Davis Lab (Jaimie Davis, Nutrition)
Jaimie Davis is an assistant professor of nutritional sciences hired as a faculty member at UT Austin in 2012. Over the past 10 years, Davis's research group research has focused on designing and implementing obesity interventions for low-income minority children and adolescents. She has extensive expertise in nutrition physical activity, and body composition assessment in pediatric populations. Davis's research also involves developing and testing school and community based gardening and cooking programs targeting obesity prevention and treatment for low-income minority populations. Her work directly address the effects of behaviors (dietary and physical activity) and changes in these behaviors on adiposity parameters, type 2 diabetes, and cardiovascular risk factors in minority youth. Dr. Davis also recently submitted an NIH grant (with co-program director Daniels as a co-investigator) to develop a centralized, real-time Monitoring and Reaction (MORE) system that combines behavioral data (i.e., diet, exercise, sleep), insulin dosing, glucose levels, and emotional status, to allow targeted and integrated communications with parents/caregivers and healthcare professionals in real-time, and to test the effects on glucose control of the child and quality of life (QoL) and emotional status of the child and parent.
Georgiou Lab (George Georgiou, Chemical Engineering and BME)
Georgiou's research group is working on the analysis of the immune receptor repertoire by proteomic and NextGen sequencing technologies. They have developed the only available platform for sequencing multiple transcripts (2 or 3) from single B cells at very high throughput, a technology that has enabled the determination of the VH:VL paired repertoire. They also developed technologies for the determination of the identity and relative quantization of the polyclonal anti- body repertoire in serum or secretions. They are currently employing these and other high information content methodologies to the analysis and mining of human immune responses following infection or vaccination. This work is generating very large datasets of VH:VL antibody sequences (and to a lesser extent TCR sequences). They are developing a cloud based computational resource for the analysis of antibody repertoires and for delineating the evolution of antigen specific antibody lineages in longitudinal samples.
Hofmann Lab: (Johann Hofmann, IB)
Hofmann's research group utilizes sophisticated bioinformatics and statistical approaches to investigate how social interactions affect neural and behavioral phenotypes of individuals. Research in humans has quantitatively demonstrated that phenotypic outcomes and behavioral attitudes can indeed be predicted based on an individuals social connections, yet how interactions with other individuals affect brain and behavior phenotypes is not well understood. The neuromolecular mechanisms that regulate social behavior are still poorly understood, even though neurodevelopmental disorders (e.g., Autism spectrum disorders) severely affect social interactions. Trainees in the Hofmann lab use a variety of vertebrate model systems to investigate the endocrine and molecular mechanisms underlying social behavior within an integrative framework.
Huk Lab (Alex Huk, NS)
Huk's research group focuses on making large-scale recordings of neural activity across the primate cortex. This is done both using direct measurement (electrophysiological recordings in nonhuman primates) and indirect techniques (functional magnetic resonance imaging in humans). The electrophysiological work is unique in that they have developed techniques to record from multiple neurons in multiple brain areas, all at the same time, in primates performing complex behavioral tasks. This allows direct tests of hypotheses about the ow of information from one brain area to another during various controlled forms of perception, cognition, and action. However, the scale of these recordings also requires the development and application of statistical and analytic tools to make sense of these high dimensional data. Likewise, the brain imaging work in his lab employs cutting-edge multiplexing sequences capable of yielding data at considerably higher spatial and temporal resolutions than conventional imaging approaches. Like the electrophysiology, these data are rich but also require new techniques to handle their scale.
Kirkpatrick Lab (Mark Kirkpatrick, IB)
Kirkpatrick's research group attacks the fundamental question of what forces drive evolutionary change of the genome from two sides: they develop mathematical models that generate quantitative hypotheses, and analyze genomic data to test those hypotheses. These models are fit to the data using a range of statistical frameworks including likelihood, Bayesian methods, and approximate Bayesian computation (ABC). There are interesting computational challenges here as they are constantly fitting ever more complex (and realistic) evolutionary models to large data sets (e.g. 103 individuals each genotyped at 105 markers). A second research theme is developing methods to estimate quantitative genetic variances and covariances in natural and domestic populations. These parameters are important because they determine how rapidly species can adapt (in nature) and how fast they can be economically improved by selective breeding (in agriculturally important animals and crop plants). Here they are developing likelihood and Bayesian-like methods that are su_fficiently efficient to work with large data sets, for example multi-generation pedigrees with hundreds of thousands of individuals.
