# Diego Raygoza-Castanos

> Software Engineer, Microsoft

Location: Greater Boston, United States
Profile: https://flows.cv/diegoraygozacastanos

## Work Experience
### Software Engineer @ Microsoft
Jan 2022 – Present | Redmond, Washington, United States

### Machine Learning Software Engineer Engineer Intern @ Palo Alto Networks
Jan 2021 – Jan 2021
•	Developed the synthetic data generator for time-series machine learning codebase.
•	Learned traditional methods of generating synthetic data through statistical techniques such as ARIMA.
•	Ran experiments on the TimeGAN (Tensorflow 2.0) and Copulas methods in Vertex AI (GCP).
•	Applied statistical learning schemes on deep learning models to capture time-series trend/seasonality.
•	Created a user interface to easily generate synthetic data based on real data or a desired distribution. 
•	Presented results of synthetic time-series data generation algorithms in a presentation fair.
Technologies Used: Python, Tensorflow (2.0), Keras, Numpy, GCP (Vertex AI), Conda, Jupyter, Scikit-Learn, Git

### Undergraduate Student Researcher @ MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Jan 2020 – Jan 2021 | Cambridge, Massachusetts, United States
- Developed a scheme and pipeline responsible for randomly sampling 2D images of meshes rendered in OpenGL from 3D data environments such as Replica.
- Created a pipeline which supplied sampled images to ProgressiveGAN or StyleGAN algorithms.
- Ran various experiments on remote GPUs in a bash environment.
- Developed Python scripts and notebooks that used analysis tools such as PCA to create GIFs and figure which visualized the latent space of generative models of 3D space.

Technologies Used: C++, OpenGL, Python, Jupyter, PyTorch, Scikit-Learn, Git, bash, conda, numpy, matplotlib, Linux, bash

### Undergraduate Student Researcher @ MIT Quest For Intelligence
Jan 2019 – Jan 2020 | Cambrige, Massachusetts
- Learned probabilistic and statistical techniques such as Markov Processes, principal component analysis, and time lagged component analysis and their implementations in Python 3 (Scikit-Learn, Numpy). 
- Learned Latent NeuralODEs, autoencoder, and variational autoencoders (PyTorch). 
- Learned and implemented (Python 3) the numerical method known as the Nudged Elastic Band (NEB) method which helped find minimum energy paths. 
- Developed multiple NeuralODE based models with PyTorch (Python 3, conda environment) for the problem of finding minimum energy paths. 
- Ran various experiments on remote GPUs in a bash environment showing that NeuralODE based models which were able to perform better than the traditional NEB method.

Technologies: Scikit-Learn, Numpy, Python, Jupyter, Git, Linux, bash, PyTorch, conda

### Data Science Intern @ SECURITI.ai
Jan 2020 – Jan 2020 | San Jose, California, United States
- Learned about the Deep Equilibrium family of deep learning models and their implementations (PyTorch). 
- Developed a feedforward Deep Equilibrium Model in PyTorch (Python 3). 
- Compared the performance of the developed Deep Equilibrium model with Securiti.ai's own model and a state-of-the-art Transformer by performing experiments in Amazon's SageMaker services.

Technologies Used: Python, PyTorch, conda, Git, Jupyter, Linux, bash, AWS, SageMaker

### Undergraduate Student Researcher @ MIT Department of Mechanical Engineering (MechE)
Jan 2019 – Jan 2019 | Cambridge, Massachusetts
- Learned the open source C++ MRAG-I2D simulation software, in particular its numerical simulations of the solutions of the Navier-Stokes equations. 
- Contributed newly proposed optimization of the simulation of fish fin-like foils to MRAG-I2D in C++ (STL). 
- Ran experiments of optimization software on remote machines in a Linux (Bash) environment and compared the simulated performance to the results predicted by the literature of origin.
- Made visualizations of simulations in terms of motion and performance in Python 3 (matplotlib).

Technologies Used: C++, STL, Git, Linux, Bash, Python, matplotlib, numpy

### Undergraduate Student Researcher @ MIT Civil and Environmental Engineering
Jan 2019 – Jan 2019 | Cambridge, Massachusetts
- Developed, in collaboration, a Python 3 framework meant to score the performance of transportation systems under the upcoming and promising TOD standard. 
- Implemented ArcGIS in a pipeline for spatial-based calculations of the metrics in the TOD standard within the Python framework. 
- Applied the framework on data of Boston's MBTA system to test its relative performance according to the TOD standard.

Technologies used: Python 3, ArcGIS


## Education
### Bachelor's degree in 6-2 & 18: Computer Science And Electrical Engineering, Mathematics
Massachusetts Institute of Technology


## Contact & Social
- LinkedIn: https://linkedin.com/in/diego-rc
- GitHub: https://github.com/Diego-Raygoza

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Source: https://flows.cv/diegoraygozacastanos
JSON Resume: https://flows.cv/diegoraygozacastanos/resume.json
Last updated: 2026-03-31