Anna O.
About Anna O.
Anna Onstad-Hargrave is a Talent Operations Specialist and PhD student at Stanford, soon to be an assistant professor at Princeton, specializing in the intersection of machine learning and systems.
Title
Anna Onstad-Hargrave is a Talent Operations Specialist.
Education and Expertise
Anna Onstad-Hargrave is currently a PhD student at Stanford University. She is co-advised by Stefano Ermon and Chris Re, both recognized experts in the field. Her research focuses on the intersection of machine learning and systems, highlighting her specialized knowledge in efficient training and long-range context management. Anna is poised to become an assistant professor at Princeton next year, underscoring her academic achievements and expertise.
Research Focus and Interests
Anna Onstad-Hargrave’s research agenda revolves around creating high-performing language models using diverse approaches. She has demonstrated that sparsity can be made hardware-friendly without sacrificing quality. Her work suggests a future shift towards focusing on efficient inference in machine learning. She aims to understand the applicability of attention mechanisms and to explore potential alternatives. Her contributions have included references to MLPerf benchmarks and significant papers such as 'A Kernel Theory of Modern Data Augmentation' and 'FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.'
Collaborative Work and References
Throughout her academic career, Anna Onstad-Hargrave has collaborated with various notable professionals in the machine learning community, including Dan Fu, Albert Gu, and Phil Wang. She has also mentioned working with Young-Jun Ko from Inflection. Her research references include insights from Steven Boyd of Stanford University, further emphasizing her engagement with the academic and professional communities in her field.