On-the-fly Data Assessment for High Throughput X-ray Diffraction Measurement

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition

Probabilistic Mixture Model-Based Spectral Unmixing

Identifying pure components in mixtures is a common yet challenging problem. The associated unmixing process requires the pure components, also known as endmembers, to be sufficiently spectrally distinct. Even with this requirement met, extracting the endmembers from a single mixture is impossible; an ensemble of mixtures with sufficient diversity is needed. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most of them assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference approach that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and presence of non-isotropic Gaussian noise. Access to reliable uncertainties on the unmixing solutions would enable robust solutions as well as informed decision making

Exploring Mathematical Strategies for Finding Hidden Features in Multi-Dimensional Big Datasets

With advances in technology in brighter sources and larger and faster detectors, the amount of data generated at national user facilities such as SLAC is increasing exponentially. Humans have a superb ability to recognize patterns in complex and noisy data and therefore, data is still curated and analyzed by humans. However, a human brain is unable to keep up with the accelerated pace of data generation, and as a consequence, the rate of new discoveries hasn\u27t kept pace with the rate of data creation. Therefore, new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and finding hidden trends and contrasts in large datasets. The primary goal of this project is to develop a new algorithm using recent advances in image processing, machine learning techniques, and employing different types of distance metrics such as Euclidian, Manhattan, and Cosine to a large amount of diffraction data collected at a synchrotron beamline in high-throughput experimentation. The new algorithm enables analysis and extraction of hidden features from a large multi-dimensional dataset on-the-fly and near real-time with minimal computational cost and human intervention. When the algorithm is performed on a large number of x-ray diffraction patterns, the algorithm can be used to find the structural phase boundaries leading to the discovery of the composition-structure relationship, which is often an end goal of many materials science experiments

Women @ Work Program

The St. Mary’s Women @ Work program partners with the Department of IT at UMass Boston to provide young women with a path to self sufficiency. It does so by placing these youth for a 3 month period, in a real-work environment where they get paid and receive hands-on practical training

YearUp – Empowering Urban Talent to Reach Their Potential

YearUp is a national program, that aims to educate and train “disconnected” young adults between the ages of 18 and 24 to enter the workforce with skills in desktop support, helpdesk and other technical areas where the demand for skilled labor is always high