### Inspiration

The way research is funded is harmful to science — researchers seeking science funding can be big losers in the equality and diversity game. We need a fresh ethos to change this.

### What it does

Connexsci is a grant funding platform that generates exposure to undervalued and independent research through graph-based analytics. We've built a proprietary graph representation across 250k research papers that allows for indexing central nodes with highest value-driving research. Our grant marketplace allows users to leverage these graph analytics and make informed decisions on scientific public funding, a power which is currently concentrated in a select few government organizations. Additionally, we employ quadratic funding, a fundraising model that democratizes impact of contributions that has seen mainstream success through <https://gitcoin.co/>.

### How we built it

To gain unique insights on graph representations of research papers, we leveraged Cohere's NLP suite. More specifically, we used Cohere's generate functionality for entity extraction and fine-tuned their small language model with our custom research paper dataset for text embeddings. We created self-supervised training examples where we fine-tuned Cohere's model using extracted key topics given abstracts using entity extraction. These training examples were then used to fine-tune a small language model for our text embeddings.

Node prediction was achieved via a mix of document-wise cosine similarity, and other adjacency matrices that held rich information regarding authors, journals, and domains.

For our funding model, we created a modified version of the Quadratic Funding model. Unlike the typical quadratic funding systems, if the subsidy pool is not big enough to make the full required payment to every project, we can divide the subsidies proportionately by whatever constant makes the total add up to the subsidy pool's budget. For a given scenario, for example, a project dominated the leaderboard with an absolute advantage. The team then gives away up to 50% of their matching pool distribution so that every other project can have a share from the round, and after that we can see an increase of submissions.

The model is then implemented to our Bounty platform where organizers/investors can set a "goal" or bounty for a certain group/topic to be encouraged to research in a specific area of academia. In turn, this allows more researchers of unpopular topics to be noticed by society, as well as allow for advancements in the unpopular fields.

### Challenges we ran into

The entire dataset broke down in the middle of the night! Cohere also gave trouble with semantic search, making it hard to train our exploration model.

### Accomplishments that we're proud of

Parsing 250K+ publications and breaking it down to the top 150 most influential models. Parsing all ML outputs on to a dynamic knowledge graph. Building an explorable knowledge graph that interacts with the bounty backend.

### What's next for Connex

Integrating models directly on the page, instead of through smaller microservices.
## Inspiration

We were inspired by how there were many instances of fraud with regards to how donations were handled. It is heartbreaking to see how mismanagement can lead to victims of disasters not receiving the help that they desperately need.

## What it does

TrustTrace introduces unparalleled levels of transparency and accountability to charities and fundraisers. Donors will now feel more comfortable donating to causes such as helping earthquake victims since they will now know how their money will be spent, and where every dollar raised is going to.

## How we built it

We created a smart contract that allowed organisations to specify how much money they want to raise and how they want to segment their spending into specific categories. This is then enforced by the smart contract, which puts donors at ease as they know that their donations will not be misused, and will go to those who are truly in need.

## Challenges we ran into

The integration of smart contracts and the web development frameworks were more difficult than we expected, and we overcame them with lots of trial and error.

## Accomplishments that we're proud of

We are proud of being able to create this in under 36 hours for a noble cause. We hope that this creation can eventually evolve into a product that can raise interest, trust, and participation in donating to humanitarian causes across the world.

## What we learned

We learnt a lot about how smart contract works, how they are deployed, and how they can be used to enforce trust.

## What's next for TrustTrace

Expanding towards serving a wider range of fundraisers such as GoFundMes, Kickstarters, etc.
## Inspiration

Mental health is a huge concern and pressing issue these days. There's also a need to provide stronger virtual alternatives when in person meetings aren't an option. The goal of this app is to help improve mental health by bringing people together to bond over shared hardships.

## What it does

TigerHelp is an online support group web app for the Princeton community. Users can participate in small pods to share struggles and support peers. Our platform offers a live secure, anonymous chat with hate/offensive speech detection to ensure the best possible experience for users with privacy and positivity prioritized.

## How we built it

Botdoc api for secure chat, react, express, node, python for ML

## Challenges we ran into

The botdoc api took awhile to learn but eventually was able to figure out how to meet all my needs (huge thanks to Rafael!)

## Accomplishments that we're proud of

Built this whole thing in <20 hours as a team of 1.

## What we learned

How to use the botdoc api, how to get live updates with swr, nginx, etc

## What's next for TigerHelp

Building out the sentiment tracker & other helpful visualizations