Developing a hierarchical contrastive learning model that captures functional exon similarity
across species using evolutionary distance, protein context, and multi-modal biological evidence.
Implemented 1D Convolutional Dilated Encoder. The framework aims to bring positive embeddings
(related sequences) closer together and push negative embeddings (unrelated sequences) farther
apart within contrastive space.
AQUAS is a robotics team at Columbia University focused on creating an autonomous robot
to detect and treat algal blooms in water bodies. Currently developing a full-stack data
dashboard for displaying robot telemetry, visualizations, and key statistics (data sent
from robot sensors). Dashboard consists of an authentication system, PostgreSQL queries,
FastAPI endpoints, useful visualizations, and clean design.
Developed a real-time American Sign Language (ASL) translator using the MobileNetV2 model.
Used MediaPipe, TKinter, and OpenCV for real-time hand detection. The model is able to
convert hand gestures into both spoken and written English, enabling sentence construction.
Pre-processed data by one-hot encoding letter classes and resizing the photos appropriately
for the model. User can add and delete letters to create words and pyttsx3 is used to
vocalize completed words.
Built and deployed a secure, role-based virtual hall pass system for Walnut Hills High
School using Flask, PostgreSQL, and Firebase authentication to manage admin and student
logins. Implemented real-time monitoring and analytics dashboards to track hallway activity
and pass usage. The project placed 1st at the University of Cincinnati IT Expo.
Conducted individual research on the optimization of underwater trash detection models.
Developed three individual object detection/image classification ML models (YOLOv8,
Faster-RCNN, custom CNN) and engineered a custom underwater image enhancement algorithm.
Compared and analyzed results from the three models in a 20-page research paper.
Acquired data from AV Traffic Sign Detection dataset on Kaggle. Pre-processed data by
scaling images to adequate dimensions and mounting them to Google Colab via Google Drive.
Used Faster-RCNN to train, validate, and test model. Experimented with learning rate,
epochs, batch size, kernels, and strides to acquire best performance. Goal of project
was to successfully classify and detect 18 distinct traffic signs on the road.
Created a neural network that used Batch Normalization, ReLU activation, and Softmax to
predict the "rating" of a student given the student's grades, extracurriculars, volunteer
activities, and parental history. One-hot encoded features so model could train on data.
Grade "0" represented best student rating and grade "4" represented the worst student
rating. Neural network model achieved 98% accuracy on test data after hyperparameter tuning.
FTC Sample Detection
Designed and optimized a YOLOv8 object detection model to identify and classify FTC Into
the Deep game elements based on color, shape, and orientation, achieving 88% detection
accuracy. Integrated model outputs into the robot's autonomous control pipeline to enhance
navigation, game element handling, and decision-making efficiency during competition.
Developed website for Ami Parikh's photography (AAAA Photography). Organized albums into
6 categories: wildlife, food, portraits, landscapes, urban, and landmarks. Implemented
linear animations using keyframes and maintained flexbox and grid-like structure throughout
website. Media queries were developed for the website to be observable on any device.
