Kumar Krishna Agrawal
graduate student at eecs, berkeley. previously research@google, math/cs at iit
kharagpur
research: empirics, theory of learning algorithms in interactive, stochastic environments;
building efficient, scalable real-world systems. some recent themes include:
- measuring representation quality for model selection in self-supervised learning
- offline reward modeling, reinforcement learning with limited, suboptimal data
- algorithms for low-latency inference in ml pipelines
- structured and compositional generative models (for code, images, audio)
previously, i've worked on fast, simple algorithms for off-policy robot learning, generative models for music, program synthesis.
opportunities: if you are at berkeley and interested in ml/systems research; please reach out!
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