Ping Li

Experience

1. Distinguished Engineer

   Linkedin

   Jun 2022 — Present

   https://pltrees.github.io/ 1. Advertising (ads) 2. Privacy: cohort-building, differential privacy (DP), federated learning 3. Embedding based retrieval (EBR) Technical reports (publications): 1 Privacy:(1) SIGIR 2023: ads privacy using cohort-building and CWS : https://arxiv.org/pdf/23•••••77.pdf (2) ICML 2023: compression for federated learning: https://proceedings.mlr.press/v202/li23o/li23o.pdf (3) differential privacy (DP) based on random projections and quantization: https://arxiv.org/pdf/23•••••51.pdf (4) differential privacy (DP) for sparse data, based on CWS and minhash: https://arxiv.org/pdf/23•••••74.pdf 2 Embedding:(5) Ads audience expansion, HNSW for sparse data, chi-square two-tower model: https://arxiv.org/pdf/23•••••07.pdf (6) KDD 2023: Fundamental data structure for embedding compression, privacy, and big models: https://pltrees.github.io/publication/KDD_2023_OPORP.pdf Big (large parameter) models:(7) deep feature interactions using block-wise permutations: https://arxiv.org/pdf/23•••••81.pdf (8) Pb-Hash for large-parameter models (QR-hash is a special case): https://arxiv.org/pdf/23•••••44.pdf Talks in the public domain: 8/15 on boosting and trees: 8/30 on embedding-based retrieval: Past position: Deputy Dean of Baidu Research: Relevant documentations in the public domain: 1. Recruiting document in 2021 (in Chinese): https://zhuanlan.zhihu.com/p/38••••••32. Baidu Wiki (Baike) entry (in Chinese) https://baike.baidu.com/item/%E6%9D%8E%E5%B9%B3/23••••92 Past Position: Rutgers University Summary by for \"machine learning\"+\"information retrieval\"(for 20••••22) https://drive.google.com/file/d/15-YmRW3cdUCCjXYdMG23z7xRetfk9mCh/view?usp=drive_link Past position: Cornell University ONR-YIP AFOSR-YIP