I am Plamen,
a Data Scientist based in Berlin.

Explainable Fraud Detection

- Contributed to the Wayfair Tech Blog with an article about Explanation Methods applied to Fraud Detection. Created a Google Colaboratory notebook with illustrating examples.

Fairwashing Explanations with Off-Manifold Detergent

- Published in the International Conference on Machine Learning (ICML) 2020.

- This paper is a continuation of my master's thesis work. We show how ML explanations can be critically manipulated and propose a way to make them more robust.

TV Series Analyzer

- Used the omdb API to obtain the ratings of TV Series.

- Visualized episode ratings in a 2D-matrix using Matplotlib and Seaborn.

- Visualized ratings over time and fit a linear regression model using Seaborn.

- Created a web app using Flask.

Berlin Housing Listings

- Scraped over 3000 listings from immobilienscout24.

- Used Google Geocoding API in combination with the folium library to make an interactive html page.

- Used Google Places API to include stations and supermarkets within 500m radius for every listing.

Click here to browse the page yourself

Hiding Unfair Bias in Neural Networks

- Predicted if US adults make more or less than 50k annually with over 80% acuracy

- Showed how an adversary can mask the feature importance of racial/gender features without affecting the prediction accuracy

Neural Style Transfer for Furniture

- Beat hundreds of engineers and reached the final eight in an internal hackathon at Wayfair.

- Used Keras and TensorFlow to generate artificial pieces of furniture fitting to an arbitrary home style.

Training Neural Networks with Manipulated explanations

- Master thesis work at TU Berlin.

- Trained a VGG16 neural net with the CIFAR-10 dataset and achieved top-1 accuracy of 92.46%.

- Extended current research and showed how popular explanation methods can be manipulated critically.