UArizona Data Lab: Graduate Research Assistants bridge teamwork and research innovation

Oct. 26, 2023

The Data Science Institute highlights three University of Arizona Graduate Research Assistants who provide valuable skills to the UArizona Data Lab led by Carlos Lizárraga. The collective expertise within the UArizona Data Lab makes it well-positioned to advance interdisciplinary research and the field of data science across various UArizona departments.

Brenda Huppenthal is a graduate student in the UArizona Computer Science department planning to pursue a Ph.D. With a research interest in computer vision, she has been working with the PhytoOracle team since the summer of 2023 to obtain useful phenotypic information from point clouds of sorghum plants using computer vision techniques. As a Graduate Research Assistant with the UArizona Data Lab, Huppenthal is not only a researcher who studies deep neural networks expanding her expertise in data science but also develops and assists with presenting workshops for the university community including an upcoming workshop on an introduction to computer vision. Her goal is to empower individuals at various skill levels with the fundamental abilities needed for data manipulation and analysis, ultimately providing them with a launching pad for deeper explorations.

Within the UArizona Data Lab, Huppenthal holds a position that allows her to expand her understanding of data science best practices while simultaneously elevating the data literacy of students, staff, and faculty across UArizona. Huppenthal’s academic and research pursuits help to encourage a community of data enthusiasts unlocking the potential of data science and computer vision.

Meghavarshini Krishnaswamy (Megh) is a 5th year PhD student in the UArizona Department of Linguistics with a specialization in phonetics. Krishnaswamy’s experience as a Graduate Research Assistant with the UArizona Data Lab and a former UArizona Data Science Fellow has contributed to her professional development and upskilling for a role in open and reproducible data science.

Her research focuses on exploring strategies for teamwork and cooperation and how human language is influenced when communicating with others in a team. She is investigating methods to automate the detection of entrainment in the speech and language of individuals collaborating on team tasks. Krishnaswamy is interested in learning more about the use of synthetic voices and whether existing architectures could effectively capture human-AI teamwork.  With her research, she recognizes that she must have the right tools to perform a variety of tasks such as creating robust pipelines for data capture and cleaning, generating usable datasets for statistical modeling and version control, and understanding different machine and deep learning architectures. 

The UArizona Data Lab gives her access to a multitude of software and a research experienced team for collaborations in short and medium-term product-based projects. In addition, she assists with workshops that are focused on knowledge gaps in academic research and approachable ways to solve them. Krishnaswamy states, “I have a supportive and result-oriented environment to learn, apply and help transfer the skills I have acquired during my research journey.”  Krishnaswamy exemplifies the fusion of linguistic expertise and data science, shaping the future of collaborative and knowledge-driven initiatives.

Ankit Pal, a graduate student in Data Science at the UArizona School of Information, wears multiple hats in research and data science. As a research assistant in the Eller College of Management, he delves into the challenging realm of medical text simplification, a task that demands a synergy of data science, programming, and natural language processing skills. In addition, Pal is a Graduate Research Assistant at the UArizona Data Lab with the Data Science Institute, actively contributing to creating and presenting workshop initiatives.

Pal recognizes the UArizona Data Lab's commitment to knowledge sharing through the series of workshops and activities provided. The workshops offer a diverse array of topics, ranging from data science and programming languages to infrastructure, deep learning, and machine learning. Participating in these workshops not only creates connections with like-minded individuals but also plays a role in augmenting Pal's technical expertise and teaching skills. Pal is laying the groundwork for contributions in the world of text simplification of medical text and data science.

Tina L. Johnson