A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.
Abstract: Over 782,000 individuals in the U.S. have end-stage kidney disease with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to...
Deplatforming Right-Wing Extremists on Twitter Following the January 6 Insurrection
Abstract: What happened when Twitter deplatformed 70,000 right-wing extremists following the January 6 insurrection? Using a panel of over a half million active Twitter users and a sharp regression discontinuity design, we test the causal effects of this intervention on the circulation of misinformation by those deplatformed, and by users from adjacent groups such as...
Understanding Large ML Models through the Structure of Feature Covariance
Abstract: An overarching goal in machine learning is to enable accurate statistical inference in the setting where the sample size is less than the number of parameters. This overparameterized setting is particularly common in deep learning where it is typical to train large neural nets with relatively smaller sample sizes and little concern of overfitting...
Multiview learning for knowledge discovery
Abstract: Extracting hidden patterns of multiview data containing heterogeneous feature representations is attracting more and more attention in various scientific fields such as image processing and natural language processing. In this talk we will present a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a...
Characterizing soil – plant – water relationships across scales for sustainable agricultural management
Abstract: Agricultural systems are pressured by growing global population, increasing water scarcity, and changing climate. In the pursuit of increasing food security, agriculture (especially intensive systems) should also minimize negative and undesired impacts on the environment and on rural societies. Part of the solution to this challenge lies in understanding how environmental factors such as...
Immune regulatory pathways in infection, inflammation and sepsis
Abstract: My lab investigates the immune responses to infection and inflammation using mouse models of parasitic worm infection and clinical samples from sepsis patients. Our ultimate goal is to identify protective or pathogenic immune pathways that we can target for diagnostic or therapeutic purposes. In our mouse infection models we investigate macrophages as first responders...
Learning Binary Code Representations for Security Applications
Abstract: Learning a numeric representation (also known as embedded vector, or simply embedding) for a piece of binary code (an instruction, a basic block, a function, or even an entire program) has many important security applications, ranging from vulnerability search, plagiarism detection, to malware classification. By reducing a binary code with complex control-flow and data-flow...
Lost in translation: The challenges and benefits of understanding complex insect societies
Social insects include the termites, ants and the social bees and wasps, which are a very large and ecologically very successful group of animals. They are also of tremendous importance for humans. Whereas some social insects are serious pest species that become increasingly difficult to control, others are of central importance for agricultural food production...
Outcomes from an experiment in creating data science centers
The Berkeley Institute for Data Science (or BIDS) was founded as part of a high-profile, multi-university initiative funded by the Moore and Sloan Foundations, collectively known as the Moore-Sloan Data Science Environments (or MSDSE), with the mission of creating ``institutional change'' around data science in academia. I will discuss some of the lessons learned in...
Fusioning big-data ecology and genomics: from data to dynamic system understanding and prediction
Much of current application efforts of data science in both of ecology and genomics has been focusing on the data-driven, static but not fully dynamic understanding of those systems. In this talk, I will introduce our recent work on fusioning data- and model-driven approaches to understand the fundamental nitrogen biochemical processes in fluctuating soil redox...