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Paul Cox is the CEO of Shark Trust with a background in Marine Biology. In his own words "My constant focus is on expanding the reach and impact of the Shark Trust and providing the resources for our team to deliver positive change. I'm always on the lookout for new partners, supporters and donors. We're looking for innovative ways to fund our charity and to extend our reach. There is much to be positive about in the progress we've made as a sector and this decade holds great hope for progress in conservation. But change requires a constant input of energy and resources. And it relies heavily on collaboration and collective action. Contact me if you want to join our mission to protect the world's amazing sharks and rays."

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Dr. Pakpour is an Entomologist and co-founder and CEO of Yeast Bay Bio, a biotech startup developing safe and sustainable alternatives to chemical insecticides used in the home. She loves bugs of all varieties, whether they are six-legged or microscopic. She is infinitely curious, so always up for connecting with fellow founders, scientists, investors, or anyone else who thinks our homes could use fewer toxic chemicals.

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Dr. Carlotta A. Berry is a Professor in the Department of Electrical and Computer Engineering at Rose-Hulman Institute of Technology and the Dr. Lawrence J. Giacoletto Endowed Chair for Electrical and Computer Engineering. Dr. Berry is the first Black woman to earn tenure, full professor and endowed chairship at Rose-Hulman.

She has a bachelor’s degree in mathematics from Spelman College, bachelor’s degree in electrical engineering from Georgia Institute of Technology, master’s in electrical engineering from Wayne State University, and PhD from Vanderbilt University. Her research interests are in robotics education, interface design, human-robot interaction, and increasing historically marginalized and minoritized populations in STEM (science, technology, engineering, math) fields. She is author of the textbook, "Mobile Robotics for Multidisciplinary Study" and editor for "Mitigating Bias in Machine Learning".

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