Machine learning at scale
“All the valuable practical skills that I learned at Stanford have put me in an ideal position to pursue my dream of making the world a better place to live in,” said Soheil Esmaeilzadeh, PhD ’21.
While at Stanford, Esmaeilzadeh was a student in the energy resources engineering and computer science departments. He learned to combine computational science, direct numerical modeling, machine learning and deep learning to solve challenging engineering problems.
“I truly feel privileged that I made it to Stanford and I will always be grateful for all the valuable experiences I had here,” said Esmaeilzadeh, who received the Stanford Graduate Fellowship in Science & Engineering that supports exceptional doctoral students. Esmaeilzadeh, who grew up in Tehran, Iran, earned a BSc at University of Tehran, a MSc at ETH Zurich, and finished his master’s thesis at UC Berkeley before attending Stanford.
Following graduation, Esmaeilzadeh began a role as a Machine Learning Scientist at Apple. “I’ve stepped into an industry where I can apply my knowledge and expertise to real-world problems that have huge impacts on people’s lives and their futures,” he said. “I’m so excited to contribute to top-notch projects where machine learning and AI intersect with high-performance computing.”
As an international student who has studied and worked across the globe, Esmaeilzadeh understands just how far Apple’s reach extends. “The fact that my daily contributions at Apple have a worldwide impact and can affect millions of lives is very exciting and fulfilling to me.”