Magnetic resonance imaging (MRI) allows for the detection structural changes due to disease or inspection of neural patterns that underlie cognition. Prior hardware and software limitations kept the dimensionality of MRI datasets low and reduced the statistical power of MRI datasets. However, recent technological advancements in MRI have allowed for the development of large scale...

Since the sequencing of the first human genome in 2001, more than 3000 human genomes have been sequenced and more than 150 million SNPs have been identified in those genomes. Many of the SNPs lie in regions of genes that encode proteins and potentially impact function and contribute to disease. However, the vast majority of...

Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference, and provide solutions to the problems we...

Robot motion planning and control in real-world settings is hindered, in part, by uncertainty. Dealing with uncertainty is a difficult problem because it invalidates the performance guarantees often available in deterministic cases, while its precise effect on motion cannot be predicted. Further, (autonomous) robot performance often emerges through the interaction of multiple components, mainly including...

Hunter-gatherer adaptations are tied to the way that climate and environment shape the food and technological resource base. Discovering the relation between climate and environmental change and human origins must be grounded in a causal understanding of the connection between climate, environment, resource patterning, and human behavior. To better understand the origins of modern humans...

Multielectrode arrays (MEA) allow recording of electroencephalogram (EEG) signals from multiple sites simultaneously. We have implemented skull surface MEA in a mouse model of Fragile X syndrome (Fmr KO mouse). This enables unprecedented electrophysiological characterization of normal vs. Fmr KO mice. In this presentation, we will describe the rationale and our early data implementing MEA...

Food intake and energy balance are controlled by a dynamic interplay of gut-brain signaling pathways that are poorly defined. Recent work from the DiPatrizio lab, however, suggests that our bodies' own cannabis-like signaling molecules, the endocannabinoids, control gut-brain signaling important for food intake. Furthermore, this signaling becomes upregulated in diet-induced obesity and causes overeating. These...

Evolutionary biology and systematics seek to understand how organisms are related and processes that lead to species, populations and associated traits of organisms. Using whole genome sequencing to inventory the DNA, computational tools to assemble and annotate genomes, and analyses to identify shared gene sequences among organisms we are assembling the fungal tree of life...

De novo genome assembly is a challenging computational problem due to the high repetitive content of eukaryotic genomes and the imperfections of sequencing technologies. Several assembly tools are currently available, each of which has strengths and weaknesses in dealing with the tradeoff between maximizing contiguity and minimizing assembly errors (e.g., mis-joins). In order to obtain...

High-throughput methods based on chromosome conformation capture technologies have greatly advanced our understanding of the three-dimensional (3D) organization of genomes and demonstrated that genome architecture strongly influences gene regulation. However, methods to analyze the 3D chromatin spatial organization data are still in their infancy. In this talk, I will first present a wavelet approach for...

We explore the NYSE Trades and Quotes (TAQ) database that contains tick-by-tick transaction information of stocks traded in the New York Stock Exchange and NASDAQ stock markets. Consistent with asymmetric information models of market infrastructure, we analyze the role of trading intensity, as a proxy for latent information, on the value of financial assets. We...

Markov chain Monte Carlo (MCMC) produces a correlated sample for estimating expectations with respect to a target distribution. A fundamental question is when should sampling stop so that we have good estimates of the desired quantities? The key to answering this question lies in assessing the Monte Carlo error through a multivariate Markov chain central...

Here I will discuss some of the projects that the Brain Game Center is working on and areas where there are significant advantages to moving beyond traditional approaches to data analytics. Issues that we are trying to solve are how can one classify people into subgroups based upon a collection of tests? What rehabilitation approaches...

The complement system is a part of innate immunity that rapidly removes invading pathogens and impaired host-cells. Activation of the complement system is balanced under homeostasis by regulators that protect healthy host-cells. Impairment of complement regulators tilts the balance, favoring activation and propagation that leads to inflammatory and autoimmune diseases. To understand the dynamics of...

Adaptive Boosting or AdaBoost, introduced by Freund and Schapire (1996) has been proved to be effective to solve the high-dimensional binary classification or binary prediction problems. Friedman, Hastie, and Tibshirani (2000) show that AdaBoost builds an additive logistic regression model via minimizing the ‘exponential loss’. We show that the exponential loss in AdaBoost is equivalent...

The explosion of Internet of Things and cloud computing applications has generated a huge demand for multi-tenant collocation data centers everywhere, extending the Internet edge beyond the traditional hub locations. As one would expect, securing datacenters against cyber attacks is extremely important, and so is providing a reliable power supply to servers. While the threat...

It is well known that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In our recent work, we have explored a different, but related, problem: how can these inter relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort...

Microblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including analyzing events and users activities and critical applications like discovering health issues and rescue services. Consequently, major research efforts are...

Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. As a result, projections of future changes in water resources become complicated due to the tight coupling between the biosphere and terrestrial hydrologic cycle. In recent years a number of physically...

Environmental sensing has expanded rapidly for more than a decade. I will provide an overview of the dimensions of this data revolution within the ecological sciences. I will then describe a specific evaluation of the water-ecosystem service trade-offs for the use of urban vegetation to cool cities. Vegetation interacts strongly with urban water sustainability. Plants...