February 2022 Summaries

In this interview with Dr. Melodie Kao, she discusses the barriers to STEM equality and the importance of boundary setting in scientific education. She emphasizes that while technical skills are important, non-technical skills such as emotional awareness, conflict management, and boundary setting play a crucial role in fostering successful careers in science. Dr. Kao shares her experiences with teaching these human-centered skills to students through workshops and integrating them into formal scientific education. She also highlights the issue of "deficit thinking" prevalent in education and how it perpetuates systemic biases, disadvantaging those who do not have equal opportunities to learn non-technical skills. Dr. Kao's research focuses on exo-volcanism, which opens up possibilities for discovering life outside our solar system and understanding the interior dynamics of planets.

A Datathon is an event where people collaborate intensively on data-related projects within a time constraint. They can be used as R&D functions, ideation tools, and team bonding exercises. To run a successful datathon in the age of the Modern Data Stack, it's essential to create a clear process for submitting and judging project artifacts, market the event effectively, set up a Slack channel for communication, give a great kickoff speech, prepare relevant data, and ensure that all teams have access to necessary tools. During the event, communicate clearly about expectations and timelines, provide food and drink, and encourage teams to do short demos of their projects. After the event, send a recap email highlighting successful projects, prioritize time for deeper investigation on promising projects, and archive all projects in a safe place. Remember to have fun and make sure everyone feels valued and rewarded for their work.

Dr. Melodie Kao is a Heising-Simons 51 Pegasi b Fellow at UC Santa Cruz in the Department of Astronomy & Astrophysics. She studies radio emissions from very low mass stars and brown dwarfs, which are failed stars that didn't have enough mass to burn hydrogen in their cores. Kao uses data analytics to understand planetary magnetic fields by studying brown dwarf magnetic fields. Her work involves designing experiments, collecting data using large telescopes, and analyzing the data using Python scripts. She emphasizes the importance of good experiment design for quality data analysis. The storage and processing of radio astronomy data is a significant challenge due to its size. Kao believes that greater data transparency and reproducible results are ideal goals in science but acknowledges the challenges posed by concerns over intellectual property and job security.