Tech Guru Talks with Lorena Mesa

    Tech Guru Talks A technical talk and workshop series created to empower Puerto Rico and LATAM’s burgeoning developer and technological community to grow and gain access to world-class speakers and developers. Become a tech guru in your own right as the best in tech fields share their expertise, insights, and stories.Moderated By: SeriouslyCreative! Upcoming Talks:Wednesday, August 19th: Your Data Is Biased, Here's Why: Fairness, Accountability, and Transparency in Machine LearningWho? Lorena Mesa, Data Engineer and Software Intelligence Systems at Github and Director and Chair of the Board of Directors of Python Software Foundation. A political scientist turned coder, Lorena Mesa is a GitHub data engineer, Director & Chair of the Python Software Foundation, JOSS editor, and PyLadies Chicago co-organizer. Lorena's time at Obama for America and her subsequent graduate research required her to learn how to transform messy, incomplete data into intelligible analysis on topics like predicting Latinx voter behavior. It's this unique background in research and applied mathematics that drove Lorena to pursue a career in engineering and data science. One part activist, one part Star Wars fanatic, and another part Trekkie, Lorena abides by the motto to "live long, python, and prosper".About the Tech Talk: Over the last ten years, we've seen an influx of ever new, customizable tech products ranging from social media to smart home technology espousing a promise that this tech product is the right fit for you. Why?  Their product fit can learn your preferences, adapt to your needs, help you in ways that are unique to you, the consumer. The innovation driving the personalization of all things tech can be attributed to the rise of artificial intelligence, or more specifically machine learning. The "brain" behind the product, the push to build personalized technology solutions has led to a data economy where the most valuable commodity is the consumer themselves. What does this tradeoff of data generation however place in the engineering of our products? At what cost should we aim to build products that can "learn about you"? Together we'll explore the evolution of new algorithms that are exploring this question, to see what costs we suffer if fairness, accountability, and transparency are left out of engineering and build scope.

    Piloto 151 Global

    Tech Guru Talks 

    A technical talk and workshop series created to empower Puerto Rico and LATAM’s burgeoning developer and technological community to grow and gain access to world-class speakers and developers. Become a tech guru in your own right as the best in tech fields share their expertise, insights, and stories.

    Moderated By: SeriouslyCreative

    Upcoming Talks:

    Wednesday, August 19th: 

    • Your Data Is Biased, Here's Why: Fairness, Accountability, and Transparency in Machine Learning
    • Who? Lorena Mesa, Data Engineer and Software Intelligence Systems at Github and Director and Chair of the Board of Directors of Python Software Foundation. A political scientist turned coder, Lorena Mesa is a GitHub data engineer, Director & Chair of the Python Software Foundation, JOSS editor, and PyLadies Chicago co-organizer. Lorena's time at Obama for America and her subsequent graduate research required her to learn how to transform messy, incomplete data into intelligible analysis on topics like predicting Latinx voter behavior. It's this unique background in research and applied mathematics that drove Lorena to pursue a career in engineering and data science. One part activist, one part Star Wars fanatic, and another part Trekkie, Lorena abides by the motto to "live long, python, and prosper".
    • About the Tech Talk: Over the last ten years, we've seen an influx of ever new, customizable tech products ranging from social media to smart home technology espousing a promise that this tech product is the right fit for you. Why?  Their product fit can learn your preferences, adapt to your needs, help you in ways that are unique to you, the consumer. The innovation driving the personalization of all things tech can be attributed to the rise of artificial intelligence, or more specifically machine learning. The "brain" behind the product, the push to build personalized technology solutions has led to a data economy where the most valuable commodity is the consumer themselves. What does this tradeoff of data generation however place in the engineering of our products? At what cost should we aim to build products that can "learn about you"? Together we'll explore the evolution of new algorithms that are exploring this question, to see what costs we suffer if fairness, accountability, and transparency are left out of engineering and build scope.
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