PhD
Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU
Akademiestr. 7
80799 München
Biosketch
Sarah has been thinking a lot about randomized algorithms and stochastic processes. She suspects that making progress in machine learning will require us to embrace its randomness, or at least get more comfortable with it. Tentatively the destination is interpretable models running on low-power edge devices for medical diagnostics and/or assistive technologies. She is always happy to hear about new ideas on how to get there.
Sarah is a PhD student at the Bavarian AI Chair of Mathematical Foundations of Artificial Intelligence, advised by Prof. Dr. Gitta Kutyniok. Before this, she worked as a research assistant at NYU Abu Dhabi. Before that, she did an MS in Computer Science at NYU Tandon School of Engineering. Before that, she studied Studio Art at NYU Steinhardt. She still likes working in pastels, writing, ceramics, and photography, but her primary medium is now equations.
relAI Research
On the hegemony of GEMM
In my research, I want to question the hegemonic dominance of the multiply-accumulate in favor of nonlinearity and inexactitude. I conjecture that the best way to advance computational power is by designing new forms of hardware, and that better models will come not only from smarter use of the data we have, but from making new kinds of data altogether. The desired societal impact is twofold: reducing the energy cost of computation; and improving health outcomes through novel diagnostic modalities, drug design, treatment procedures, and assistive devices.