PeerSupport

(Previously called TherapyNow)

PeerSupport is an anonymous peer support chat application that allows users to message each other without the worry of their identity being known by their peers. Users can flag risky messages (e.g. Suicidal Ideation, Potential Violence, Harassment), which only monitor admins (e.g. school counselors) can view and decide the best course of action to ensure a safe environment. The flagging of messages can also be done by a machine learning algorithm.

To view images of the functionality in action, CLICK HERE!

The video above was our winning submission to the ESHIP.2 Solutions Challenge (a.k.a. Change The World Challenge)

HackRPI

President, Director of Logistics, and Master of Ceremonies

In the Fall of 2018, I discussed the creation and rekindling of RPI's annual hackathon with Chuck Stewart, RPI's Computer Science Department head. The last HackRPI event was in the Fall of 2017, where the final organizers left the organization. My team and I built HackRPI from the ground up to host HackRPI in the Spring of 2019 where we offered more than 80K worth of prizes. Some of the companies we managed to get a sponsor from where Bloomberg, TripAdvisor, Tensorflow, Palantir, and many others! By our next event in the Fall of 2019, we partnered with IBM and had 400 attendees.

In order to keep HackRPI consistently hosting annual hackathons at RPI, I established HackRPI as an official organization through the Computer Science Department, wrote a Constitution & Bylaws, and constructed an effective organization hierarchy with a new member recruiter position.

SAFEWheel (Smart Alerts for Everyone Wheel)

SAFEWheel is a system made up of a steering wheel cover and a photo capturing/analyzing device, that sounds an alarm when the driver’s hands are off the wheel or their eyes are closed. The data is stored on a web application for data visualization and analysis on driving performance. The system uses Google App Engine, Cloud Storage, AutoML Vision, a Raspberry Pi w/ Camera Module, and pressure sensors. The work appeared on Google’s internal newsletter as it showcased the usage of their Google Cloud Platform.

Suicidal Ideation Text Classification

The team members and I who were working on the PeerSupport project, created our own suicidal ideation test set by self annotating almost 2K pieces of text from Twitter and Reddit (r/SuicideWatch). I performed a grid search on multiple Support Vector Machine hyperparameters. The final accuracy based on the test set came out to be 84%. The most important feature used was TF-IDF (term frequency-inverse document frequency). Each step of the process can be followed through my Google Colab project

Sentiment Text Classification

I used GoogleNews Word2Vec, the ISEAR dataset, and a multi-layered Convolutional Neural Network in order to create an almost 90% accurate sentiment text classifier based on the test set. Each step of the process can be followed through my Google Colab project

Multiparty Interactions with a Robot Using Large Language Models

Speaker diarization is a key component of systems that support multiparty interactions of co-located users, such as meeting facilitation robots. The goal is to identify who spoke what, often to provide feedback, moderate participation, and personalize responses by the robot. Current systems use a combination of acoustic (e.g. pitch differences) and visual features (e.g. gaze) to perform diarization, but involve the use of additional sensors or require overhead signal processing efforts. Alternatively, automatic speech recognition (ASR) is a necessary step in the diarization pipeline, and utilizing the transcribed text to directly identify speaker labels in the conversation can eliminate such challenges. With that motivation, we leverage large language models (LLMs) to identify speaker labels from transcribed text and observe an exact match of 77% and a word level accuracy of 90%. We discuss our findings and the potential use of LLMs as a diarization tool for future systems.