OpenMined aims to lower the barrier to entry in privacy preserving AI by offering a substantial fee free learning opportunity that can potentially lead to a career in the field of secure and private AI.
Our organization is truly global in nature - our members and leadership come from all around the world.
Even though we are varied in "how we got here" - the consistent thread running through OpenMined is that you are welcome here! Roll up your sleeves and dive in!
My name is Kritika, and let me tell you a bit about how it all happened for me.
I found OpenMined as I was looking for current implementations of Differential Privacy and was amazed at all the privacy-preserving tech I found. I started with the free Secure & Private AI (SPAI) course on Udacity. On the basis of that knowledge, I began poking around with PySyft to contribute as a developer. I also joined an amazing study group within OpenMined where I would learn and talk about various new ideas in the space of privacy preserving machine learning (PPML). I learned new tools and libraries like PySyft, TensorFlow, and how to understand new technologies like Federated Learning and SMPC.
My experience is not limited to learning technical content through coursework and development based contribution. I got the opportunity to become a mentor to beginners in the organization. I was happy to take such a responsibility as I love speaking to new people and I thought it would help accelerate my growth. I became an active part of the (then) newly founded Learning Team along with WomenWhoCode and we started building a privacy tech boot camp. I helped them with content creation, strategy, and planning.
Gradually, I became more involved with research based experiences. The first point of my research contribution at OpenMined was when I started the #research channel in the OpenMined slack with the help of senior members. Soon I got involved with the community in the #topic_differential_privacy channel, asking fun questions and interacting with people there.
Eventually, I found my true calling within the community and space of privacy-preserving machine learning: working on Differential Privacy research in Deep Learning. Ironically, I created a Differential Privacy based GSoC proposal which got rejected, but it resulted in the creation of a new research project and me becoming a research scientist at OpenMined. Soon after, I became a part, and then the lead, of the newly formed Differential Privacy Research team. Since then, many projects have spun out of the DP research team and some of them also aid with DP development.
There is immense value in these learning opportunities. They have a snowballing effect. More generally, all these opportunities helped me understand the bigger picture in Privacy tech and gave me the opportunity to write blog-posts, give numerous talks on public forums, and meet many talented hard-working people. I have recently been given the opportunity to become a speaker at the Privacy Conference #PriCon2020 on Sept 26 & 27. All of these exercises have gradually helped me build my interests and strengthen my basics in Deep Learning & Differential Privacy.
Being part of teams and leading them has given me new leadership skills and communication skills. It has even helped me create good relationships and friendships and to cultivate a growth mindset. They have built my confidence immensely, I feel much less prone to imposter syndrome now.
Does this all sound Interesting? Would you like to meet them?
Come hear a short summary from many of our Development Leads at #Pricon2020.
"Where do I fit in?"
Sunday 2:00pm UTC (Local times at pricon.openmined.org/agenda)
Then the FIRST thing you do is: Join our growing community at slack.openmined.org
There, you'll find a Step-by-Step set of instructions in the Welcome Package!
The goal of the Welcome Package is to help you:
• get to know who we are and what we do,
• find the best place for you to fit into the community,
• and get started on your path to contributing in your unique way!
It's the best all-inclusive resource for getting started in the OpenMined community!
Here you can find all the answers to questions like:
- How do I join OpenMined's Development Teams, Community Teams, or Research Teams?
- What projects does OM work on, and how to they work together?
- How do I get an OpenMined Mentor, and what should I expect from them? (550+ currently being mentored, 80+ graduated from the Mentorship program.)
- Where can I find short, medium, and long video content about how OpenMined works?
- Are there tutorials to help me learn about the code base?
- Where is the best place to get started contributing to the code base?
What you learn
By joining the OpenMined community, there is a plethora of skills you can develop by utilizing the substantial learning opportunities offered. The key fields of learning are as follows:
- Open Source Contribution
OpenMined is a great place to start contributing to open source, as it is an extremely helpful, friendly, and resourceful community, where every beginner is given personal mentoring and is encouraged to learn, grow and contribute.
- Machine Learning
OpenMined creates many tools, educational resources and opportunities for making scientific contributions in the field of Artificial Intelligence, and the sub-fields of Machine Learning such as Natural Language Processing, Computer Vision, Deep Learning, Reinforcement Learning, etc.
Various cryptographic ideas and techniques are implemented and explored at OpenMined, such as Secure Multi-Party Computation, Homomorphic Encryption, Zero-Knowledge proofs, from the perspective of Information Security and verifiablity.
- Federated Learning
Some of OpenMined's key technology is built using the framework of federated learning - both model-centric and data-centric. It enables us to use Machine Learning in a truly distributed fashion.
- Differential Privacy
Various deferentially private algorithms and mechanisms are applied and built from scratch in OpenMined's libraries, along with resources on how to use Differential Privacy for use cases such as Deep Learning, and integrate it with Federated Learning.
OpenMined offers many roles for leadership and helps build management and communication skills. Due to its open nature, newcomers are encouraged to pursue their own projects and create teams to collaborate on them.
Overall, you learn about the various facets of secure and private AI and learn to truly appreciate the breadth of privacy-preserving technologies being created at OpenMined.
Once you've learned these skills, you may be ready to join a team at placements.openmined.org
So... How about it? I'm going to PriCon2020 Are you?
I'm going because I can't wait to hear all about the new advances in Privacy Preserving Machine Learning and meet all the cool people there!
For more info on how to get involved, join us at the "Where do I fit in?" session at #PriCon2020 on Sunday (Sept 27 - 2:00pm UTC).
For registration & your LOCAL times please see: agenda link.