Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.
My research focuses on the causes, dynamics and outcomes of conflict, at the international and local levels. My methodological areas of interest include spatial statistics, mathematical/computational modeling and text analysis.
Map/time-series/network plot, showing the flow of information across battles in World War II. Z axis is time, X and Y axes are longitude and latitude, polygons are locations of battles, red lines are network edges linking battles involving the same combatants. Source: https://doi.org/10.1017/S0020818318000358
Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
Timothy C. Guetterman is a methodologist focused on research design and mixed methods research. His research interests include advancing rigorous methods of quantitative, qualitative, and mixed methods research, particularly strategies for intersecting and integrating qualitative and quantitative research. Tim is the PI of NIH-funded research that uses quantitative, qualitative, and mixed methods research to investigate the use of virtual human technology in health, education, and assessment. He has been applying natural language processing techniques to the analysis of mixed methods datasets. He also conducts research on teaching, learning, and developing research methods capacity as Co-PI of a William T. Grant Foundation qualitative and mixed methods research capacity building grant and in his role as evaluator and Co-I for the NIH-funded Mixed Methods Research Training Program for the Health Sciences. Tim has extensive professional experience conducting program evaluation with a focus on educational and healthcare programs.
My research is focused on a wide range of topics from computational social sciences to bioinformatics where I do pattern recognition, perform data analysis, and build prediction models. At the core of my effort, there lie machine learning methods by which I have been trying to address problems related to social networks, opinion mining, biomarker discovery, pharmacovigilance, drug repositioning, security analytics, genomics, food contamination, and concussion recovery. I’m particularly interested in and eager to collaborate on cyber security aspect of social media analytics that includes but not limited to misinformation, bots, and fake news. In addition, I’m still pursuing opportunities in bioinformatics, especially about next generation sequencing analysis that can be also leveraged for phenotype predictions by using machine learning methods.
A typical pipeline for developing and evaluating a prediction models to identify malicious Android mobile apps in the market
Dr. Hemphill studies conversations in social media and aims to promote just access to social media spaces and their data. She uses computational approaches to modeling political topics, predicting and addressing toxicity in online discussions, and tracing linguistic adaptations among extremists. She also studies digital data curation and is especially interested in ways to measure and model data reuse so that we can make informed decisions about how to allocate data resources.
My core research focuses on the politics and measurement of human rights, discrimination, violence, and repression. I use computational methods to understand why governments around the world torture, maim, and kill individuals within their jurisdiction and the processes monitors use to observe and document these abuses. Other projects cover a broad array of themes but share a focus on computationally intensive methods and research design. These methodological tools, essential for analyzing data at massive scale, open up new insights into the micro-foundations of state repression and the politics of measurement.
People rely more on strong ties for job help in countries with greater inequality. Coefficients from 55 regressions of job transmission on tie strength are compared to measures of inequality (Gini coefficient), mean income per capita, and population, all measured in 2013. Gray lines indicate 95% confidence regions from 1000 simulated regressions that incorporate uncertainty in the country-level regressions (see below for more details). In each simulated regression we draw each country point from the distribution of regression coefficients implied by the estimate and standard error for that country and measure of tie strength. P values indicate the simulated probability that there is no relationship between tie strength and the other variable. Laura K. Gee, Jason J. Jones, Christopher J. Fariss, Moira Burke, and James H. Fowler. “The Paradox of Weak Ties in 55 Countries” Journal of Economic Behavior & Organization 133:362-372 (January 2017) DOI:10.1016/j.jebo.2016.12.004
My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.
We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.
Cultural systems are fundamentally structural phenomena, defined by patterns of relations between elements of public representations and individual behaviors and cognitions. However, because such systems are difficult to capture with traditional empirical approaches, they usually remain understudied. In my work, I draw on network analysis, statistics, and computer science to create novel approaches to such analyses, and on cognitive science to theorize the objects of these investigations. Broader questions that interest me are: how are different cultural elements interrelated with one another? What is the relationship between public cultural representations and individual cognition and behavior? And how can we capture the structure of these interrelationships across large social and time scales? Methodologically, I am currently focused no developing applications of word embeddings and other natural language processing methods to sociological questions about cultural change.