Gal Oren

Visiting Scholar, Stanford University
Visiting Assistant Professor, Computer Science, Technion

AI for Science and Systems

Today, my research sits at the intersection of artificial intelligence, natural science, and computing systems. In AI for Science, I develop AI methods in collaboration across scientific fields, especially materials, physics, and biology. In AI for Systems, I study how AI models and agents can work on code, parallelism, performance, and the hardware-software stack itself.

Before AI reshaped all of that, scientific computing was a loop: design the code or the system around a scientific question, run it, port it to new hardware, tune performance and data movement, and then go back to the simulations, the instruments, the data, and the scientists around the problem. That is where I came from, through scientific computing, high-performance computing, and distributed systems. Over the last decade, AI changed that loop, and that is what led me toward these two directions.