Fabricio Flores

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About Me

My interest are in the areas of Generative AI, LLMs, Mathematical Statistics and Computer Vision.

As a Machine Learning Engineer at AMD, I specialize in NLP, LLMs, and Computer Vision, with a strong interest in generative AI and quantitative finance. My experience includes developing and deploying advanced computer vision models for major retailers in the USA and a top beverage company in Southeast Asia, enhancing their operational efficiency. Additionally, I've contributed to predictive maintenance in refrigeration systems and automated defect detection in semiconductors, significantly impacting technological innovation.

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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 Politécnica Nacional
Quito, Ecuador.

Professional & Research Experience

Nov 2023 - Present

Senior Machine Learning Engineer

Advanced Micro Devices (AMD)
Austin, TX, USA.
Aug 2022 - Nov 2023

Senior Data Scientist

Walmart Global Tech
Dallas, TX, USA.
Jan 2020 - Aug 2022

Data Scientist

Fortive (FTV Employment Services LLC)
Pittsburgh, PA, USA.
Jan 2019 - Dec 2020

Machine Learning Engineer (consulting)

Grid Fruit LCC
Pittsburgh, PA, USA.
Jan 2019 - Dec 2020

Research Associate

Carnegie Mellon University:
- Civil and Environmental Engineering
- Electrical and Computing Engineering
Pittsburgh, PA, USA.
Jan 2019 - Dec 2020

Assistant Lecturer

Carnegie Mellon University:
- Machine Learning Department
Pittsburgh, PA, USA.
Jan 2017 - Aug 2017

Data Scientist

Produbanco Grupo Promerica
Quito, Ecuador.
Jan 2012 - Aug 2014

Liquidity Analyst

Banco Internacional
Quito, Ecuador.

Skills & Abilities

90%
Coding: Python | R
90%
Deep Learning | Natural Language Processing
90%
Data Visualization
90%
PyTorch
70%
Statistics | Financial Mathematics
90%
SQL | Non-SQL | Vector Databases

Services

Consulting Services

Advanced Analytics, Artificial Intelligence and Machine Learning

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 inevitable aspect of industrial operations, making it essential to implement tools that offer early warnings for potential or imminent failures. Predictive maintenance plays a crucial role in both the healthcare sector and in managing refrigeration systems in supermarkets. This approach utilizes data from sensors that continuously monitor the condition of equipment and their environment. The aim is to enhance management practices, optimize energy use, and comply with environmental policies.
The underlying technology for predictive maintenance includes signal processing, sensor fusion, and machine learning. Signal processing helps filter out noise to isolate valuable data, while sensor fusion improves the accuracy of measurements by integrating data from multiple sources. Machine learning and artificial intelligence then analyze this information to predict the likelihood of equipment failure within a specific timeframe, thereby allowing for proactive maintenance strategies.

Improving E-Commerce with Image Quality and Description Accuracy

In a previous project, a detailed score index was created to check image quality for e-commerce product images, focusing on resolution, lighting, composition, and overall appeal. Alongside, Natural Language Processing (NLP) and Large Language Models (LLMs) were used to ensure product descriptions matched the images accurately. This approach improved the shopping experience by providing high-quality images and consistent, accurate product information. The combination of these technologies helps customers make informed decisions and enhances their satisfaction and engagement with the online store.

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

I participated in research focused on detecting and counting people in indoor settings such as classrooms and offices. This project utilized RGB-D cameras, including Kinect and Intel RealSense, to gather depth information. This data was then analyzed using convolutional neural networks to detect and count humans. The resulting occupancy data informed the control of heating and ventilation systems, enhancing the energy efficiency of buildings.

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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.