Scholar
Jakub Grudzien Kuba
Google Scholar ID: wMjQdBcAAAAJ
UC Berkeley
Citations & Impact
Citations
1,587
H-index
9
i10-index
9
Publications
13
Co-authors
5
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- No specific information provided about publications, awards, or patents.
Research Experience
- At Berkeley, he works on offline model-based optimization, developing algorithms that enable optimization of arbitrary objects entirely with machine learning. These technologies have applications in protein optimization, drug discovery, and chip design. At Meta, he focuses on improving reinforcement learning algorithms in scenarios with limited training data. In the summer of 2024, he interned at Squarepoint Capital as a quantitative researcher.
Education
- He graduated from Staszic High School in Warsaw, Poland. He did his undergrad at Imperial College London, UK, in Mathematics with Mathematical Computation, where he worked with Yaodong Yang on multi-agent reinforcement learning and game theory. He completed his MSc in Statistics at the University of Oxford, working with Jakob Foerster on reinforcement learning theory and meta learning.
Background
- He is a 4th-year PhD student in AI at UC Berkeley, advised by Sergey Levine and Pieter Abbeel. His research interest lies in the interplay of reinforcement learning and generative modeling. He is also a student researcher at Meta.
Miscellany
- He has a strong interest in quantitative finance.
