R42 Institute
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R42 Institute AI Fellows Program

R42 Institute  is running an AI Fellowship Program. A launchpad for deep technology and science disruptors, the Fellowship is designed to develop AI/machine learning, deep science, design thinking and entrepreneurial skills of emerging talent.


The Fellowship has been designed to provide each Fellow with one-on-one mentoring, an opportunity to hone their tech and science skills and combine it with design thinking tools over an accelerated period. 


This is an online program, with virtual interaction.


There is no fee for the program. The Fellowship is highly competitive and spaces are limited.


AI Fellows will receive:


  • R42 Institute AI Course (both live and recorded classes) - Fellows can get accelerated content to work on their projects

  • One-on-one mentoring with R42 team

  • All R42 lectures

  • R42 Institute Certificate

  • A unique opportunity to build a wide network of inventors, entrepreneurs and investors


Dates: Rolling 8-Week Program.


Application Process:


  1. Apply: Our application is simple. Tell us which project you are interested in, or propose a new one for R42, and a bit more background on yourself.

  2. Interview: After receiving your application, we will schedule a 20-minute online video interview with a member of the R42 team to learn more about your background and interests.

  3. Get Notified: After the interview, you will receive an admissions decision quickly - within 1-2 days. 


About R42 Institute:


R42 Institute, part of the R42 group, educates about AI, longevity, biotechnology. Its mission is to further define the university of the future which will be ever more important in the post-COVID world. This is another opportunity to seek ways to create the future and not just react to it.


Current Project Offerings: 


Each Fellow will undertake a project. Below are the projects R42 is offering. More projects may be listed later. Please, contact us directly if you have any ideas for projects for R42 and its portfolio companies.


1. 3D Videoconferencing holodeck platform (with Dr. Ronjon Nag)

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

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

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

  • REQUIREMENTS: C or C++ programming skills.

2. Graphical AI Platform (with Dr. Ronjon Nag)

  • Artificial Intelligence (AI) principles are now widely used in industry and many disciplines, not just in computer science, but also in physics, engineering and chemistry.

  • Textbooks and courseware are now available explaining AI in easy to understand language. Over time the tools have become easier to use with libraries for Python.

  • Platforms such as Tensorflow and Keras have made it much easier for those who have a python programming background to implement quite advanced AI systems such as convolutional neural networks in just a few lines of code.

  • Notwithstanding this, graphical user interface platforms are few and far between, and we are far away from an “Excel for AI”. This project will start to put the framework for such a project to allow users to upload data, clean data and change neural network parameters from a graphical user interface, maybe creating an upgraded form of playground.tensorflow.org.

  • Pre-requisites: a Python programming background.

3. Stock Market Prediction (with Dr. Ronjon Nag)

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

  • 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.

  • 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)

  • 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.

  • Pre-requisites: Python.

5. MemLove - Mental Health App for Grievers (with Praveena Dhanalakota)

  • MemLove is a uniquely personalized proactive mental health app for grievers, especially now due to COVID-19 impact, to cope faster and healthier by emulating the voice of loved ones. MemLove's mission is to recreate and keep memories of loved ones alive through artificial intelligence.

  • We hypothesize that the application of psychology models such as Continuing Bonds can impact ~100M people yearly in the US and defined social benefit capabilities. We are in the phase of system and services design to devise a framework that provides suitable and sustainable ways to build and deliver the services to users.

  • Some of the technology areas we are focusing on are:

    • Speaker diarization

    • Voice emulation

    • Speech emotion recognition

    • Knowledge graph for predicting possible conversations

  • 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)

  • 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.