Zack Ankner

I am a third year undergraduate student at MIT currently studying Computer Science and Mathematics. I am currently a member of Jonathan Ragan-Kelley's lab where I work with William Brandon. I was previously a member of the Programming Systems Group (PSG) led by Michael Carbin, where I was supervised by Alex Renda. I have also worked as a research scientist intern at MosaicML since the summer of 2022.

My research is aimed at improving LLMs through strong empirical investigation of simple modeling changes. I believe that there are many uncomplicated research ideas which yield consistent performance gains that are overlooked for more complex and intricate solutions that are often brittle and do not scale. In pretraining I have investigated LLM based data filtering techniques as well as scheduling masked autoencoder objectives for the purpose of improving trained model quality. For inference methods I have proposed adding sequential dependence to draft heads to improve speculative decoding performance. My current research interest and projects involve improving LLM performance using dynamic inference compute as well as training improved preference models. While not my current research focus, I have worked on generative 3D modelling , neural surrogates of symbolic programs, and the science of ML in the past.

You can find my resume here.