js-based mobile app, enabling real-time crowd monitoring.
Tech Stack: Node.js, Firebase, YOLOv5n, React-Native-Maps, Raspberry Pi, Python
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This project integrates Zillow's API and Twilio's WhatsApp messaging service to automate the search for rental properties. It retrieves apartment listings in a specified location based on user-defined criteria such as price range, number of bedrooms, and bathrooms. The tool filters results based on key features like parking and laundry, and automatically sends the details to the user's WhatsApp. The project simplifies the apartment search process by providing real-time updates on available properties directly to the user’s phone.
Initiated at the age of 16, the Speaker Backpack was my first engineering project, where I repurposed speakers from my brother's Dodge Neon and integrated an 8" sub, powered by a 12V DC battery. The design incorporated an echo chamber to optimize sound output, applying basic audio engineering principles. While the 70lb backpack was not the most practical in terms of portability, it was a hit at football practices and basketball games, teaching me about audio engineering, amp wiring, circuitry, and fabrication using heavy machinery like the shop bot.
Designed as a "totem" for music festivals, this project utilized ~1200 individually addressed RGB pixels attached to an 8-foot carbon fiber pole to create distinguishable pattern algorithms visible from a distance. Integrated with a sound sensor and Arduino, the display responded to bass frequencies, altering patterns during music "drops". My brother graciously assisted with the coding aspect, while I took the lead in the hardware development and overall design of the project. While it garnered interest for purchase, it was not pursued due to cost-effectiveness and market size. This project enhanced my knowledge in matrix manipulation, transformation, and data vectoring, and was a wonderful experience in collaborative work and leadership.
My friend and I embarked on a journey to refurbish a 1995 Regal speed boat, purchased for around 2 thousand dollars. Upon discovering a crack in the exhaust manifold and subsequent rust inside, we disassembled the engine, installed a new part, and retuned the engine. Despite initial stalling issues due to our tuning, professional retuning made it fully operational. This project provided insights into engine mechanics, tuning, and failure analysis.
I presented on the topic of privacy-preserving deep learning, specifically focusing on the paper 'Privacy-Preserving
Deep Learning' by Reza Shorki and Vitaly Shmatikov. This research explored how distributed learning techniques enable machine learning models to be trained without exchanging raw data between participants, thereby enhancing privacy within deep learning systems.
This presentation was held on:
- October 8, 2024 (Tuesday): Privacy-preserving deep learning
A full report on this paper is linked below.
Read the full paper here | Download Presentation
I presented on the topic of membership inference attacks in machine learning, focusing on the research paper "Membership Inference Attacks Against Machine Learning Models" by Milad Nasr, Reza Shokri and Amir Houmansadr. The presentation covered how these attacks exploit differences in model behavior to infer whether specific data points were part of the training dataset.
The presentation was held on:
- November 19, 2024 (Tuesday): Machine learning with membership privacy using adversarial regularization
A full report on the paper and its findings is linked below.
Read the full paper here | Download Presentation