The Proximal Distance Principle for Constrained Estimation
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...
Data-Efficient Deep Learning using Physics-Informed Neural Networks
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...
Electronic Bee-Veterinarian: A Data Centric Approach to Monitor Honeybee Health
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...
LAMBRETTA: Learning to Rank for Twitter Soft Moderation
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...
Charting the Future: Integrating Health Informatics into Undergraduate Medical Education
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...
Using AI to enhance FIB-SEM Image Segmentation For Cell Biology
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...
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...