Diya D
- Research Program Mentor
MEng at University of California Berkeley (UC Berkeley)
Expertise
Engineering, mechanical engineering, industrial engineering, engineering mathematics, computational engineering, project management, machine learning for mechanical engineering, theory of constraints, capacity planning, flow optimization
Bio
I'm an MEng mechanical engineering graduate from UC Berkeley, with a minor's in data science. Initially, my coursework started with applying machine learning algorithms to engineering problems, specifically within my concentration in fluid dynamics. Today, my work revolves around data analysis within the semiconductor industry. Some of my everyday tasks involve the managing production line, planning and meeting capacity targets, performing data analysis of tool performances, and optimizing scheduling and dispatching logic of the entire process flow. I'm a very social person, so when I'm not working, I like to hang out with my friends and plan activities, from hikes to cookouts. When it comes to unwinding, I'm usually indoors.Project ideas
Data analysis for prediction of machine performance
In a semiconductor factory (where computer chips are made), machines run almost nonstop to produce tiny, powerful chips used in phones, cars, and computers. But when a machine suddenly breaks down, it can stop the whole production line — kind of like when one broken link stops a whole chain. In this project, students will explore how data can help us predict when a machine might need maintenance before it actually fails. You’ll look at simplified data that represents how machines perform over time (for example, temperature, pressure, or run time). Then, you’ll learn how to find patterns that might signal a problem coming up — just like noticing your laptop slowing down before it crashes.