Fabricio Flores


Machine Learning | Applied Mathematics

About Me

My interest are in the areas of Applied Machine Learning, Quantitative & Data Analytics and Computer Vision.

I work as a Data Scientist at FORTIVE (FTV Employment Services LLC) on projects related with sensor data analysis (data acquisition and applied machine learning). My primary focus is on projects related with Telemetry Signals, Asset Monitoring, Asset Failure Prediction and Sensor Fusion for motion estimation.

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Education

MSc Advanced Infrastructure Systems

Carnegie Mellon University
Pittsburgh, PA, USA.

MSc Computational Mechanics

Carnegie Mellon University
Pittsburgh, PA, USA.

BSc Mathematics

Escuela Polit├ęcnica Nacional
Quito, Ecuador.

Professional & Research Experience

Data Scientist

Fortive (FTV Employment Services LLC)
Pittsburgh, PA, USA.

Machine Learning Engineer (consulting)

Grid Fruit LCC
Pittsburgh, PA, USA.

Research Associate

Carnegie Mellon University:
- Civil and Environmental Engineering
- Electrical and Computing Engineering
Pittsburgh, PA, USA.

Assistant Lecturer

Carnegie Mellon University:
- Machine Learning Department
Pittsburgh, PA, USA.

Data Scientist

Produbanco Grupo Promerica (Banking)
Quito, Ecuador.

Liquidity Analyst

Banco Internacional (Banking)
Quito, Ecuador.

Services

Consulting Services

Advanced Analytics, Artificial Intelligence and Machine Learning

Skills & Abilities

85%
Coding: Python / R / MATLAB / C++
80%
Web Data Extraction: Selenium
80%
Data Visualization: Plotly / DASH / SHINY
70%
Keras - TensorFlow
70%
Cloud Computing (AWS, Google, Azure)
60%
ETL: SQL / Non-SQL

Languages

English Proficient level
Spanish First Language

Hobbies & Interests

Drone Flight
Photography
Computer Programming

Machine Learning for Asset-Failure prediction and monitoring

Asset failure is an unavoidable part in every industrial process and thus it is necessary to count-on with the necessary tools that provide early warnings in case of potential or catastrophic failures. Notabily, predictive maintenance is central for systems and devices that are being used in the healthcare industry as well as refrigeration assets used in supermakets. Asset failure prediction uses data from sensing devices, that are continuously collecting the state of these systems and its surroundings, with the goal of providing better management, optimizing energy consumption and ensure environmental policy compliances.
The technology that allows this type of prediction relies on signal processing, sensor fusion and machine learning. Signal Processing is used to extract useful information from noisy signals while Sensor Fusion technologies are used with the purpose of reduce uncertainty on the measurements. Finally, Machine Learning and Artificial Intelligence tools predict the likelihood of failure within certain timeframe.

Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor

I collaborated in research involving human detection and counting in indoor environments (classrooms, offices). In this project, RGB-D cameras (Kinect and Intel RealSense) were used to capture depth information that later on was used to perform human detection and counting using convolutional neural networks. The occupancy information was then used to control heating and ventilation systems in order to improve building energy usage.

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Machine Learning for Retail Analytics

As a research associate, (under the guidance of CMU Professor, Javad Mohammadi), I collaborated on projects related to building energy management and retail analytics for one of the largets supermakets in the USA and partnerships industries in Thailand respectively. In particular, I worked on real-time analytics in order to increase inventory turnover, track product freshness and optimize warehouse through inventory management.

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