About
Hi, I'm Nahal — a soon-to-be Waterloo graduate working at the intersection of machine learning, intelligent systems, and computational neuroscience.
how I got here
I started in applied ML and software through co-ops, building systems in fast-paced environments and getting drawn toward the places where models meet the real world: noisy data, constrained systems, ambiguous objectives, and users who do not behave like clean benchmarks.
Over time, I became less interested in treating models as black boxes and more interested in the internal story: how intelligent systems represent information, adapt over time, make decisions under uncertainty, and fail when the world shifts underneath them.
That curiosity eventually pulled me toward computational neuroscience and brain-inspired AI — not as a departure from engineering, but as another way to think about building more adaptive, robust, and interpretable systems.
what I'm building toward
My work spans ML engineering, agentic AI evaluation, robotics, biosignal systems, and computational neuroscience research. I enjoy problems where the model is only one part of the system — where infrastructure, reliability, feedback loops, evaluation, and human behavior all matter just as much as the model itself.
Recently, I've been especially interested in frontier engineering problems: building reliable AI systems, evaluating agentic behavior, designing adaptive decision-making systems, and exploring how ideas from neuroscience can inform the next generation of intelligent systems.
I write on my blog about AI, systems, computational neuroscience, and the questions I'm still trying to think through.
— N🌱