In this talk, we discuss the paper "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters" by L. Xia et.al. Nature Communications, 2024. Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection …

In this talk, we discuss the paper "Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains" by K. Lee. Abstract The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features …

In this talk, we discuss the paper "Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics" by J. Thompson et al., bioarxiv, 2025. Abstract Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites …

In this talk, we discuss the paper "Decomposing causality into its synergistic, unique, and redundant components" by Álvaro Martínez-Sánchez et al., Nature Communications, 2024. Abstract Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference …

In this talk, we discuss the paper "SCassist: An AI Based Workflow Assistant for Single-Cell Analysis " by Vijayaraj Nagarajan et al., bioarxiv, 2025.  Abstract Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power …

In this talk, we discuss the paper "Tackling inter-subject variability in smartwatch data using factorization models" by Arman Naseri et. al, Scientific Reports, 2025. Abstract Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to …

In this talk, we discuss the paper "Simulating the Spread of Infection in Networks with Quantum Computers" by Xiaoyang Wang, Yinchenguang Lyu, Changyu Yao and Xiao Yuan, Physical Review Applied, vol.19, 064035 (2023). Abstract We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution …