Every summer the UBC Engineering Physics ENPH 253 course drops four-person teams into an intense six-week sprint: design and build a fully autonomous rescue robot that frees stranded “pets” (plushies) and returns them to a safe zone. Our Rolling Ohms bot line-tracked around the course, scooped friendly pets with a servo arm, and flung hostile ones with TPU flywheels spinning at ~50 km/h tips to clear an eight-foot arc.
[Read More]At AltaML, I spent several months immersed in impactful machine learning projects, tackling real-world challenges at the intersection of data science, software engineering, and public infrastructure. Below, I reflect on some key challenges and insights from my experience.
My first major project involved information classification for a large enterprise client with strict regulatory requirements around document retention. The core challenge was classifying documents with incomplete or partial textual information, often with an extremely limited dataset for training and validation.
[Read More]Below is a copy of a speed talk that I completed at TRIUMF in summer 2024 as a part of the TRIUMF Science Week. If I get a video of the presentation I will be sure to upload it!
Particle reconstruction in the ATLAS detector is the practice of associating calorimeter and tracker signals particle and determining which particles caused them, with how much energy, and through what process. One crucial step in reconstruction is cell segmentation where we determine which calorimeter signals belong to which tracks from the inner tracker. Current methods to perform segmentation rely on algorithms that grow with quadratic complexity, posing a challenge for segmentation after the High-Luminosity LHC (HL-LHC) upgrade, which promises a significant increase in the number of collisions per event and, therefore, the complexity of each event. Working under Dr. Max Swiatlowski, I am applying PointNet and other machine learning approaches to cell segmentation in the ATLAS calorimeter.
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