Research Engineer · Google DeepMind
Ph.D. in Electrical Engineering · Reinforcement Learning & Foundation Models
I am a Research Engineer at Google DeepMind in Mountain View, CA. My work centers on reinforcement learning, post-training, and foundation models. I investigate novel uncertainty estimation and exploration paradigms for Large Language Models (LLMs), designing methods that improve model reasoning, data efficiency, and reliability in complex environments.
Prior to Google DeepMind, I spent a year at Uber Technologies, where I worked on dynamic pricing and cost estimation models.
I received my Ph.D. in Electrical Engineering from the University of Southern California (USC), alongside concurrent Master's degrees in Computer Science and Applied Mathematics. Prior to USC, I completed dual B.S. degrees in Electrical Engineering and Computer Science at Sharif University of Technology.
My work focuses on advancing the core capabilities of large language models through reinforcement learning and post-training. I develop novel exploration algorithms to dramatically improve data efficiency, applying these online RL techniques across complex downstream domains such as code generation and reasoning.
Worked on the Dynamic Pricing team, developing real-time trip cost prediction and marketplace pricing algorithms across high-throughput global traffic.