PhD Candidate in Electrical Engineering at University of California, Los Angeles
How media typically covers Yasmine Badr
Yasmine Badr as author
Kunlun, a new scalable architecture for recommendation systems, doubles scaling efficiency and increases GPU utilization from 17% to 37% by introducing optimizations like Generalized Dot-Product Attention and Computation Skip, now deployed in Meta Ads.
“Author of "Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems throu" on arXiv”