Hi! My name is Luda.

I'm currently work as a Software Engineer at Airbnb, living in San Francisco, CA. I recently graduated from Stanford University with a combined Bachelors/Masters degree in Computer Science, and my main interests are focused on machine learning and data mining. I'm especially interested in using cutting-edge techniques to tackle tough, interdisplinary problems in a variety of real-life contexts. I've worked on projects ranging from improving urban livibility through network analysis, uncovering trends in historical manuscript illustrations using neural networks, to predicting poverty metrics using road typology.

I am an explorer at heart. Traveling is one of my passions, and you can see some of my adventures(and misadventures) here. In my spare time, I also enjoy beaches, swimming near beaches, All-you-can-eat KBBQ, reading, and collecting subway maps.

Love to chat to anyone about interesting ideas, so drop me a line! Connect with me on LinkedIn or email me at luda AT cs.stanford.edu.


Here's some of the personal projects I'm currently working/have worked on before!


In collaboration with the British Library, we are exploring how we can apply Convolutional Neural Networks to aid art history research. We used transfer learning to provide over 20 million new tags to over 1 million book illustrations from the 1500s to the 20th century. We presented this project in London for the 2016 British Library Labs Competition and was thrilled to receive 1st place in the contest.

Project Urban Oasis

Recent trends in urban design have focused on “re-humanizing” city centers by converting motor ways into pedestrian and bike-only roads. We used open-source GIS data to build road network models and applied graph network analysis to evaluate potential roads for pedestrian/bike conversions.

Baidu Deep Learning Internship

During my summer internship at Baidu Beijing, I worked on optimizing their internal deep learning framework(inspired by Caffe), which is used behind the scenes to power the search engine used by around 1 Billion people. I built automatic gradient check systems, network visualizations, and dabbled in experimentation with state-of-art deep NN models to improve accuracy.

OpenStreetMap Road Network Analysis for Poverty Mapping

Objective poverty metrics are crucial for development, yet these data are lacking in many countries. We used publicly available OpenStreetMap data to calculate infrastructure robustness features, which are then used to predict key poverty measures for countries in sub-Saharan Africa.

City of Cycles: Beijing

City of Cycles: Beijing tells a story of an ancient city through modern web visualizations. It displays the growth of the city rings throughout the 20th century and provide interesting historical contexts along the way.


A new rendition of a classic board game built on JS/HTML5, designed for simplicity. Supports single-play or multi-player over network modes.

Other Stuff

Here's some other interesting links/blurbs.

  • Travel Blog

    I semi-frequently update my blog with my travels/musings. Here's a map detailing where I've been.

  • Teaching

    Teaching rocks. I was a Course Assistant for many Stanford CS classes, including CS231N: Convolutional Neural Network and CS246: Mining Massive Datasets . I also taught as Section Leader for the CS106 program at Stanford for many quarters, as well as at BreakoutMentors, teaching K-12 students how to code. I do private tutoring with kids 1-on-1 in CS and Math, so please reach out if you know someone who needs tutoring!