Jiajun Song
Research Engineer at BIGAI. Previous @PKU Math @Duke Stats
Starting as an applied statistician, I have developed a keen interest in challenging inference problems in the real world. There is rarely a clean setup, and novel and complex issues consistently emerge in the data, such as spill-over spatial effects, feedback loop in the system, network interference and so on.
Meanwhile, in an era when more people come to foundation models for their excellence at prediction, obtaining interpretability and effective model inference poses a formidable challenge.
My current interests lie in
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(Causal) Inference framework in complex settings, such as networks or correlated data.
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Analyzing foundation models with statistical methods, such as high-dimensional statistics.