Dzmitry Bahdanau
Team Member at Periodic Labs
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolDzmitry Bahdanau is a research-driven software engineer with 15 years of experience bridging deep learning research and production-grade ML systems, currently based in Montreal and serving as a Team Member at Periodic Labs. He has led conversational AI research programs at ServiceNow Research and holds adjunct faculty ties at McGill and a core affiliation with Mila, combining academic rigor with industry impact. His work spans goal-driven dialogue, semantic parsing, AI-based code modeling, and efforts to reconnect symbolic AI with deep learning, often focusing on systematic generalization. A seasoned open-source contributor, he has improved core ML tooling and RL starter frameworks (e.g., adding gradient-norm monitoring and PPO support) and enhanced data pipeline robustness in widely used projects like fuel and BabyAI. Notably, Dzmitry blends hands-on backend engineering experience from Yandex-era search ranking and transliteration classifiers with contemporary research leadership, enabling practical solutions that are informed by deep theoretical insight.
15 years of coding experience
3 years of employment as a software developer
Bachelor's Degree Computer Science, Bachelor's Degree Computer Science at Belarusian State UniversityMaster's Degree Computer Science, Master's Degree Computer Science at Jacobs University Bremen
Russian, Russian, German
Github Skills (17)
pytorch10
hdf10
ppp10
data-pipelines10
python10
machine-learning10
reinforcement-learning10
data-pipeline10
testing9
algorithm9
unit-testing9
imitation-learning9
algorithms9
implement9
unit-test9
Programming languages (6)
Github contributions (5)
mila-iqia/babyai
Jul 2018 - Mar 2020
BabyAI platform. A testbed for training agents to understand and execute language commands.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:267 commits, 27 PRs, 91 pushes in 1 year 8 months
Contributions summary:Dzmitry contributed to the BabyAI platform by implementing features and improving the existing codebase. They introduced a default Proximal Policy Optimization (PPO) algorithm, created a script for printing instructions, and added logging for git information. Furthermore, the user modified the training script to print true rewards and enhanced the logging with a focus on training metrics.
nlpagentsmachine-learningimitation-learningnlp-machine-learning
mila-iqia/fuel
Mar 2015 - Aug 2017
A data pipeline framework for machine learning
Role in this project:Back-end Developer
Back-end DeveloperContributions:3 releases, 60 commits, 54 PRs in 2 years 6 months
Contributions summary:Dzmitry primarily contributed to the `fuel` data pipeline framework, focusing on enhancing the core functionality. They fixed bugs in the `Merge` transformer, introduced support for iteration schemes, and improved the handling of versioning. Furthermore, they added support for the `get_example_stream` method in the `H5PYDataset` class and made other improvements related to data handling and iteration.
pipelinedata-sciencepipeline-frameworkmachine-learningmlops
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