Illuminating metabolomics dark matter - Reshaping how to mine and reuse big mass spectrometry data for small molecule discovery
Abstract: High-throughput mass spectrometry has enabled unprecedented depth and versatility to observe the molecules in the world around us. Traditionally, a handful of molecules were detected in a typical measurement. Today, this has grown to thousands of molecules in a few minutes. The growth in data presents new opportunities for discovery but also challenges in...
The Age of Creative AI ?
Abstract: Generative models have made significant advances in recent years, sparking an explosion of new applications with far-reaching societal implications. I will discuss the mathematical intuition behind diffusion models, the core technology behind recent art generation tools like DALL-E-2, Imagen, Stable Diffusion, Dreambooth, Lensa, and others. These applications introduce new technical challenges both for computational...
Are hallucinations in text generation always undesirable? A perspective from text elaboration
Abstract: Recent developments in deep learning have led to exponential improvements in Natural Language Generation (NLG), particularly in terms of fluency and coherency. On the other hand, deep learning-based text generation is also susceptible to hallucinating unintended text that is not directly supported by the source document. These unsupported texts are called hallucinations and are...
How to survive Google taking over your research field and (perhaps) thrive
Abstract: Two years ago, Google team made an incredible advance in structural biology, practically solving the protein folding problem (predicting protein structure from its amino acid sequence). The AI-based AlphaFold algorithm was shown to produce protein models comparable in quality to the experimental ones. It shaked up the field of structural biology, which now must...
New Regression Model: Modal Regression
Abstract: Built on the ideas of mean and quantile, mean regression and quantile regression are extensively investigated and popularly used to model the relationship between a dependent variable Y and covariates x. However, the research about the regression model built on the mode is rather limited. In this talk, we propose a new regression tool...
Scalable Privacy-Aware Collaborative Learning
Abstract: Privacy-preserving collaborative learning allows multiple data-owners to jointly train machine learning models while keeping their individual datasets private from each other. The main bottleneck against the scalability of such systems to a large number of participants is their communication cost. In this talk, we will introduce novel distributed training frameworks that can achieve scalability...
Functional Ultrasound Imaging (fUSI): A game changer in neuroscience and medicine
Abstract: Recent advances in neuroimaging technology have significantly contributed to a better understanding of human brain organization, and the development and application of more efficient clinical programs. However, the limitations and tradeoffs inherent to the existing techniques, prevent them from providing large-scale imaging of neural activity with high spatiotemporal resolution, deep penetration, and specificity in...
Statistical methods for analyzing and comparing single-cell gene expression data
Abstract: Single-cell RNA sequencing (scRNA-seq) experiments enable gene expression measurement at a single-cell resolution, and provide an opportunity to characterize the molecular signatures of diverse cell types and states in tissue development and disease progression. However, it remains a challenge to construct a comprehensive view of single cell transcriptomes in health and disease, due to...
Why 95% of papers on Time Series Anomaly Detection are Wrong (with more general lessons for Researchers).
Abstract: Time Series Anomaly Detection (TSAD) is the task of monitoring a time series, say an ECG, or the pressure in an industrial boiler, while attempting to recognize when there has been an anomalous event. The anomalies could be the beginning of heart attack, or a leak in the boiler that will cause the industrial...
Estimation and Sensitivity Analysis for Causal Decomposition: Assessing Robustness Toward Omitted Variable Bias
Abstract: A key objective of decomposition analysis is to identify risks or resources (‘mediators’) that contribute to disparities between groups of individuals defined by social characteristics such as race, ethnicity, gender, class, and sexual orientations. In decomposition analysis, a scholarly interest often centers on estimating how much the disparity (e.g., health disparities between Black women...