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Hey there πŸ‘‹

πŸ§‘β€πŸ’» I'm Hilmi, a seasoned Data Scientist with extensive experience in architecting machine learning solutions in the Cloud.

πŸ’Ό Currently, I hold the role of a Machine Learning Engineer at PropertyGuru Singapore, where I specialize in transforming machine learning projects into revenue-generating solutions while optimizing business operations. Notably, I have successfully delivered a B2B solution known as the Automated Valuation Model, which is actively utilized by enterprise customers, including banks and real estate consultants in Malaysia.

πŸ“Š I possess a robust background in analytics and programming. I've made significant contributions to the field by publishing seven papers on Trustworthy Artificial Intelligence in top-tier international conferences and journals, under the guidance of ACM Distinguished Scientist Prof David Lo.

πŸ… Additionally, my expertise in Data Science, particularly in the domains of NLP (Natural Language Processing) and CV (Computer Vision), is exemplified by my profile as a Kaggle Competitions Expert. I also hold an AWS Certified Solution Architect certification, affirming my ability to effectively productionize machine learning projects.

Muhammad Hilmi Asyrofi's Projects

mart icon mart

Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"

merge-repos icon merge-repos

a script to merge several repositories into one codebase

mma_training icon mma_training

Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"

mmocr icon mmocr

OpenMMLab Text Detection, Recognition and Understanding Toolbox

mt-nlp icon mt-nlp

Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models

multimodal-toolkit icon multimodal-toolkit

Multimodal model for text and tabular data with HuggingFace transformers as building block for text data

once-for-all-adversarial-training icon once-for-all-adversarial-training

[NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong Chen*, Shupeng Gui, Ting-Kuei Hu, Ji Liu, and Zhangyang Wang

sadl icon sadl

Code release of a paper "Guiding Deep Learning System Testing using Surprise Adequacy"

search-visualization icon search-visualization

AUSearch | Find an accurate API usage examples here! Save your time on strolling around many source codes

speech-recognizer icon speech-recognizer

simple voice recognizer which is implemented using HTK framework and Julius autoscript

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