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Pablo Zegers

Co-Founder & Vice President of Product at Anastasia AI

Pablo Zegers was born in Santiago, Chile, in 1968. He received his
Bachelor in Industrial Engineering degree and his Professional
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Pablo Zegers was born in Santiago, Chile, in 1968. He received his Bachelor in Industrial Engineering degree and his Professional Industrial Engineer certification from the Pontificia Universidad Católica, Chile, in 1992, his Master of Science degree from The University of Arizona, USA, in 1998, and his Doctor of Philosophy degree, also from The University of Arizona, in 2002. He started to work as a professor at the College of Engineering and Applied Sciences of the Universidad de los Andes, Chile, in 2002 and left the university as an Associate Professor in 2017. He was the Academic Director of that College from 2006 to 2010, and its Interim Dean for a brief period at the end of 2010. During his stay at the university he led, or was part of, the teams that designed the strategic plan of the college, created Electrical Engineering and Computer Science, renovated the academic curricula of all the departments of the college, hired tens of professors, and reorganized the administrative structure of the college. As a teacher, his main achievement was advising more than 90 engineering students through their final engineering thesis. He has many publications whose topics range from the use of the mathematical theory of information theory in artificial intelligence to the design of algorithms that found thousands of binary stars in the Magellanic Clouds that orbit our galaxy. His interests are information theory, artificial intelligence, machine learning, and neural networks.
Speaker's last talk
SigOpt Summit 2021
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SigOpt Summit 2021
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Democratizing Time Series Forecasting for Any Industry

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Pablo Zegers
Co-Founder & Vice President of Product at Anastasia AI
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Democratizing Time Series Forecasting for Any Industry

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Pablo Zegers at "SigOpt Summit 2021"
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November 16, 2021
Online
16
815
ai, conversation, deeep learning, design, hpc, machince learning, medicine

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