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Eduardo Cueto Mendoza

Flat 2~~~
~~~ 236 North Circular Road~~~
~~~ Dublin 7, Co. Dublin~~~
~~~ P: +353 (087) 434-7184~~~
~~~ cueto303@gmail.com

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PhD Researcher

At the moment doing research on neural networks and convex optimization, learning Julia and Rust. Playing Red Dead Redemption 2 and L.A. Noir.

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Education

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Technological University Dublin

Current
PhD Computer Science

My current work explores the interaction of DNN and their energy efficiency and size in storage/memory. I am currently exploring methods that combine Topological Data Analysis into the generation of slimmer architectures, also simultaneously profiling state of the art architectures to fully understand the interaction of hardware and the amount of energy consumed. The former data is compared with other classical or other variations of the architecture to truly see if the increment in accuracy/precision does not require an exorbitant amount of energy (compared to our gains).

Centro de Investigación en Computación - IPN

Aug 2016 - Jun 2018
Master in Computer Science

Learned state of the art machine learning algorithms and natural language processing techniques. Thesis work was done towards the computational relationships of group theory and natural languages.

Universidad Autónoma de Ciudad Juárez

Aug 2011 - May 2016
Bachelor in Mathematics

Learned a variety of methods to prove statements in different areas of mathematics. Optative course work was geared towards applied mathematics: optimization, probability theory, functional analysis, stochastic ODE, etc. Thesis was required for the degree, work was done in stochastic optimization.

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Work Experience

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Infolink-EXP.

Feb 2019 - Mar 2020
Lead Data Analyst

Applied ML to mine relevant information from our customers and integrate it to our platform called Zahoree. This platform is a customer journey modeling system, my contribution to the team is the processing of all our natural language sources (tickets, complaint emails, and reviews) and transform this data into information that is important for our customers business intelligence.

Indboo.

Jan 2018 - Jan 2019
Odoo Python Developer

Developed modules for Odoo (Open ERP) using Python, Javascript and PostgreSQL. The team used Agile Scrum and DevOps as the development methodology. Used docker and Git as version control. Started the job remotely. We modified Odoo to adjust it to our customers’ needs.

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Languages

  • Spanish: Mother tongue
  • English: fluent TOEFL 97; 2018
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Honors, Awards

  • 2018: Graduated Master of Computer Science with Honors
  • 2014: Best scientific poster award bachelor category, Universidad Autónoma de Ciudad Juárez.
  • 2010: Basic science award in physics 2nd place, Instituto Tecnológico de Ciudad Juárez.
  • 2010: Basic science award in mathematics 3rd place, Instituto Tecnológico de Ciudad Juárez.
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Research Activity and Thesis

  • 2018 Workshop on Computational Mathematics Problems. (CIMAT)
  • 2018 Methods of Analysis of Noncommutative Groups. (Master Thesis)
  • 2016 Using the AW-SDG algorithm for faster parameter learning in a regression. (Bachelor Thesis)
  • 2013: Two-month research visit as part of the Mexican National Academy of Science summer research program at Instituto Politecnico Nacional / Escuela Superior de Economia, Researched Financial Mathematics under Dr. Francisco Venegas Martínez.
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Conference Participation

  • 2017 Numerical Analysis of the construction of a similarity matrix using an algorithmic measure on abstract groups; IPN International CORE 2017 Congress.
  • 2014: Entropy as the limit for data compression, XLVII National Congress of The Mexican Mathematical Society.
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Interests

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  • Neural Networks
  • Convex/Combinatorial Optimization
  • Statistical/Machine Learning
  • Information Theory
  • Julia Programming Language
  • COBOL
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Open Source Projects

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  • Green Flux: Functions that measure the approximate kWh electricity draw and non-embedding Floating Point operations consumed in the training of a Flux.jl model.

  • Flux Compress: Functions and structures that extend Flux.jl layers to allow the compression of models.