Machine Intelligence + Statistics
Here is a video of me generating the cover image
I am a recent graduate from the University of Toronto where I did an undergraduate degree in Statistics, Computer Science and Mathematics. Presently, I am working as a data scientist with the Chief Data Office at the Royal Bank of Canada and also am a machine learning engineer at WOMBO.ai.
Furthermore, I am a researcher at the ML Collective where I am working on Automated Machine Learning (AutoML), specifically projects pertaining to Neural Architecture Search.
I formerly interned as a data scientist with the artificial intelligence team at Bell Canada and have previously worked on several other research projects covering topics such as multi-armed bandit algorithms, clinical trials and population dynamics during COVID-19.
More information can be found in my CV here .
Sign-to-Speech Model for Sign Language Understanding: A Case Study of Nigerian Sign Language
Steven Kolawole, Opeyemi Osakuade, Nayan Saxena & Babatunde Olorisade
Workshop on Machine Learning for the Developing World, 35th Conference on Neural Information Processing Systems (NeurIPS), 2021
Preprint | Oral Talk | Code
Sampling Heuristics for Active Function Learning
Rebekah A. Gelpi, Nayan Saxena, George Lifchits, Daphna Buchsbaum & Chris Lucas
In Proceedings of the 19th International Conference on Cognitive Modelling, 2021 Abstract in proceedings of the 43rd Annual Meeting of the Cognitive Science Society, Vienna, Austria, 2021
Paper | Poster | Talk
Political and Socioeconomic Influences on Social Distancing Behaviour in the United States
Liam Keating, Nayan Saxena, Emma Cooper, Jordan Tirico, Daniel Khain & Daphne Imahori
SocArXiv, 2020 (doi:10.31235/osf.io/emrtj) Paper
A dashboard focused on COVID-19 and US Crime data that makes future predictions on crime rates across Seven US Cities using Vector Auto Regressive Models and provide crime estimates before and after onset of the pandemic to gain insight into the effect of self-isolation measures on human behavior. This project won the Gold Prize for Best Dashboard based presentation out of 42 competing teams at the American Statistical Association Datafest.
A binary classifier between open and closed eyes that seamlessly converts eye-blinking data into Morse code and finally speech, trained and presented at Hack The North 2019 (University of Waterloo). The project uses Keras with a Tensorflow backend to convert real-time blinking data to speech by translating data input into Morse code and further decrypting data into audio output and keystrokes.
I also like taking pictures, travelling and going on adventures. Here are some curated pictures from my Instagram.
If you found any of my work interesting, I'd love to chat ! Feel free to reach out: email@example.com.