Dealing with Latent Pre-exposure to Information Treatments
Abstract: In Social Sciences, many experiments rely on responses to information treatments. Experimental subjects in the treatment group receive some information that subjects in the control group don't. Often, the proportion of people in the treatment and control groups who were pre-exposed to the information is unknown and uncontrolled by the researchers. If that pre-exposure...
Machine Learning Guided Modeling of Ligand-Protein Binding Energy Landscape: Applications in Small Molecule and Protein-based Drug Design.
Abstract: Molecules in cells constantly move. The motions of proteins in living cells can be simple fluctuations or functional. Therefore, investigating protein dynamics is crucial for understanding protein function and for accurately compute ligand-protein binding free energy landscape. Because experimental structures are static conformations, classical or enhanced molecular dynamics (MD) simulations are commonly used for...
Some Thoughts on Data Science - Population Health Collaborations.
Abstract: Data science involves the application of knowledge from the fields of computer science (on how to manage data) and statistics (on how to analyze data) to solve theoretical and practical problems. The field of population health involves investigation of health outcomes, patterns of health determinants, and policies and interventions that link them (Kindig and...
Remote Sensing of plant and soil for precision agriculture
Abstract: Agricultural systems are often characterized by high spatial and temporal variability in the factors that determine crop yield. In particular, the variability of soil and other environmental factors affecting yield are notoriously hard to characterize at very high spatial resolution. Recent high-resolution satellites (e.g., Sentinel and PlanetScope) may be useful tools for monitoring crops...
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...