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 AI Fellows 2021 Projects

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Projects:

1. 3D Videoconferencing holodeck platform (with Dr. Adrian Cable).                                                                                                        

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  1. Recently there have become available new 3D visualization tools for holographic representations

  2. This project will involve constructing a toolkit with off-the-shelf hardware

  3. The objective will be to create a 3D video conferencing solution so people can look at tech across the table in 3D

  4. REQUIREMENTS: C or C++ programming skills.

2. High Speed object recognition for a throwable camera (with with Francisco Aguilar and Sietse Dijkstra)

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  1. One of R42's portfolio companies has built a throwable ball camera that can capture stabilized video in near real time as the ball is travelling through the air. The objective of this project will be to investigate the application of neural network systems like YOLO and Single Shot Detection to categorize objects and people in the video.

  2. Pre-Reqs: Python 

3. Stock Market Prediction (with Dr. Jeremy Sosabowski and Dr. Frank Sortino)                                                

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  1. Predicting the stock market has been the holy grail for financiers. There have been many mathematical models.

  2. Most often, however, these models rely on their own past data. We will be taking signals from a model that does not use past data and attempt to get a model that takes an ensemble of predictive signals to find the best model for the trading system.

  3. We will be working with signals from Algodynamix.com. Pre-requisites: knowledge or willingness to learn, a statistical programming language. And/or user interface programming.

4. Making AI Easier to Explain: Creation of AI examples for Use in Teaching (with Dr. Ronjon Nag)            

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  1. This project will involve creating sample code in python on the google colab platform. The projects will take a problem, and have code that can upload a dataset, and allow users to change parameters in various kinds of neural networks, using the Keras platform.

  2. Pre-requisites: Python

5. Mimio - Creating a Personlity Engine (with Praveena Dhanalakota)

  1. Some of the technology areas we are focusing on are:

- Speaker diarization

- Voice emulation

- Speech emotion recognition

- Knowledge graph for predicting possible conversations

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2. Student Goals:

- Experiment on speech sentiment analysis

- To analyze voice data to understand emotion and sentiment.

- Experiment on the knowledge graph

- Recreate mannerisms based on past conversation data available. This will enable to frame interactive and therapeutic responses

- Author a blog post for experiments

Pre-requisites: Programming experience (minimum 1-2 years)

6. SuperBio: Creating a No-code Computational Biology Platform (with Berke Buyukkucak)

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Summary: Predicting protein folding is the holy grail of biology and there has been great strides made in this field, thanks to machine learning. Fellows that participate in this project will decipher, understand and try to improve the AlphaFold code that was implemented and then made public by DeepMind, Google's AI company.

Motivation: Protein folding prediction knowledge and skill can be translated into machine learning driven drug discovery research, which is a growing and forceful field that shows the potential to revolutionize both pharmaceutical and medical research.

Challenges: Difficult concept to understand and implement, terminology based knowledge.

 

Impact: Learn about a unique field that's developing, get students educated and knowledgeable. Pre-requisites: user interface programming, machine learning.

7.  Mathematics of Venture Capital 

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Summary: Venture capital investing is often described as qualitative where companies are evaluated on ephenral criteria. This project will attempt to apply mathematics to venture capital portfolio contruction, and company evaluation. Pre-reqs: Excel.

8.  Developing a New Post Modern Portfolio Theory

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This project is based on a US National Science Foundation (NSF) proposal to improve the currentModern Portfolio Theory. The group will investigate the class of assets in which the Sortino Ratio provides an improved efficient frontier compared to the Sharpe Ratio. The mentor for this project is Mr. Hassan Alam a serial entrepreneur with undergraduate degrees from MIT and graduate degree from Stanford. Assisting Mr. Alam will be Dr. David Edelman, professor of quantitative finance at University College Dublin (BS, MS MIT, Phd. Columbia) and Dr. Rickmer Kose (PhD Cambridge University)

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