Data Science Fellows
Data Science Fellows
Ryan Bartelme holds an undergraduate degree in Microbiology from the University of Wisconsin - Madison, where he studied dimorphic fungal pathogenesis. After undergrad, he spent some time in academic support roles, as well as in the urban agriculture industry. Ryan completed an interdisciplinary PhD in Freshwater Sciences from the University of Wisconsin - Milwaukee. His doctoral work encompassed aspects of aquaculture/aquaponics, controlled environment agriculture, environmental engineering, chemistry, microbial ecology, genomics, and computational biology. Currently, he is working with Dr. Bryan Heidorn as a postdoctoral researcher in the School of Information at the University of Arizona. His latest research project uses machine learning and deep learning methodologies to predict plant phenotypes from genomic and environmental data.
Alex is a data visualization researcher from the HDC Lab (http://hdc.cs.arizona.edu/), working with Professor Kate Isaacs. His research is focused on the creative impacts that data structures can have on analysis questions and visualization designs. For example, to what extent does formatting data as a table, or as a network, influence the analysis questions that a scientist might think to ask? His main technical expertise is primarily in methodologies for discovering and elucidating visualization needs, and in web-based visualization software design and development. Visit his website: https://alex-r-bigelow.github.io
Gustavo de Oliveira Almeida earned Bachelor’s degrees in Business and Computer Information Systems, a Master’s of Business Administration, and a PhD in Business, and completed a graduate certificate in Systems engineering. He was a Visiting Scholar at Rory Meyers College of Nursing - New York University (2018-2019). He is currently working towards a second PhD in informatics, and is a permanent faculty of the Graduate Program of Business Administration at Federal Fluminense University - Brazil. He is interested in studying multidisciplinary collaboration dynamics with wearable sensors and bridging applications in health and data science, specialized in multimodal data streams. Gustavo holds a postdoctoral position in the School of Information with Dr. Win Burleson working with the UA Holodeck, an immersive experiential virtual reality environment that generates extensive real-time data encompassing multiple components.
Alise Ponsero is a microbiologist and a bioinformatician studying the role of microorganisms in their environment. She received her PhD in microbiology and cellular biology from the University of Paris-Saclay (France). She holds two master’s degrees, one in environmental microbiology and another in computer science. She is currently working as a postdoctoral fellow in Dr. Hurwitz lab. Her research focuses on the development of new bioinformatic tools to analyze massive datasets, leveraging her expertise in both microbial ecology and data science. She is also involved in the development of cyberinfrastructure such as iMicrobe (https://imicrobe.us) and Planet Microbe (https://planetmicrobe.org).
- Science Analyst
Reetu is a science analyst at CyVerse (https://cyverse.org/), where she works with CyVerse technologies, developers and researchers to develop cutting edge scientific workflows and applications in the area of bioinformatics and high-throughput sequence analysis. Reetu also helps to train researchers in using cyberinfrastructure, provide user support and helps improve platforms and services. She earned her Master’s degree in bioinformatics from Banasthali University, India and PhD in plant sciences and bioinformatics from the National University of Ireland, Galway. Her PhD research was focused on studying the evolutionary processes that can contribute to the species-specific adaptation in plants. She continued working on plant genomics for her postdoc in Professor Gloria Coruzzi lab at New York University where she developed data management and analysis systems to infer complex regulatory networks from gene expression and DNA-binding datasets.