Disclaimer: This site is currently in a minimal state, information about the experiences/research/projects is still being filled out, if you are interested in the summary provided and would like to learn more feel free to reach out to me (email below)!

Hello, my name is Siddharth (Sid) Arya. I am a machine learning researcher/engineer. Currently, I am in my final year of a BS in Computer Science at the University of Toronto. Alongside courses on fundemntal principles of computer science, with a focus of machine learning, I have also had the pleasure of taking part in research as an undergrad. I have worked under the supervision of Prof. Rahul Krishnan at the Vector Institute on explaining model inference in the healthcare sector, as well as developing novel methods to monitor models post-deployment. I also worked with Prof. Marsha Chechik on creating requirment specifications for software systems with image classification models - to avoid model failure. Additionally, I am currently a member in the Dynamic Graphics Project Lab under the supervision of Prof Kyros Kutulakos working on processing algorithms for SPAD (single-photon-avalanche diode) cameras.

While I deeply enjoy research, I am eager to transition into a more applied setting where I can leverage my background in machine learning to solve real-world business challenges. I want to see firsthand how ML models impact decision-making, optimize processes, and drive innovation beyond academic settings.

Some highlights:

• Published a python package alongside peers at Vector to allow users to validate their models in a production scenario (unlabeled test data): pypi, Github_Repo
• Received a research grant from the Data Science Institute at UofT to conduct a benchmark study of contemporary data shift detection methods and presented research findings at the annual SUDS showcase: Linkdin_Post

Please reach out to me on my linkdin or email (‘sid19arya’ @ ‘gmail.com’) if you’d like to chat!

Research and Work Interests

I consider myself a novice in the machine learning field, my primary interest would be working with Excellent and Accomplished Researchers to learn from them!

Work I am interest in:

• Explainable AI
• Causal Inference : I believe that the path to generalizable and truly ‘intelligent’ models is through a causal understanding of the world
• Robotics
• Uncertainty Quantification

Beyond that some of the work I done so far:

• Explainable AI : understanding the way models behave that they do
• Guardrails for AI : how can we add automated supervision for knowing when AI models might start making mistakes
• Computational Imaging (new addition): how do camera’s capture images, how can we develop these image capturing capabilities in different contexts

Oh, and I also play the football (not the American version) and am learning the guitar.