I want to change the way people use data and models


My name is Fernanda Mora, I'm an Applied Mathematician, Actuary and Computer Scientist born in Mexico City.

My major goal is to generate value through state-of-the-art technologies and models that tackle relevant problems in society, government and companies. My second goal is to promote a quantitative & data-driven approach to problem-solving and decision-making.

To achieve this, I have developed a unique academic & industry experience that combines theory & research with a practical & client-oriented approach. I strongly believe that a variety of attributes, mindsets and experiences are needed to build technology that is able to make a meaningful impact.

Research highlights

Projects and papers

Local layered algorithmic model for topological design of rural telecommunications networks

Conference Paper, 2016

Presented on the International Conference on Operations Research for Development, ICORD 2016. The paper aims to present local layered algorithmic model for the topological design of rural telecommunications networks, which can be used to estimate the infrastructure requirements and associated costs that are necessary to deliver broadband coverage to unconnected rural communities in Mexico. Check the slides here.

An emsemble model for the Stanford Question Answering Dataset (SQuAD)

Summer project, 2016

Worked with Profr. Eric Nyberg as a Visiting Research Scholar in Machine Learning models for Q&A in the Language Technologies Institute at Carnegie Mellon University. The project proposed a modular methodology to solve a reading comprehension problem in the SQuAD dataset presented by [Rajpurkar et al. 2016]. Check the weekly deliverables (eight) here and the summary slides here.

Deep Learning fundamentals with an application to forecast electric energy demand in Mexico

Applied Mathematics thesis with special mention

My bachelor's degree thesis had two main objectives. First, to give a general overview of Deep Learning: what is it, how does it differentiate from general Machine Learning, on what assumptions it relies and notable architectures and applications. Second, to apply a Deep Learning model (a Long Short Term Memory network) to a pressing problem in Mexico, namely, electric energy consumption. The slides can be checked here. The code of the Deep Learning model can be checked here.

Economic Burden of Diabetes Mellitus in Mexico, 2013

Study developed for the National Health Foundation

Diabetes Mellitus type II represents a serious health problem in Mexico with 6.4 million diagnosed patients. The economic cost of the disease is hard to calculate since it generates two types of costs: direct costs including medical treatment and drugs, and indirect costs including costs due to early mortality and disability. In this study an estimation of both costs is proposed.

Recent efforts towards Machine Reading Comprehension: A Literature Review

Literature review, 2016

Progress in automatic reading comprehension has been slow. In response, recently, machine learning approaches have been taken. There has been two major efforts towards the advance of machine reading comprehension: creation of datasets and development of models; we discuss both in this survey: the most relevant and the state-of-the art approaches. Check summary slides here.

On the use of Machine Learning techniques to detect Malware in Android Operative System: A survey

Survey, 2016

In this survey we explore the most novel and relevant approaches for malware detection in Android Operative System using Machine Learning techniques. Specifically, we reviewed the most cited published works from 2012 to 2016. We analyze Machine Learning relevant elements: the features, the dataset, the models and the performance results. We conclude the work with some opportunity areas together with interesting venues of work regarding a general approach to detect Android malware.

Teaching and Learning

Most relevant courses given or taken


Data Mining, Fall 2016

Theoretical and practical graduate class on:

  • Regression (multiple linear regression)
  • Classification (logistic regression, discriminant analysis, support vector machines and neural networks).
  • Clustering (hierarchical and partioning)
  • Time series (linear and non-linear methods)


M.Sc. in Computer Science

  • Computer Architecture
  • Distributed Computing
  • Advanced Operative Systems
  • Programming languages
  • Complexity and Computability
  • Analysis of Algorithms
  • Machine Learning
  • Computational Statistics
  • Big Data
  • Data Mining
  • Programming languages
  • Network security and ethical hacking

B.Sc. in Applied Mathematics

  • Advanced Statistics
  • Advanced Probability
  • Statistical sampling
  • Statistical learning
  • Stochastic processes
  • Bayesian Statistics
  • Simulation
  • Numerical Analysis
  • Advanced Optimization
  • Applied Analysis
  • Dynamical Systems (I and II)
  • Operations Research
  • Statistical Learning
  • Mathematical Analysis (I and II)
  • Functional Analysis
  • Linear Algebra
  • Modern Algebra

B.Sc. in Actuarial Sciences

  • Financial Mathematics (I and II)
  • Life contingencies
  • Reserves
  • Derivatives
  • Risk Modelling
  • Actuarial Models
  • Advanced Microeconomics
  • Macroeconomics
  • Competitive and non-competitive markets
  • Bayesian statistics
  • Pensions
  • Uncertainty Economics
  • Life and property insurance

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