Grad. Researcher at Broad Institute of MIT & Harvard
daviddao at broad.mit.edu
"I am interested in machine learning (especially deep learning) and its applications, including bioinformatics, computer vision, natural language understanding and information retrieval"
Grad. Researcher at Broad Institute of MIT and Harvard
Deep Learning for high dimensional image-based data and cell profiling
Google Summer of Code Fellowship
Coding on Interactive Data Visualization for the Web
Technical University of Munich: Master's Degree in Computer Science
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
Meeting and discussion with Nobel Laureates in Physics
Visiting Student at CERN
Machine Learning and Pattern Recognition in Particle Physics
Automated Plausibility Analysis of Large Phylogenies
Here, we develop an automated plausibility assessment approach and introduce the effective algorithm Plausibility-Check. We conducted experiments on simulated and real data sets using the STBase tree database that show that the overall running time of our algorithm on large phylogenies improves by five orders of magnitude compared to the naıve algorithm. Plausibility-Check is available as part of the RAxML open source code that is available for download at https://github.com/stamatak/standard-RAxML
D. Dao, T. Flouri, A. Stamatakis
Pattern Recognition in Computational Molecular Biology, Wiley & Sons, 2015
Anatomy of BioJS, an open source community for the life sciences
BioJS is an open source software project that develops visualization tools for
different types of biological data. Here we report on the factors that influenced the growth of
the BioJS user and developer community, and outline our strategy for building on this
growth. The lessons we have learned on BioJS may also be relevant to other open source
G. Yachdav, T. Goldberg, S. Wilzbach, D. Dao, I. Shih et al.
BioJS is a community-based standard and repository of functional components to represent biological information on the web.
Deep Learning in Action
Deep Learning in Action is a regular speaker series in Munich with 350+ participants about Machine Learning (especially Deep Learning).
Allows interactive exploration and analysis of data, particularly from high-throughput, image-based experiments. Included is a supervised machine learning system which can be trained to recognize complicated and subtle phenotypes, for automatic scoring of millions of cells.