Using Stochastics and Machine Learning
Harish Bhat, associate professor of mathematics, likes to
tackle difficult problems and find solutions that help people, especially if he can use novel approaches in math or computer science. “I got into stochastics and machine learning because I enjoy solving real-world problems,” he said, “and finding solutions often involves using models and algorithms from these areas.” He recently joined the U’s Math Department after teaching for a decade at the University of California, Merced. “When I joined UC Merced,” he noted, “it was still the newest campus in the UC system, so it felt a bit like a startup—there was always plenty to do besides the usual teaching and research duties.” In addition to teaching, Bhat served on faculty hiring committees and developed curricula for undergraduate and graduate students.
Algorithms and Forecasting Harish Bhat
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Initially he got into stochastics (an area concerned with random probabilities) and machine learning through the subprime loan financial crisis of 2008. He and another graduate student saw this focus as a way to develop a more accurate pricing model, and they began using machine learning that relied on using short-term memory models instead of the usual Black-Scholes-Merton model that uses long-term price variation. “We developed and used a large database of historical option prices to show that our model could outperform competing models, such as Black-Scholes,” said Bhat. Data collection played a huge role in both the development
