Anna Susmelj
Senior Software Engineer, Machine Learning at Google
Dübendorf, Zurich, Switzerland
Summary
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Senior🎓
Top SchoolAnna Susmelj is a Senior Software Engineer specializing in machine learning with six years of experience building large-scale video and audio representation systems and deploying multimodal embedding models at Google. With a PhD from ETH Zürich and postdoctoral work at ETH AI Center and Facebook AI, she focuses on unsupervised and self-supervised learning, domain robustness, and generative approaches for 3D and biomedical data. She has led applied ML teams as Head of AI at Biognosys, bringing research-grade methods into production while establishing CI/CD and MLOps practices. An ELLIS member and reviewer for ICML, ICLR and NeurIPS, she blends rigorous academic research with pragmatic engineering to optimize training pipelines and serving performance. Notably, her background spans hyperbolic-geometry methods for biological data to conditional diffusion for high-resolution 3D shape generation, reflecting a rare mix of theoretical depth and production impact.
6 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at ETH ZürichLomonosov Moscow State University
English, Russian, German
Github Skills (20)
topological-data-analysis9
combinations8
realm8
branches8
hyperbolic8
cosmology8
geometry7
single-cell-genomics7
autoencoder7
generative7
modeling7
uncertainty6
data-analysis5
deep-learning3
pytorch3
Programming languages (4)
Github contributions (5)
facebookresearch/PoincareMaps
Jun 2020 - Jul 2021
The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.
Contributions:4 commits, 1 PR, 5 pushes in 1 year
brancheshierarchieseuclidean-geometryrealmsingle-cell
klanita/MIC
Nov 2023 - Jun 2024
[CVPR23] Official Implementation of MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Contributions:2 PRs, 60 pushes, 5 branches in 7 months
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