Neeratyoy Mallik is a PhD student at AutoML Freiburg with 8 years of experience applying and engineering scalable hyperparameter optimization methods for deep learning. His research and code have produced published work (IJCAI, NeurIPS dataset track) and practical tools such as DEHB and HPOBench, reflecting a blend of rigorous experimentation and reproducible benchmarking. He contributes to prominent open-source infrastructure—most notably enhancements to the widely used openml-python API—improving dataset handling, task management, and robustness for community datasets. Prior roles span research analyst and team lead positions in industry where he built end-to-end ML systems, prototyped IoT and NLP solutions, and taught data science foundations. Based in Freiburg, Germany, he combines academic depth with hands-on software development and a proven track record of shipping well-tested features and benchmarks. A less obvious strength is his history of socio-entrepreneurial and teaching initiatives, showing an ability to translate technical work into real-world impact and knowledge transfer.
8 years of coding experience
6 years of employment as a software developer
10+2 (CBSE), 10+2 (CBSE) at Rajhans Vidyalaya
10 (ICSE), 10 (ICSE) at Arya Vidya Mandir, Juhu
Bhaktivedanta Swami Mission School, ISKCON
Master's degree, Computer Science, Master's degree, Computer Science at The University of Freiburg
Bachelor of Engineering (B.E.), Computer Science, Bachelor of Engineering (B.E.), Computer Science at Indian Institute of Engineering Science and Technology, Shibpur
OpenML's Python API for a World of Data and More 💫
Role in this project:
Data Scientist & Backend Developer
Contributions:79 reviews, 121 commits, 51 PRs in 2 years 2 months
Contributions summary:Neeratyoy's contributions primarily involved enhancing the OpenML-Python API's data handling and task management capabilities. They implemented optional data split downloads, added error handling for task IDs, and improved the integration of class labels within task metadata. Furthermore, the user refactored code related to dataset fetching, particularly for sparse data handling, and extended the API to accommodate additional functionalities like listing evaluation measures. They also worked on adding unit tests for the new functionalities, improving the quality of the code.
Contributions:41 commits, 1 PR, 44 pushes in 1 month
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