About Me

I am a Machine Learning Engineer working on perception and safety systems for intelligent and autonomous vehicles. My work sits at the intersection of machine learning, signal processing, and probabilistic modeling, where the goal is to build systems that detect weak signals in noisy, real-world environments.

Much of my work focuses on extracting meaningful structure from imperfect data. This includes designing models that detect rare or ambiguous events — from acoustic systems that identify emergency vehicles beyond line of sight to statistical pipelines that detect anomalous patterns in large-scale transportation data. I am particularly interested in how machine learning and signal processing techniques can be combined to build perception systems that remain robust under uncertainty.

Previously, I was a Senior AI Engineer at Renesas Electronics, where I implemented deep neural networks on embedded automotive hardware. That experience shaped how I think about machine learning systems: models must not only be accurate, but efficient, interpretable, and reliable under strict compute and memory constraints. Much of my work involved adapting neural architectures and signal processing pipelines for real-time inference on edge devices and integrating them into production automotive systems.

Earlier in my career, I worked in applied research on early language development at the University of Chicago’s Thirty Million Words Center. As part of that work, I contributed to the development of one of the first automated parentese detection systems for the wearable LENA device — technology that analyzes day-long audio recordings to measure caregiver–child speech interactions and conversational turns. Parentese, the exaggerated and highly expressive speech style adults use when speaking to infants, has been shown to significantly improve early language development and conversational engagement.

I enjoy thinking about perception, probabilistic reasoning, and the mathematics behind machine learning algorithms. On this blog, I share ideas about these topics and explore the principles that make intelligent systems work. I especially enjoy explaining concepts through small examples that make the underlying intuition clear.

Outside of work, I spend most of my time playing basketball and soccer in local leagues.

I also love the Fourier Transform. If there was another way to think about this blog it would be various applications of the Fourier Transform.