David Dao
daviddao at broad.mit.edu
Machine Learning Engineer
I work on applied artificial intelligence. My research focuses on deep neural networks, data-driven medicine and autonomous driving systems. I was a researcher at the Broad Institute of MIT and Harvard. Currently, I work at Mercedes-Benz on autonomous cars. I believe strongly that my job as a researcher is to advance and serve the field, and the public broadly. Thus, I am a firm believer in open source and am organizing Germany's largest machine intelligence meetup and Harvard University's refugees and education conference."

Research at Mercedes-Benz Research & Development North America

Fall 2016

Deep Learning for Autonomous Driving


Grad. Researcher at Broad Institute of MIT and Harvard

Fall 2015

Deep Learning for Image-Based Chemical-Genetic Profiling

Google Summer of Code Fellowship

Summer 2014

Coding on Interactive Data Visualization for the Web

Technical University of Munich: Master's Degree in Computer Science

Since 2014

Major in Computer Science. Focus on Machine Learning, Bioinformatics, and Computer Vision


Researcher at Heidelberg Institute for Theoretical Studies

2014 - 2015

Pattern Recognition in Computational Molecular Evolution


Karlsruhe Institute of Technology: Bachelor's Degree in Computer Science

2010 - 2014

Major in Computer Science and Minor in Physics. Focus on AI, Machine Translation, and Machine Learning


62nd Lindau Nobel Laureate Forum Participant

Summer 2012

Meeting and discussion with Nobel Laureates in Physics

Visiting Student at CERN

Fall 2010

Machine Learning and Pattern Recognition in Particle Physics


CellProfiler Analyst: interactive data exploration, analysis, and classification of large biological image sets
David Dao, Adam N. Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh and Anne E. Carpenter
Bioinformatics (2016)
Automated Plausibility Analysis of Large Phylogenies
David Dao, Tomas Flouri, Alexandros Stamatakis
Pattern Recognition in Computational Molecular Biology, Wiley & Sons (2015)
Anatomy of BioJS, an open source community for the life sciences
Guy Yachdav, Tatyana Goldberg, Sebastian Wilzbach, David Dao, Iris Shih, Saket Choudhary, Steve Crouch, Max Franz, Alexander García, Leyla J García, Björn A Grüning, Devasena Inupakutika, Ian Sillitoe, Anil S Thanki, Bruno Vieira, José M Villaveces, Maria V Schneider, Suzanna Lewis, Steve Pettifer, Burkhard Rost, and Manuel Corpas
eLife (2015)

My Projects

STN TensorFlow
The Spatial Transformer Network (STN) allows the spatial manipulation of data within neural networks. It is part of Google's TensorFlow models repository.
Residual Learning Papers
Awesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks (50+ layers), especially residual neural networks.
CellProfiler Analyst
Free software for cell biologists that allows the interactive exploration and machine learning analysis of subtle phenotypes in high throughput, image based experiments.
I am one of the core developer of the BioJS project: an open source library of JavaScript components to interactively represent biological data in the web.
Deep Learning in Action
Deep Learning in Action is a regular speaker series in Munich with 1000+ participants about Machine Learning (especially Deep Learning).
Harvard Refugedu
Co-organized first-ever refugee education conference at Harvard University that strives to engage researchers, practitioners and policymakers from Europe, the Middle East and the US.