Rasmus Palm
Machine Learning Engineer at twig.energy
Denmark
Summary
🤩
Rockstar🎓
Top SchoolRasmus Palm is a machine learning engineer and researcher with 16 years of experience building production-grade ML systems and publishing academic work. He holds a PhD in Machine Learning from DTU and has led ML teams at Tradeshift, where he designed a zero-setup, online classifier-as-a-service that handled millions of models and thousands of requests per second (released as open source Blayze). His work spans end-to-end deep learning for document information extraction, hands-on model implementation (including convolutional predictive encoders), and scalable ML infrastructure. Currently at twig.energy, he combines research rigor from his postdoc with pragmatic engineering to deliver high-throughput, fault-tolerant ML services. An early contributor to DeepLearnToolbox and front-end improvements on Selenium docs, he blends deep technical breadth with practical product impact.
16 years of coding experience
10 years of employment as a software developer
Student, Mathematics, Student, Mathematics at Espergærde GymnasiumTechnical University of Denmark
English, Danish
Stackoverflow
1reputation
3kreached
2answers
1question
Github Skills (21)
machine-learning10
matlab10
octave10
mask-rcnn10
deep-learning10
neural-network10
html10
faster-rcnn10
web-development10
javascript9
autoencoder9
ui-design8
ux-design8
google-analytics8
postmessage6
Programming languages (12)
Github contributions (5)
rasmusbergpalm/DeepLearnToolbox
Oct 2011 - Dec 2015
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
Role in this project:
ML Engineer
ML EngineerContributions:130 commits, 2 PRs, 3 pushes in 4 years 1 month
Contributions summary:Rasmus primarily contributed to the development and training of deep learning models using the provided MATLAB/Octave toolbox. Their work involved implementing and checking the numerical gradients for Convolutional Neural Networks (CNNs) and neural networks. They introduced code for the Stacked Convolutional Predictive Encoder (SPAE) and added examples to demonstrate and test the functionality of various deep learning models.
autoencodersneural-netsoctavetensorflowstacked
SeleniumHQ/www.seleniumhq.org
Mar 2010 - Sep 2010
Old Selenium website and docs (deprecated)
Role in this project:Front-end Developer
Front-end DeveloperContributions:11 commits in 6 months
Contributions summary:Rasmus primarily contributed to the UI/UX of the "Bromine" project, a Selenium-related tool. They added and updated project links, incorporated Google Analytics tracking for downloads and screencast views, and modified the display of the screencast image. The user also updated the Bromine version details on the download page.
selenium
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