SEQAM – Sequence Analysis and Modeling Lab conducts research in computational modeling methods for analysis of multimodal and multivariate data, with particular focus on sequential data, such as time-series (video, audio, motion sensors), biomedical data (imaging, genomic, proteomic, etc.) We develop mathematical and statistical models and the accompanying algorithms that can rapidly process large and heterogeneous datasets and reveal the underlying, latent factors affecting the data.
Lab Members
Faculty
Students
Gang Qiao
Alumni
Pai-Hsi Huang
Rui Huang
Minyoung Kim
Kooksang Moon
Pavel Kuksa
Wang Yan
Xiaoye Han
Andre Cohen
