Anders Damgaard
Associate Professor
Copenhagen, Capital Region of Denmark
Summary
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Rockstar🎓
Top SchoolAnders Damgaard is an associate professor and environmental engineer with 13 years of research experience focused on implementing life cycle assessment (LCA) models for solid waste management and developing a next-generation waste LCA platform. Based at DTU in Copenhagen, he blends deep academic expertise—from a PhD in Environmental Engineering to postdoctoral work in the US—with hands-on modeling of waste treatment processes and systems-level sustainability analysis. His background includes software and ML work—contributions to a deep learning Python repo implementing convolutional layers and autoencoders—revealing a practical comfort with computational methods that strengthen his LCA tool development. Known for bridging rigorous research and applied engineering, he routinely translates complex environmental data into decision-ready models for waste management and policy.
13 years of coding experience
12 years of employment as a software developer
MSc, Environmental Engineering, MSc, Environmental Engineering at North Carolina State UniversityHolstebro Gymnasium & HFBSc, Environmental Engineering, BSc, Environmental Engineering at Purdue UniversityMSc, Environmental Engineering, MSc, Environmental Engineering at Technical University of Denmark
Github Skills (6)
neural-network10
machine-learning10
convolutional-neural-networks10
deep-learning10
python10
autoencoder10
Programming languages (3)
Github contributions (5)
andersbll/deeppy
Sep 2014 - Apr 2016
Deep learning in Python
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:234 commits, 2 PRs, 44 pushes in 1 year 6 months
Contributions summary:Anders's commits primarily focus on implementing deep learning functionalities within the project, as seen from the introduction of convolutional layers, autoencoders, and the use of numerical gradient checks. The commits show a deep understanding of machine learning concepts, with an emphasis on image recognition and deep learning techniques using the cudarray library. Furthermore, the user's work includes refactoring and restructuring the project, which included a restructuring of the project into layers, as well as, improvements to training and optimization, with the inclusion of different loss functions and learning rules.
pythondeep-learningnumpymachine-learningneural-network
andersbll/neural_artistic_style
Sep 2015 - Oct 2016
Contributions:16 commits, 14 pushes, 1 branch in 1 year 1 month
artisticpython
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