About me
I am an applied scientist at Microsoft in the 
MSAI organization, where I work on improving Copilot experiences across M365. My focus is improving personalization in search relevance scenarios by integrating approaches from graph representation learning, natural language processing, and information retrieval.
Previously, I completed my MSc in Machine Learning at Mila as part of the Complex Data Lab under the supervision of Professor Reihaneh Rabbany. I graduated with high distinction from the undergraduate honors computer science program at Carleton University in Ottawa, Canada, where I worked with the wonderful Professor Majid Komeili and TA’d for courses like Introduction to Web Development and Storytelling with Data.
I also contribute to the Refugee Law Lab in a volunteer capacity, helping lead data science efforts supporting the Refugee Law Lab portal by building scalable pipelines for scraping, parsing, and analyzing court records using open-source software & NLP methods.
Updates
- December 2023: Our work, Temporal Graph Benchmark for Machine Learning on Temporal Graphs was presented at NeurIPS 2023! This was an incredible collaborative effort led by Shenyang Huang.
- July 2023: I joined Microsoft full-time as part of the Graph Intelligence Sciences team in MSAI, where I’ll be working on search personalization & improving grounding data for LLMs such as M365 Copilot.
- May 2023: Fast and Attributed Change Detection on Dynamic Graphs with Density of States appeared at PAKDD 2023.
- April 2021: Our work, The Surprising Performance of Simple Baselines for Misinformation Detection, was accepted to WWW 2021.
- November 2020: Our short paper Detecting Informative COVID-19 Tweets by Attending over Linked Documents appeared in the W-NUT workshop at EMNLP 2020.
- September 2020: I wrote a tutorial on document ranking with AllenNLP.
- May 12, 2020: Here are the results of my graduate school applications as well as some supporting materials, for anyone who feels unsure about the process like I did. (repo)
- 2019:
- August: My work, Trouble with the Curve: Predicting Future MLB Players Using Scouting Reports, was a finalist in the reproducible research competition at the the 2019 Carnegie Mellon Sports Analytics Conference.
- July: I completed my second internship at Microsoft (MSAI), using character-level RNNs to extract meeting suggestions from Outlook emails. This was part of a larger project for which we were awarded patent #US20210073293A1, Composing rich content messages.