This is a page to keep track of the talks I’ve done, both in industry and internally. The slides for my talks can be found on my GitHub. My talks are mostly TED talk styled. That is, I use slides as visual cues to help me remember the story I’d like to tell, and to also engage the audience. If your story is strong, you barely need any slides! It’s a style I learned from my supervisor, Prof. Jianping Gan, during my Master’s degree.


conference presentation
IEEE AeroConf 2021
Algorithmic Justice: AI and Ethics for Newcomers to ML (Panelist)
December 15, 2020
Machine Learning in Financial Markets: Pitfalls and Solutions (Take 3!)
Data Science Conference Europe
November 18, 2020

Highly Recommended: A Fireside Chat with AISC’s Resident Experts on Recommender Systems (Panelist)
October 6, 2020

Machine Learning in Financial Markets: Pitfalls and Solutions (Take 2!)
AI Geeks
May 20, 2020

Speaker and Panelist
99 AI Challenge, University of Toronto
March 23, 2020

Machine Learning in Financial Markets: Pitfalls and Solutions
Womxn in Data Science Toronto
March 2, 2020

Diversity and Inclusion Panel Guest

December 3, 2019

Speaker and Panelist
Her Code Camp
September 14, 2019

Introduction to Data Science and Machine Learning
June 4, 2019



Delphia has a Data Science Speaker Series where we get to share what we’ve learned with the rest of the team. I’ve spoken at it twice: I spoke about some research on a project called SessNet, and I also presented my Introduction to Data Science talk a few weeks into my time at Delphia.

We also have experimented with a Financial Learning Series that I initiated, where we read various finance books and then present our learnings, as an effort to share out knowledge to the rest of the team. I spoke several times: I spoke about the book Common Stocks & Uncommon Profits, as well as how to value companies using comparative company analysis.


I ran a lunch and learn series while working at Mercatus. The idea was to help the company become more data-driven. There were 4 lessons in the series:

  1. Introduction to Data Science
  2. Case Studies and Code
  3. Introduction to the Mathematics of Machine Learning
  4. Exploratory Data Analysis (Hands on)I planned to do a 5th lesson, where we create a model based on the EDA work done in lesson 4. I never did that 5th lesson.