Kemal Kurniawan - Research Fellow at University of Melbourne

Kemal Kurniawan

Research Fellow at University of Melbourne

Melbourne, Victoria, Australia

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Summary

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Rockstar

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Top School

Kemal Kurniawan is an NLP researcher and Research Fellow at the University of Melbourne with 11 years of experience spanning applied industry roles and academic research. He studies human label variation in legal-domain NLP and has a track record of moving models from research to production, including dialogue-based image retrieval at Amazon and Indonesian NER and summarization systems at Kata.ai. His background blends rigorous PhD-level research with hands-on engineering—evident from contributions like a well-maintained PyTorch CRF implementation where he implemented core algorithms, testing and documentation. Kemal also regularly teaches and mentors across machine learning courses and capstone projects, translating complex methods into practical curricula for students and industry practitioners. Comfortable across backend engineering, model implementation, and reproducible research, he brings both deep technical depth and an educator’s knack for clear, usable artifacts.

11 years of coding experience

3 years of employment as a software developer

MSc, Artificial Intelligence, Distinction, MSc, Artificial Intelligence, Distinction at The University of Edinburgh

The University of Melbourne

S.Kom., Computer Science, 3.87, S.Kom., Computer Science, 3.87 at University of Indonesia

Indonesian, English, Japanese

Github Skills (10)

pytorch10

machine-learning10

python10

documentation10

pytest9

neural-network9

algorithms8

data-structures8

algorithm8

data-structure8

Programming languages (9)

C++RustCSCSSJavaScriptJupyter NotebookRubyPython

Github contributions (5)

kmkurn/pytorch-crf

Nov 2017 - Jan 2022

(Linear-chain) Conditional random field in PyTorch.

Role in this project:

Back-end Developer & Documentation Specialist

Contributions:5 releases, 16 reviews, 143 commits in 4 years 2 months

Contributions summary:Kemal primarily implemented and refined core functionality for a conditional random field (CRF) model in PyTorch. Their work involved defining the CRF class, implementing the forward computation and decoding methods, and fixing various issues within the model. Additionally, the user was responsible for documenting the code, updating the README, and integrating build and testing infrastructure.

pytorchdeep-learningconditionalneural-networkslinear

kmkurn/text2array

Jan 2019 - Jul 2022

Python library to convert text dataset into arrays.

Contributions:14 PRs, 82 pushes, 18 branches in 3 years 5 months

python-librarypythondeep-learningnumpydataset

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