About Me

Hi I am Nayan, an undergraduate student studying Statistics, Computer Science and Mathematics at the University of Toronto. I am currently interning as a data scientist with the artificial intelligence team at Bell Canada, and working on better understanding human function learning using Gaussian processes at the Computational Cognition Lab supervised by Prof. Daphna Buchsbaum (UofT/Brown University) and Prof. Chris Lucas (University of Edinburgh). I am also a researcher at the ML Collective where I am working on Automated Machine Learning (AutoML), specifically projects pertaining to Neural Architecture Search.

I have previous experience briefly running monte-carlo simulations on the Niagara supercomputer cluster and working on the development of an R package for a journal manuscript on clinical trials under the supervision of Prof. Jessica Gronsbell (UofT) and Zack McCaw (Google). I was also a Research Assistant at the Intelligent Adaptive Interventions Research Group, where I conducted research on Statistical Inference for Multi-Armed Bandit Algorithms (RL) supervised by Prof. Joseph Jay Williams, and have worked on leveraging data to analyse political, social, and economic impact on social distancing behaviour in the United States supported by the University of Toronto COVID-19 Student Engagement Grant.

    Teaching Assistant:
    • STA130: Introduction to Statistical Reasoning and Data Science (Winter 2022)
    • STA302/1001: Methods of Data Analysis I (Fall 2021)
    • STA247: Probability with Computer Applications (Fall 2021)
    • STA130: Introduction to Statistical Reasoning and Data Science (Fall 2020)

Reviewer : Workshop on Meta-Learning (MetaLearn 2021) @ NeurIPS 2021

Other Affiliations : FOR.ai , UofT Artificial Intelligence Group & UofT Machine Intelligence Student Team

Nayan Saxena


Neural Architecture with Few Bad Operations Towards One Shot Search Space Poisoning in Neural Architecture Search (Student Abstract)
Nayan Saxena, Robert Wu & Rohan Jain
In Proceedings of the 36th AAAI Conference on Artificial Intelligence, Vancouver, BC,Canada, 2022

Neural Architecture Type System NeuralArTS: Structuring Neural Architecture Search with Type Theory (Student Abstract)
Robert Wu, Nayan Saxena & Rohan Jain
In Proceedings of the 36th AAAI Conference on Artificial Intelligence, Vancouver, BC,Canada, 2022
Preprint | Code

NSL in Practice 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

Neural Architecture Search (NAS) Search Space Poisoning the Search Space in Neural Architecture Search
Robert Wu*, Nayan Saxena* & Rohan Jain* (*equal contribution)
Workshop on Adversarial Machine Learning, 38th International Conference on Machine Learning, 2021
Long Preprint | Workshop Paper | Poster | Code

Bandit Optimisation Statistical Consequences of Dueling Bandits
Nayan Saxena, Pan Chen & Emmy Liu
Workshop on Reinforcement Learning for Education, 14th International Conference on Educational Data Mining , Paris, France, 2021
Paper | Preprint | Spotlight Talk | Code

Gaussian Process Picture 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

sparse_matrix_student IRT++: Improving Student Response Prediction With Gaussian Initialisation and Other Modifications
Nayan Saxena, Varun Lodaya & Trisha Thakur
In Proceedings of the 21st IEEE International Conference on Advanced Learning Technologies, Tartu, Estonia, 2021
Paper | Code

texas_social_distancing 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)


COVID-19 & Crime Dashboard

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.

Get in touch

If you found any of my work interesting, I'd love to chat ! Feel free to reach out by emailing me directly: nayan.saxena@mail.utoronto.ca