My ultimate aim is superintelligence, and its acceleration of scientific discovery by performing the entire research process autonomously. Imagine the work of tens of thousands of scientists and engineers, amplified to incomprehensible magnitudes, and completed in a fraction of the time it would take us humans. A future of exponential progress, all watched over by machines of loving grace.
My journey in machine learning began when my infant sister was diagnosed with stage four cancer. This led to the creation of Lung AI, a deep learning model that detects and classifies lung cancer. Lung AI won four hackathons and contributed to my 11 total hackathon wins.
This work earned me recognition as one of the youngest MLH Top 50 Hackers out of 150,000+ hackers worldwide.
Following this, I interned at Deep Genomics as a machine learning engineer, working at the bleeding edge of AI and genomics.
After that, I worked on machine learning research at NVIDIA and the Vector Institute, specifically in robotics, reinforcement learning and mechanistic interpretability, addressing the challenge of enabling autonomous systems to exhibit agency and make intelligent decisions.
Currently, I'm working on fast, distributed machine learning inference for image and video models at Tenstorrent. I have a lot of fun designing and implementing models and inference systems that run across dozens of chips in diverse cluster topologies, scaling efficiently toward hundreds of accelerators.
In addition, I am a cellist and enjoy making music with others (1, 2). As well, I'm bilingual in English and French.