Markey Lab (Mia Markey, BME)
Markey's research group develops decision support systems for clinical decision- making and scientific discovery. Her lab leverages signal processing, machine learning, and statistical methods in designing algorithms for data-driven, health-focused research. In addition to collaborations with other engineering researchers and clinical experts, Markey's group has close partnerships with colleagues in the behavioral sciences. For example, Dr. Markey leads a collaborative, multi-institutional team working towards the vision of a decision support system that will enable breast cancer patients, in consultation with their healthcare providers, to choose a reconstruction strategy with maximal potential to optimize psychosocial adjustment.
Meyers Lab (Lauren Ancel Meyers, IB and SDS)
For over a decade, Dr. Meyers's research group has been working in the field of mathematical epidemiology, pioneering network-based mathematical modeling of infectious disease transmission in human and animal populations. Collaborating with field ecologists, epidemiologists, and public health agencies around the globe, she and her research group at UT have applied these methods to diverse data sets to gain a better understanding of the dynamics of infectious diseases (in particular, pandemic influenza, Ebola, SARS, and HIV) and to develop effective strategies for surveillance, mitigation and conservation. Over the last six years, Dr. Meyers has led several large interdisciplinary research teams in developing decision-support tools for optimizing infectious disease surveillance systems and control policies. These projects network graduate students from diverse fields with state and national public health practitioners, and provide critical graduate training in translating basic science into practical applications.
Preston Lab (Alison Preston, NS)
Preston's research group uses a combination of behavioral and human brain imaging techniques to explore how we form new memories, how we remember past experiences, and how our memories for the past influence what we learn in the present. In particular, Dr. Preston's work has focused on characterizing the functional role of the human hippocampus and its interactions with prefrontal, parietal and sensory cortices during behaviors that rely on memory. Her lab has brought several new paradigms and techniques to bear on these questions, including high-resolution functional magnetic resonance imaging (fMRI), which affords more precise visualization of the detailed structure of the human brain, including hippocampal sub-fields. The lab has also combined these cutting-edge fMRI acquisition methods with machine learning techniques to decode when individuals retrieve specific memory content in service of decision making. In particular, students receive practical training on sophisticated fMRI data analysis techniques that utilize the high-performance computing resources provided by the Texas Advanced Computing Center.
Sacks Lab (Michael Sacks, BME)
Sacks's research group is internationally renowned for their work on cardio- vascular biomechanics, with a focus on the quantification and simulation of the structure-mechanical properties of native and engineered cardiovascular soft tissues. His group also works on the mechanical behavior and function of heart valves, including the development of the first constitutive models for these tissues using a structural approach; and on the biomechanics of engineered tissues, and on understanding the in-vitro and in-vivo re- modeling processes from a functional biomechanical perspective. The research includes multi-scale studies of cell/tissue/organ mechanical interactions in heart valves and tries to determine the local stress environment for heart valve interstitial cells. Recent research has included developing novel constitutive models of right ventricular myocardium that allow for the individual contributions of the myocyte and connective tissue networks.
Wilke Lab: (Claus Wilke, IB)
Wilke's research group carries out computational research in protein biochemistry, molecular evolution, and systems biology. At present, the lab pursues two major research directions: first, the evolution of protein-coding genes, in particular as applied to virus evolution and viral host-range shifts, using existing and novel computational methods such as maximum-likelihood and Bayesian statistics, computational protein design, and all-atom molecular dynamics simulations; second, the development of statistical and mechanistic models of bacterial metabolism. The goal in this research is to predict how bacterial metabolism changes when bacteria are grown in different environments.