Lia Corrales

By |

My PhD research focused on identifying the size and mineralogical composition of interstellar dust through X-ray imaging of dust scattering halos to X-ray spectroscopy of bright objects to study absorption from intervening material. Over the course of my PhD I also developed an open source, object oriented approach to computing extinction properties of particles in Python that allows the user to change the scattering physics models and composition properties of dust grains very easily. In many cases, the signal I look for from interstellar dust requires evaluating the observational data on the 1-5% level. This has required me to develop a deep understanding of both the instrument and the counting statistics (because modern-day X-ray instruments are photon counting tools). My expertise led me to a postdoc at MIT, where I developed techniques to obtain high resolution X-ray spectra from low surface brightness (high background) sources imaged with the Chandra X-ray Observatory High Energy Transmission Grating Spectrometer. I pioneered these techniques in order to extract and analyze the high resolution spectrum of Sgr A*, our Galaxy’s central supermassive black hole (SMBH), producing a legacy dataset with a precision that will not be replaceable for decades. This dataset will be used to understand why Sgr A* is anomalously inactive, giving us clues to the connection between SMBH activity and galactic evolution. In order to publish the work, I developed an open source software package, pyXsis (github.com/eblur/pyxsis) in order to model the low signal-to-noise spectrum of Sgr A* simultaneously with a non-physical parameteric model of the background spectrum (Corrales et al., 2020). As a result of my vocal advocacy for Python compatible software tools and a modular approach to X-ray data analysis, I became Chair for HEACIT (which stands for “High Energy Astrophysics Codes, Interfaces, and Tools”), a new self-appointed working group of X-ray software engineers and early career scientists interested in developing tools for future X-ray observatories. We are working to identify science cases that high energy astronomers find difficult to support with the current software libraries, provide a central and publicly available online forum for tutorials and discussion of current software libraries, and develop a set of best practices for X-ray data analysis. My research focus is now turning to exoplanet atmospheres, where I hope to measure absorption from molecules and aerosols in the UV. Utilizing UM access to the Neil Gehrels Swift Observatory, I work to observe the dip in a star’s brightness caused by occultation (transit) from a foreground planet. Transit depths are typically <1%, and telescopes like Swift were not originally designed with transit measurements (i.e., this level of precision) in mind. As a result, this research strongly depends on robust methods of scientific inference from noisy datasets.

cirx1_heinz_pretty_image

As a graduate student, I attended some of the early “Python in Astronomy” workshops. While there, I wrote Jupyter Notebook tutorials that helped launch the Astropy Tutorials project (github.com/astropy/astropy-tutorials), which expanded to Learn Astropy (learn.astropy.org), for which I am a lead developer. Since then, I have also become a leader within the larger Astropy collaboration. I have helped develop the Astropy Project governance structure, hired maintainers, organized workshops, and maintained an AAS presence for the Astropy Project and NumFocus (the non-profit umbrella organization that works to sustain open source software communities in scientific computing) for the last several years. As a woman of color in a STEM field, I work to clear a path by teaching the skills I have learned along the way to other underrepresented groups in STEM. This year I piloted WoCCode (Women of Color Code), an online network and webinar series for women from minoritized backgrounds to share expertise and support each other in contributing to open source software communities.

Xianglei Huang

By |

Prof. Huang is specialized in satellite remote sensing, atmospheric radiation, and climate modeling. Optimization, pattern analysis, and dimensional reduction are extensively used in his research for explaining observed spectrally resolved infrared spectra, estimating geophysical parameters from such hyperspectral observations, and deducing human influence on the climate in the presence of natural variability of the climate system. His group has also developed a deep-learning model to make a data-driven solar forecast model for use in the renewable energy sector.

Nicholas Henderson

By |

My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.

Gary Luker

By |

We use a variety of quantitative imaging methods, ranging from single cells to clinical studies, to investigate cancer signaling and response to therapy over space and time. We develop image analysis methods to extract data from thousands of single cells over time and voxel-wise measurements of imaging parameters. We also use bulk and single-cell RNA sequencing to investigate heterogeneity among cancer cells and changes induced by intercellular interactions. A current goal of our ongoing work is to merge RNA sequencing and imaging data to understand cell decision making in cancer. We collaborate with investigators using machine learning and computational modeling approaches to inform cell signaling and resultant behaviors in tumor growth and metastasis.

Lana Garmire

By |

My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.

Maureen Sartor

By |

My lab has two main areas of focus: molecular characteristics of head and neck cancer, and the intersection of regulatory genomics and pathway analysis. With head and neck cancer, we study tumor subtypes and biomarkers of prognosis, treatment response, and recurrence. We perform integrative omics analyses, dimension reduction methods, and prediction techniques, with the ultimate goal of identifying patient subsets who would benefit from either an additional targeted treatment or de-escalated treatment to increase quality of life. For regulatory genomics and pathway analysis, we develop statistical tests taking into account important covariates and other variables for weighting observations.

Carlos Aguilar

By |

The Aguilar group is focused understanding transcriptional and epigenetic mechanisms of skeletal muscle stem cells in diverse contexts such as regeneration after injury and aging. We focus on this area because there are little to no therapies for skeletal muscle after injury or aging. We use various types of in-vivo and in-vitro models in combination with genomic assays and high-throughput sequencing to study these molecular mechanisms.

Xu Shi

By |

My methodological research focus on developing statistical methods for routinely collected healthcare databases such as electronic health records (EHR) and claims data. I aim to tackle the unique challenges that arise from the secondary use of real-world data for research purposes. Specifically, I develop novel causal inference methods and semiparametric efficiency theory that harness the full potential of EHR data to address comparative effectiveness and safety questions. I develop scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems and coding systems.

Evan Keller

By |

Our laboratory focuses on (1) the biology of cancer metastasis, especially bone metastasis, including the role of the host microenvironment; and (2) mechanisms of chemoresistance. We explore for genes that regulate metastasis and the interaction between the host microenvironment and cancer cells. We are performing single cell multiomics and spatial analysis to enable us to identify rare cell populations and promote precision medicine. Our research methodology uses a combination of molecular, cellular, and animal studies. The majority of our work is highly translational to provide clinical relevance to our work. In terms of data science, we collaborate on applications of both established and novel methodologies to analyze high dimensional; deconvolution of high dimensional data into a cellular and tissue context; spatial mapping of multiomic data; and heterogenous data integration.

Joshua Welch

By |

Our research aims to address fundamental problems in both biomedical research and computer science by developing new tools tailored to rapidly emerging single-cell omic technologies. Broadly, we seek to understand what genes define the complement of cell types and cell states within healthy tissue, how cells differentiate to their final fates, and how dysregulation of genes within specific cell types contributes to human disease. As computational method developers, we seek to both employ and advance the methods of machine learning, particularly for unsupervised analysis of high-dimensional data. We have particular expertise in manifold learning, matrix factorization, and deep learning approaches.