Machine Learning

Finding hidden patterns in messy data.

Estimating wildfire risk from remote-sensing data

Over the last few decades, wildfires have become a massive problem, with longer fire seasons and larger fires each year. Assessing the fire likelihood — the probability of wildfire burning in a specific location — is critical for forestry management, disaster preparedness, and early-warning systems.

Traditionally, we estimate wildfire likelihood by modeling fire behavior across simulations by varying parameters, which can be time-consuming and computationally-intensive. Instead of using simulations, I implemented and trained deep neural networks to predict the occurrence of wildfires from remote-sensing data, using data from historical wildfires. I demonstrated that these models can successfully identify areas of high fire likelihood from aggregated data about vegetation, weather, and topography.

I will be presenting this work at the NeurIPS 2020 Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop.

Python TensorFlow Earth Engine

Training a neural network to find more earthquakes than ever before

When we think of earthquakes, we think of the ground shaking. But there exists an entirely different type of earthquake, much smaller in amplitude, similar to a repeating rumbling: These tiny repeating earthquakes are called tectonic tremor.

Since tectonic tremor repeats regularly, it is valuable for mapping the precise location of faults. However, it is near the noise level and thus difficult to detect reliably. It is usually detected by looking for a match using carefully crafted reference templates.

Using a catalog of more than 1 million tremor events detected along the San Andreas Fault for over 15 years, I created a novel approach to tremor detection with a convolutional neural network. I demonstrate that this methodology can detect tremor of very low signal amplitude, even below the background noise level, without prior templates.

Python C++ TensorFlow

Tracking cars using telecom fiber

With the development of autonomous vehicles, there is an increasing need for sensors detecting motion in the cameras' dead angles, especially in urban areas.

At Stanford University, I demonstrated that it is possible to repurpose telecom fiber-optic cables — the same as for high-speed internet — to track vehicle traffic. This is done by sending a laser pulse through the fiber and measuring the backscattered energy. As moving objects make the fiber underground vibrate, it shows up in the recorded data.

To extract this data's full value, I developed new signal processing algorithms for vehicle tracking, combining high-performance computing and deep learning. Since the data are solely based on vibrations, this process is entirely anonymous.

Python C++ TensorFlow