For more organizing activities and community-building, see events
NYU AI School, 2021-23
The event serves over 300 students annually and prioritizes those from underserved communities who lack institutional resources in artificial intelligence and machine learning. The lectures are augmented by carefully developed tutorials that lay emphasis on practical machine learning. We are generously funded by Genentech and DeepMind.
Knowledge Transfer: DJ Unicode
- Co-founder, 2017 - present, DJ Unicode
I am passionate about knowledge transfer actively working with a student-run organisation that I co-founded. Unicode was born of the need for skill development at the grassroots level in addition to the need for a rapport between college freshmen, sophomores, and juniors at universities that don’t offer such opportunities by means of the coourse structure. Our aim is to extend the ‘summer-of-code’ workflow to the rest of the year helping our students to build a strong foundational understanding of software development. I’m leading the expansion of our mentorship into teaching math and statistics for machine learning through comprehensive reading groups on standard texts in the subject.
Unicode started in 2017 with 15-20 students separated into 5 teams based on their projects. Today, we are a thriving community of 200+ members, with teams winning hackathons, students receiving international internship offers, multiple selections for Google Summer of Code each year, and alumni at Ivy League universities and FAANG companies in the USA!
Founder, 2020 - Present, Unicode Research Group
I received a generous grant from Google Research India to teach a (independent) 10-week Unicode ML Summer Course in Summer 2021 to students from Tier II and Tier III universities in India. Much obliged to my TAs from Unicode Research for their hard work and commitment!
- We had a fantastic Demo Day showcasing student projects at the Unicode ML Summer Course!
I founded a research arm within Unicode, focused on doing collaborative research in statistics and machine learning, with particular emphasis on AI for social good. This includes extensions of projects by students in our ML Summer Course, and ideas by Unicode students and collaborators. I was joined by Dr. Akash Srivastava from the MIT-IBM AI Lab to help teach the students about deep generative models in a palatable fashion, introducing them to probabilistic machine learning.
Ongoing research projects at Unicode Research include estimating the causal effect of mentorship on student career outcomes, social network analysis using probabilistic machine learning, and other topics in deep generative modeling.