MyStory – Afterlife AI & Data In The Frozen World With Bruno Guerreiro
The recent successes of Covid-19 vaccines discovery and Perseverance Rover’s Descent and Touchdown on Mars showed how AI is increasingly adopted across diverse industries. And at the same time, death has never been so present in our life. Although a taboo in many society, death seems to be an inevitable human being condition. Some religions are relating about an afterlife or resurrection in another body. And many people in the real life have kept their loved ones alive in the digital world and even plan their digital legacy with digital estates and digital assets. Knowing the exponential amount of data generated by this pandemic situation, are data scientists the ones able to effectively tackle real-world problems and help in more intelligent and better-informed decision-making? My answer is no. Today in MyStory Serie of Unexpected Data, you will hear the story of our guest that decided to self-learn data science and programming from scratch. And all of that in purpose to elucidate new discoveries in the area of bio preservation.
Bruno M. Guerreiro was born in 1995 in Setúbal, Portugal. He holds a B.Sc. and M.Sc. in Biochemistry from NOVA School of Science and Technology. Currently, he is a Ph.D. Biochemistry student, specializing in biophysics. Most recently, he won a Fulbright Scholarship to study at University of California, Berkeley, one of the best institutions for researching cryopreservation. He has 5 years of experience in cryobiology, specifically at the intersection of biotechnology and cell cryopreservation and has published 3 papers in the prestigious journals Carbohydrate Polymers and Cryobiology. His research focuses on elucidating new structure-function relationships between biocompatible polymers and ice growth avoidance/binding.
Given the multidimensionality of his complex task, he decided to self-learn data science and programming from scratch because he recognized the usefulness of Big Data analytical strategies to solve his problem. Right now, he is attempting to create a feature engineering framework in cryobiology by analyzing big chunks of untapped literature and using the newground insights to filter out lab experimentation load.
Soon to come
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