Elle O’Brien

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My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.

Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency

The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.

View MIDAS Faculty Research Pitch, Fall 2021

 

Lia Corrales

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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.

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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.

Wei Lu

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Dr. Lu brings expertise in machine learning, particularly integrating human knowledge into machine learning and explainable machine learning. He has applied machine learning in a range of domain applications, such as autonomous driving and machine learning for optimized design and control of energy storage systems.

Carina Gronlund

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As an environmental epidemiologist and in collaboration with government and community partners, I study how social, economic, health, and built environment characteristics and/or air quality affect vulnerability to extreme heat and extreme precipitation. This research will help cities understand how to adapt to heat, heat waves, higher pollen levels, and heavy rainfall in a changing climate.

Omar Jamil Ahmed

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The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neurological disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in Alzheimer’s disease, Parkinson’s disease and epilepsy. Our research involves the collection of massive volumes of neural data. Within these terabytes of data, we work to identify and understand irregular activity patterns at the sub-millisecond level. This requires us to leverage high performance computing environments, and to design custom algorithmic and analytical signal processing solutions. As part of our research, we also discover new ways for the brain to encode information (how neurons encode sequences of space and time, for example) – and the algorithms utilized by these natural neural networks can have important implications for the design of more effective artificial neural networks.

Niko Kaciroti

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Niko Kaciroti is a Research Scientist at the Departments of Pediatrics and Biostatistics. He received his PhD in Biostatistics from the University of Michigan. Since then he has collaborated in multidisciplinary research at the University of Michigan and with researchers from other universities in the United States and internationally. Dr. Kaciroti is a faculty member at the Center for Computational Medicine and Bioinformatics. His main research interest is in using Bayesian models for analyzing longitudinal data from clinical trials with missing values, as well as using Bayesian methods for nonlinear and dynamic models. Dr. Kaciroti is an elected member of the International Statistical Institute and serves as statistical editor for the American Journal of Preventive Medicine and the International Journal of Behavior Nutrition and Physical Activity.

Xianglei Huang

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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

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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.

Sindhu Kutty

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My research centers on studying the interaction between abstract, theoretically sound probabilistic algorithms and human beings. One aspect of my research explores connections of Machine Learning to Crowdsourcing and Economics; focused in both cases on better understanding the aggregation process. As Machine Learning algorithms are used in making decisions that affect human lives, I am interested in evaluating the fairness of Machine Learning algorithms as well as exploring various paradigms of fairness. I study how these notions interact with more traditional performance metrics. My research in Computer Science Education focuses on developing and using evidence-based techniques in educating undergraduates in Machine Learning. To this end, I have developed a pilot summer program to introduce students to current Machine Learning research and enable them to make a more informed decision about what role they would like research to play in their future. I have also mentored (and continue to mentor) undergraduate students and work with students to produce publishable, and award-winning, undergraduate research.