Abdul Ansari
Senior Applied Scientist at Amazon Web Services (AWS)
Berlin, Germany
Summary
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Rockstar🎓
Top School
Top expert inQuantitative Finance and Algorithmic Trading with Machine Learning
Abdul Ansari is a Senior Applied Scientist at AWS AI in Berlin with 11 years of experience bridging deep generative modeling research and production ML engineering. He holds a PhD in Computer Science from NUS, where his thesis and publications at CVPR, ICLR, NeurIPS and AAAI focused on probabilistic generative models, variational inference, and time-series modeling. At AWS he has shipped improvements to widely used open-source time-series toolkits (Contributions to gluonts and AutoGluon’s Chronos), reflecting a strong track record of turning research ideas into robust, production-ready code. He enjoys adapting mathematical and physical techniques—recently optimal transport and stochastic differential equations—into ML models, particularly for images and temporal data. Comfortable across research, teaching, and engineering contexts, he combines rigorous theoretical grounding with practical bug fixes, evaluation improvements, and feature work that improve reproducibility and usability.
11 years of coding experience
2 years of employment as a software developer
Indian Institute of Technology RoorkeeDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at National University of Singapore
English, Hindi
Stackoverflow
6,267reputation
1.1mreached
200answers
22questions
java
top-5%
python
top-5%
image
top-5%
computer-vision
top-5%
opencv
top-5%
Github Skills (30)
transformers10
pytorch10
python10
machine-learning10
time-series10
forecasting10
deeplearning-ai10
deep-learning10
fine-tuning10
forecast10
automated-machine-learning10
image9
data-science9
pandas9
java9
Programming languages (7)
Github contributions (5)
awslabs/gluonts
Aug 2022 - Jan 2023
Probabilistic time series modeling in Python
Role in this project:
ML Engineer
ML EngineerContributions:68 reviews, 10 commits, 50 PRs in 5 months
Contributions summary:Abdul primarily contributed to the improvement and maintenance of time series forecasting models. Their work included fixing bugs related to date shifting in the R forecast wrapper, improving the plotting functionality of the `QuantileForecast` class, changing the fallback predictor in `SeasonalNaive` to `nanmean`, adding NaN validation to the evaluator, fixing methods to work with `rpy2 v3+`, adding missing value imputation to `SeasonalNaive`, and updating the Dockerfile for R forecast models. They also added a new dataset and removed MXNet from default dataset paths.
forecastingpythontime-series-analysistimeseries-forecastingaws
autogluon/autogluon
Mar 2024 - Mar 2025
Fast and Accurate ML in 3 Lines of Code
Role in this project:ML Engineer
ML EngineerContributions:73 reviews, 15 PRs, 6 pushes in 11 months
Contributions summary:Abdul contributed significantly to the `autogluon/autogluon` repository, specifically within the time series forecasting module, Chronos. Their work focused on enhancing the fine-tuning capabilities of Chronos models, including adding support for fine-tuning, addressing CPU-related issues, and improving test coverage. These changes included adjusting default parameters for fine-tuning and removing evaluation logic, ultimately refining the model's functionality.
forecastingimage-textmlppythonmeta-learning
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