ML engineer. Singapore.
“the gunplay is tight but the story drags in the back half”
1,232 possible titles. No server.
I build machine-learning systems that end up in front of people, not just in a notebook. Most of my work starts with a messy signal, a sensor stream, a pile of past papers, four million game reviews, and ends with something measured and shipped.
Right now I am a third-year Computer Science and Design undergraduate at the Singapore University of Technology and Design, on exchange at the University of Waterloo for the autumn term. Before this I spent four months at Bosch building time-series models for virtual sensing, and I join Infineon in December as a data science and automation intern.
The projects below are all live or reproducible. The models on this site run in your browser rather than on a server, which felt like the honest way to let you check my work rather than take my word for it.
Data Scientist and Automation Intern · Singapore
Machine Learning R&D Intern · Singapore
Solo
Finds every past paper an exam question has appeared in, cites each appearance exactly, then marks a written answer one mark point at a time and explains why a correct-sounding answer still scored zero.
30,176mark points parsed from 315 papers
Industry project
A learning platform for the Dyslexia Association of Singapore that clusters learner assessment data into readable cohort profiles and generates differentiated literacy activities through a curriculum-grounded retrieval pipeline that refuses rather than invents.
Team of 8149 commits, ~16,700 lines
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Team of 5
Detecting machine-generated text when the test set is drawn from entirely different source corpora than the training set, which turned a model-selection exercise into a domain-shift one.
0.801Macro F1, up from 0.657; beats the shared task's fine-tuned RoBERTa at 0.7342
Solo
Two DistilBERT models over 4.2 million game reviews: one reads sentiment, the other names which of 1,232 games a review is about from the text alone.
60.7%top-1 across 1,232 titles, against a ~0.08% random baseline. Sentiment: 94.9% accuracy, 0.949 F1
UROP, self-initiated
An educational-content pipeline that turns notes into flashcards, a video and a PDF, implemented three separate ways behind one interface so the concurrency models could be compared directly.
3 runtimesThreadPoolExecutor, asyncio and Vertex AI ADK, over 253 Python and 45 front-end tests
Front end is deployed; backend hosting is still in progress, so generation does not run live yet.
Solo
ResNet50 transfer learning over mammogram and ultrasound images, plus a DICOM ingestion pipeline for the larger clinical archive.
99.7%on the original, non-augmented set, against an 83% majority-class baseline. I am re-auditing whether the augmented training set was derived from these same images, which would make this optimistic
BEng Computer Science and Design
GPA 4.99/5.0. SUTD ASEAN Scholarship. Honours List AY 2024/2025.
Exchange semester, Computer Science (Faculty of Mathematics)
Canada-ASEAN SEED-2 Scholarship recipient (Government of Canada).
Available for an internship, May 2027 – Sep 2027.
The fastest way to reach me is owencliffton@gmail.com. I am also on LinkedIn and GitHub.