I am an incoming AI resident at Google Brain Montreal. Starting in 2020, I will return to UC Berkeley for my PhD, from which I graduated with degrees in Computer Science and Applied Mathematics.
During my undergraduate career, I collaborated heavily with Sergey Levine in deep reinforcement learning, and with Ben Brown in interpretable machine learning. I also had the privilege to co-create Stat 140: Probability for Data Science in 2017, for which I served as the head teaching assistant several times.
My first name is pronounced Dibbo or Dih-bo. On legal documentation, I go by Dibya Jyoti Ghosh.
- I was chosen to receive the CS Major Citation and the Outstanding GSI Award.
- At ICLR 2019, I presented our work on actionable representations (ARCs) for reinforcement learning
- I was chosen as a finalist for the CRA Outstanding Undergraduate Researcher Award
- At NeurIPS 2018, I presented our work on Variational Inverse Control with Events (VICE).
- At PASC 2018, I gave a talk on interpretable generative models for multi-omics data in the Use of AI to Analyze Complex Biological Systems mini-symposium. Slides coming soon!
- At ICLR 2018, I presented our work on Divide-and-Conquer Reinforcement Learning.