Bilge Acun

Menlo Park, United States

Bilge Acun is a Research Scientist at Facebook AI Research (FAIR). Her research interests include Systems for Machine Learning, Parallel and Distributed Computing, Energy Efficient Computing. She is working on making large-scale machine learning systems fast and efficient. Particularly, she works on two optimization areas to improve the system throughput and efficiency:System and hardware optimizations using accelerators i.e. GPUs / TPUs and Algorithmic methods such as tensor compression.

Bilge received her Ph.D. degree in 2017 at the Department of Computer Science at Universtiy of Illinois at Urbana-Champaign. Her dissertation received the 2018 ACM SigHPC Dissertation Award Honorable Mention. Before joining Facebook, she worked at the IBM Thomas J. Watson Research Center as a Research Staff Member.

Research Areas

Bilge's Publications

May 19, 2021

SYSTEMS RESEARCH

TT-REC: Tensor Train Compression for Deep Learning Recommendation Model Embeddings

Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu

May 19, 2021

February 27, 2021

SYSTEMS RESEARCH

RANKING & RECOMMENDATIONS

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood

February 27, 2021