Viveca Lindahl
Senior Data Scientist
Stockholm, Stockholm County, Sweden
Summary
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Senior🎓
Top SchoolViveca Lindahl is a Senior Data Scientist with a PhD in physics and a decade of experience applying machine learning and statistical modeling to domains from battery health and anomaly detection at Northvolt to credit risk and fraud detection at Froda. She blends deep algorithmic thinking—evident from developing the AWH enhanced-sampling algorithm and contributing it to the widely used GROMACS simulation toolkit—with practical engineering skills to deploy models in production. Her background spans reinforcement learning, time-series modeling, and large-scale simulation performance work, and she has a track record of driving robustness improvements in open-source C++ codebases. Curious and hands-on, she favors learning-by-doing and often tests theory against real-world systems, a mindset rooted in both academic research and industry delivery.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at KTH Royal Institute of TechnologyMaster's degree Engineering Physics, Master's degree Engineering Physics at Uppsala University
Swedish, English
Stackoverflow
1reputation
0reached
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Github Skills (12)
c-language10
acs10
cprogramming-language10
molecular-simulation10
grok10
gr10
r10
fileio9
file-access9
numerical-methods9
file-processing9
file-handling9
Programming languages (3)
Github contributions (5)
gromacs/gromacs
Mar 2016 - Jun 2018
Public/backup repository of the GROMACS molecular simulation toolkit. Please do not mine the metadata blindly; we use https://gitlab.com/gromacs/gromacs for code review and issue tracking.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits in 2 years 2 months
Contributions summary:Viveca significantly contributed to the GROMACS molecular simulation toolkit by implementing and enhancing the AWH (Accelerated Weight Histogram) biasing module. This involved adding new features, such as the ability to read and write AWH data, and integrating force correlation calculations, which are crucial for friction analysis in simulations. The user also fixed issues, such as ensuring the validity of input parameters, and improving the robustness of the code, like when dealing with potentially null log file pointers.
gromacspythonissue-trackinggitlabmolecular
vivecalindahl/awh-tutorial
Apr 2018 - Jun 2018
Contributions:28 commits, 7 pushes, 1 branch in 1 month
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