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Currently, I am dealing with various single-cell transcriptomic datasets available within the group (e.g- datasets of colorectal cancer and, brain tumor, etc.). Analysis of these datasets could provide insight into disease pathogenesis, molecular heterogeneity, and therapeutic responses. However, technical biases inherent to each batch of transcriptomic profiles present a unique problem when analyzing data generated from different studies. I am willing to improve the robustness of these data analysis procedures using novel machine learning approaches.