How could Neural Networks understand Programs? - Dinglan Peng; Shuxin Zheng; Yatao Li; Guolin Ke; Di He 001; Tie-Yan Liu (ICML, 2021)

Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding. - Shengjie Luo; Shanda Li; Tianle Cai; Di He 001; Dinglan Peng; Shuxin Zheng; Guolin Ke; Liwei Wang 001; Tie-Yan Liu (NeurIPS, 2021)

Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor. - Sishuai Gong; Dinglan Peng; Deniz Altinbüken; Pedro Fonseca 001; Petros Maniatis (SOSP, 2023)

μSwitch: Fast Kernel Context Isolation with Implicit Context Switches. - Dinglan Peng; Congyu Liu; Tapti Palit; Pedro Fonseca 001; Anjo Vahldiek-Oberwagner; Mona Vij (SP, 2023)

Pegasus: Transparent and Unified Kernel-Bypass Networking for Fast Local and Remote Communication. - Dinglan Peng; Congyu Liu; Tapti Palit; Anjo Vahldiek-Oberwagner; Mona Vij; Pedro Fonseca 001 (EuroSys, 2025)