Martijn de Vries

Rendering Handwritten Equations

As a student at university, I regularly wished I could easily take a picture of my notes and equations and have them show up digitally! With this in mind, I built a tool that can recognize individual symbols in an equation, and render the equation in LaTeX format.

Project Overview and Outcomes:

The tool uses OpenCVs findcontour function to detect symbols in the image. I then built a custom pre-processing pipeline that infers which contours belong together to form symbols, and what the order of the symbols should be. The symbols are then fed to retrained EfficientNetB0 Convolutional Neural Network, and a prediction for each symbol is made. Finally, the predictions for each symbol are stiched into an equation in LaTeX math mode format in the postprocessing pipeline, using the information from the previous steps.

At present, the tool predicts at least 50% of the equation correctly 70% of the time, and makes a perfect prediction about 16% of the time. A more robust version of the tool is currently in development

Solar Intensity Forecasting

As renewable energy sources start to make up an increasingly large share of electricity production in the United States, the challenge of how to integrate these (often variable and irregular) sources into the grid is becoming more and more relevant. In a small team, we constructed a model that can forecast the Solar intensity a few days ahead in Los Angeles, so that the energy grid can be optimally managed.

Project Overview and Outcomes:

In order to train and test our model, we used freely available weather data from the National Solar Radiation Database, which collects weather information at kilometer-resolution throughout the entire US using satellite data. We then used three different neural-net based approaches to forecast the 'Global Horizontal Irradiance', or GHI, several days in the future.

Although the Neural Prophet model performs the best, with the lowest scoring Root Mean Squared Error, it only works with univariate timeseries, and thus requires current GHI measurements to forecast GHI. In contrast, a Recurrent Neural Network can forecast the GHI using weather data, with an average error of 15% at solar noon.

NLP Analysis and Classification of Reddit posts

For this project, I wanted to investigate the way that political polarization in the United States is reflected in language use. In order to gauge this, I built a classification model that can classify posts and comments from two politically-themed subreddits: r/politics and r/conservative.

Project Overview and Outcomes:

I collected data from October 2022, the month leading up to the midterms, from both subreddits using the Pushshift API. After cleaning and EDA, I built separate models for both posts and comments. I used a TF-IDF vectorizer to vectorize the language, as well as a custom scikit-learn transformer to customize the number of bigrams (sets of two words) that are retained in the model. Posts and comments are classified based on the language, as well as some secondary identifying features.

The best-working model is a stacked model consisting of logistic regression, random forest, and Naive Bayes. The model is able to classify 88.6% of posts correctly, and 65.4% of comments. The main difference in performance is simply due to the fact that comments are typically shorter, which makes it harder to categorize them. By studying feature importances of the various models, we can gain interesting insights into the difference in language use in these two subreddits. An example is shown in the in the image to the right, which shows the most predictive bigrams in the Naive Bayes classifier. If one of these bigrams occurs in a post title, the model is much more likely to classify the posts as belonging to r/politics (blue) or r/conservative (red).

Predicting Housing Prices in Ames, Iowa

I used the famous Ames, Iowa housing dataset to build a predictive house prices model using linear regression techniques. In order to find the most predictive features, I performed extensive EDA and built several models of increasing complexity. The plot on the right shows the predicted vs actual sale price for the best performing model.

Project Overview and Outcomes:

Because this dataset has a relatively large number of features (81), extensive EDA and feature engineering was necessary to find the best model. The best-performing model includes a total of 22 features, of which 11 are categorical (and therefore have to be one-hot encoded). Additionally, I found that log-transforming the sale price improves the performance of the model, as the relationship between the predictor variables and the sale price are not strictly linear.

The root mean squared error of the model on the testing data is about $22100. Adding regularization (Ridge Regression) increased model performance by a negligible amount, indicating that the model might still be somewhat underfit.

Data Science Immersive Fellow

• Computer Vision: Designed a stand-alone tool that accepts handwritten math equations as input, then leverages a Convolutional Neural Network to render the equation digitally, correctly predicting at least 50% of the equation in 70% of cases.
• Time Series Forecasting: Predicted solar power available in Los Angeles using a Recurrent Neural Network and weather data from the National Solar Radiation Database, with an average error of 15% at solar noon.
• NLP and classification: Developed a binary classification model which correctly classifies Reddit posts and comments between r/politics and r/conservative with 88.6% accuracy, using logistic regression, random forest, and stacking.

Postdoctoral Researcher

• Communication: Presented results of peer-reviewed studies at US conferences; collaborated with the Chandra press office to craft an accessible, scientifically meaningful, and visually appealing press release - in the top 10% most watched videos on the Chandra X-ray Observatory Youtube Channel.
• Mentorship: Supervised 3 new scientists/researchers in department operating procedures, instrumentation software, and best practices. Instilled data stewardship and rigorous application of statistical models.
• Proposals: Crafted compelling proposals with a success rate ~3x above the average, securing time at astrophysical observatories. As a member of the Chandra Time Allocation Committee in 2022, assessed and evaluated observing proposals based on merit, cost, risk, urgency, and suitability for the observatory.

• Chandra Measurements of Gas Homogeneity and Turbulence in the Perseus Cluster
  Developed a custom statistical method to measure galaxy cluster turbulence, using Bayesian Inference and Markov Chain Monte Carlo Sampling. Reduced error margins by 20%, and redefined benchmarks for simulations, theoretical work, and future observations.
• The Long Filament of PSR J2030+4415
  Discovered new pulsar filament, mapping its full extent with fresh observations, feeding new data-driven insights into the origin and physical nature of these filaments. Developed a new 'filament-finding' algorithm based on this discovery, which will be used to search for filaments in upcoming observations.
• A Quarter Century of Guitar Nebula/Filament Evolution
  Spearheaded first long-term study of a pulsar filament, comparing 4 epochs of observations (data from 25+ years). Developed generalizable and re-usable Python code to fit bow shock models to observational data.

University of Groningen