Fall 2026 Missouri Data Science and Informatics Symposium

The Ozone Mapping and Profiler Suite-Nadir Mapper (OMPS-NM) is a key satellite instrument for retrieving and analyzing ozone concentrations in both the total column and different atmospheric layers. Given the relatively large footprint of the OMPS-NM sensor, accurately detecting cloud-contaminated fields of view (FOVs) at the sub-pixel scale is essential for both calibration/validation efforts and the direct assimilation of Sensor Data Record (SDR) radiances into numerical weather prediction (NWP) models. To address this challenge, a novel sub-pixel cloud detection model has been developed using deep learning techniques. The model is trained using reference cloud information from the Visible Infrared Imaging Radiometer Suite (VIIRS), which is collocated on the same satellite platform as OMPS-NM. The training dataset consists of globally matched NOAA-21 OMPS-NM and VIIRS measurements which are randomly selected from different months in 2024 to capture a wide range of atmospheric and surface conditions. For each OMPS-NM footprint, the collocated VIIRS cloud mask is used as the ground truth to train a deep neural network on the corresponding OMPS-NM spectral signatures. To reduce model complexity and improve efficiency, the OMPS-NM radiance spectra are transformed into principal components (PCs), with only the top 14 PCs used as input features. The model’s performance is evaluated globally against the VIIRS-derived cloud mask. Results show a strong spatial agreement between the model predictions and VIIRS cloud data, achieving a high global mean detection accuracy of 89%. Regionally, the model performs slightly better over oceans (90%) than over land (87%). Further comparison with the operational OMPS-NM effective cloud fraction, derived from NOAA’s total ozone product, demonstrates that the deep learning model is significantly more effective at identifying sub-pixel clouds that are often missed by the operational method. This enhanced detection capability highlights the model’s potential for improving the accuracy and reliability of OMPS-NM science products.

Shape is foundational to biology. Observing and documenting shape has fueled biological understanding as the shape of biomolecules, cells, tissues, and organisms arise from the effects of genetics, development, and the environment. To comprehensively quantify the vast morphological diversity in biology, we focus thus on Topological Data Analysis (TDA). TDA is an emerging mathematical discipline that uses principles from algebraic topology to comprehensively measure shape in a broad scope of different datasets. As a proof of concept, we explore a series of applications of TDA in plant biology with a variety of data inputs. 
 
With the Euler Characteristic Transform and X-ray CT scans of a wide array of barley varieties, we can predict genomic markers based solely on shape characteristics of their seeds alone. With persistent homology and Molecular Cartography technology, we can model patterns of mRNA spatial localization for different genes, cell types, and organs of the soybean root and nodule. With mapper and RNAseq data, we can uncover novel lung cancer subtypes. With adjacency complex filtrations, we can describe the intricate patterns made by pavement cells in arabidopsis leaves. 
 
The vision of TDA, that data is shape and shape is data, will be relevant as biology transitions into a data-driven era where meaningful interpretation of large datasets is a limiting factor. 
 
I am an Assistant Professor for Data Science at the  Division of Plant Science and Technology (DPST) of the University of Missouri—Columbia (MU). I also have an adjunct appointment at MU's Department of Mathematics. I am mainly interested in understanding and modeling plant morphology using topological data analysis (TDA). I am also interested in morphometrics, developmental plant biology, basic image processing, educational research, and data science for social justice. I go by he/él pronouns. 
 
I got my PhD from the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. I went there for the math and I stayed for the plants. My dissertation consisted of applying tools from algebraic topology to plant shape data. Before that, I got my math degree from the Universidad de Guanajuato with extensive support from the Mathematics Research Center (CIMAT). During my undegraduate degree, I tried to quantify the morphology of pre-Columbian masks with TDA, and how this might reveal their culture of origin.