Note: For more specific details on the implementation of our comprehensive plan for Greenwood's matchamking methodology, please take a gander at the wiki of this repository!

Youtube Pitch Link

Filmmaker and Animator

AI and Back-end developer

Back-end and AI developer

Full stack developer

Greenwood specifically asked us just for a matchmaking algorithm, but we wanted to go the extra mile and give them a full technical plan that can last for a long time.

• We propose a three step plan that caters to both, Greenwoods immedieate and long term needs.

• Step 1 (Prototyped): Match making ALgorithm and UI (Sample UI)

  • Due to the lack of digitised data, a machine learning model is currently unfeasible. Therefore, we planned and coded an algorithm that weights each criterion respectfully

• Step 2 : Simple Machine Learning

  • With medium amounts of data, we can use some simplistic ML techniques.

• Step 3 : Complex AI

  • vast data allows for, and needs powerful techniques that can provide great insight.

• Step 1 : Use inputs from 3 sources,

  • Greenwood Database
  • Mentor UI
  • Student UI

• Data for ML (Steps 2 and 3) Work with Greenwood and come up with metrics that may be of use to them to accurately judge how well a mentorship went (the most obvious of which would be to ask for some kind of feedback from students/mentors) to come up with data points that will give us insight into future operations.

• Potential Future: In the long term, the AI will no longer just help in matchmaking, but also find new opportunities such as unexplored geographical areas, underutilised tools, and most effective mentorship methods. This could springboard a bunch of other methods to further Greenwood’s goal of increased representation of Black and Latinx communities in the business world.

Greenwood is an NPO that provides career opportunities to Latinx and Black Communities within the financial industry.

It has been 3 years since they implemented their business model of matching students with mentors for guidance in career prep. Having witnessed success with over 300 students in 2019, they are now looking to expand their reach to the national level. With this expansion, they expect their students to increase by an exponential amount.

All of us have been students in the US, though based in India, and we applaud Greenwood's initiative. Hopefully, in the future, we may be able to replicate such a program in our own country as well.

We found it challenging to try and balance the algorithm. Adding weights to each criterion was a complex task. Too high a weight gave too much preference to criteria and visa versa. Moreover, trying to decide which criteria were more important than the other took quite a bit of time, however, after having had several discussions with the Greenwood staff, we were able to come to a conclusion.

The conclusion that we drew in the previous section is essentially the algorithm we designed. We are proud of how it functions. Albeit not perfect, it does a decent job for having only worked on it for over a week. We are sure we will be able to further optimize the algorithm if we continue to work with Greenwood.

The unique dynamic of trying to empower Black and LatinX communities, introduced to us by Greenwood, taught us many invaluable lessons on problems faced by these communities. It was a pleasure to work with Greenwood to try and devise an algorithm that can potentially help these communities gain the mentorship they need to kick start their careers. Careers they might never have had the option of pursuing it were not for Greenwood.

If we win, the financial funding will be enough for us to dedicate our time to the Greenwood project. Along with improving the UI experience, we can further optimize the algorithm to generate better matches. Better the match, better the mentor-student experience, and thus better the rep for Greenwood.

Once Greenwood collects and digitizes enough data over the next few months, we can test out multiple machine learning models, providing better matches at a faster pace.

At the end of the day, we want to make sure that the algorithm and model help accelerate Greenwood's growth and reputation. By trying our level best to ensure the best possible mentor-student matches, we want to stimulate extremely positive experiences for Greenwood's clients. We hope that word of these positive experiences spread around, leading to a greater number of applicants for Greenwood.