Edwin Salcedo
- Systems Engineer
- Lecturer
- Data Scientist
- Research Engineer

I'm a lifelong learner. To summarise, I am committed to developing innovative intelligent systems to improve other's lives. My academic research and professional projects occur at the intersection of four fields: Machine Learning, Internet of Things, Computer Vision, and Software Engineering.
MIT Innovator Under 35
Awarded due to my research in the computerized tomography field and its reutilization with virtual reality.
Chevening Scholar 2017/2018
Sponsored by the British government to further my studies in Advanced Software Engineering
Bio: MIT Innovator Under 35, Chevening Alumni, and Systems Engineer. Edwin completed his Master's Degree in Digital Business Administration MBA at the Polytechnic University of Valencia (Spain). Then, he upgraded his tech and academic skills studying an MSc in Advanced Software Engineering at The University of Sheffield (United Kingdom) where he later worked as a Research Software Engineer and Graduate Teaching Assistant. He currently works as a Lecturer and Research Engineer in IoT and ML at UCB.
Outside of the academic arena, Edwin has had extensive experience as a software engineer, IT manager, and team lead. Until August 2020, he worked as a Research and Data Science Consultant for Hivos and Swisscontact.
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Modules Taugh: DIT-500 Internet of Things Fundamentals, SIS-111 Programming Fundamentals, DAA-530 Unsupervised Machine Learning.
Developing an IoT system for a smart saline level monitoring device. Creating ML models for risk trajectories and anomaly detection.
Santiago, Chile
2020 - 2021
2020 - 2021
Sheffield, United Kingdom
2017 - 2018
2017 - 2018
Valencia, Spain
2016 - 2017
2016 - 2017
La Paz, Bolivia
2016 - 2016
2016 - 2016
La Paz, Bolivia
2011 - 2015
2011 - 2015
I’m always interested in personal initiatives or professional projects where I can apply my knowledge in IoT,
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Machine Learning, Computer Vision, and Software Engineering.
Automatic system for rainfall monitoring and prediction with IoT and Machine Learning
Abstract: Environment information such as precipitation, temperature, solar radiation, soil and ambient humidity is fundamental to identify the climate change patterns across a country. For example, the monthly and daily records of these meteorological variables are important to locate regions that present water deficit (droughts) or excess (floods). By, monitoring, analysing, and evaluating this data, it is not only possible to take effective ac Continue reading...
Author(s): Edwin Salcedo Aliaga
International Congress of Computer Engineering (INFO 2017), Lima, Peru
I'm currently based in La Paz, Bolivia 🇧🇴
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