Abstract: Electric power systems have taken drastic advances in deployment of new sensor technologies and communications infrastructure to support opportunities for new data availability in the field of smart grids. New measurement devices are developed and installed at scales in forms of advanced metering infrastructure, distributed energy resources monitoring systems, and high-resolution time-synchronized wide-area monitoring...

ABSTRACT: Statistical methods often involve solving an optimization problem, such as in maximum likelihood estimation and regression. The addition of constraints, either to enforce a hard requirement in estimation or to regularize solutions, complicates matters. Fortunately, the rich theory of convex optimization provides ample tools for devising novel methods. In this talk, I present applications...

ABSTRACT: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work...

ABSTRACT: Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is...

ABSTRACT: To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This talk presents LAMBRETTA, a system that automatically...

Abstract: Digital information systems have become central to the practice of medicine over the last twenty years, and the next twenty years will bring even greater changes challenges, and benefits for physicians, patients, and health systems. While trends in AI, Big Data, and Digital Health indicate major changes on the horizon, health informatics platforms that...

Abstract: Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has been improved to the point that nanoscale imaging of whole cell and even larger biological samples can now be reliably obtained. The science bottleneck now moves to processing these images to extract insights and knowledge. Librarians at UCR and Virginia Tech are working with microscopy and...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...