Curriculum Vitae

Production vision systems, from model behavior to device release.

I build camera and image AI systems across model development, C++/Android integration, visual-quality evaluation, and production handoff. My public work extends that discipline into reliable agent and multimodal workflows.

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Profile

Senior AI Engineer with production depth in camera AI, computational photography, edge deployment, and C++/Android integration. I turn model behavior and visual-quality feedback into deployable systems, hard-case evaluation, and maintainable handoff, while building tested public references for reliable agent workflows.

Target roles: Applied AI Engineer, Forward Deployed Engineer, Computer Vision Engineer, and Multimodal AI Engineer.

Professional Experience

Senior AI Engineer | Black Sesame Technologies (Singapore)

Jan 2025 - Present

  • Own end-to-end delivery for camera and image AI features, translating product requirements and field feedback into technical scope, prototypes, evaluation gates, and production handoff.
  • Lead real-time composition and portrait-imaging systems combining detection, optical-flow tracking, segmentation and matting, monocular depth, GPU rendering, and application state behavior.
  • Drive model-to-device delivery across PyTorch, quantization, TFLite/ONNX, C++/Android integration, GPU pipelines, and on-device debugging.
  • Coordinate product, camera tuning, runtime, QA, and customer-facing teams around reproducible issue sets, acceptance decisions, and release readiness.

AI Engineer | Black Sesame Technologies (Singapore)

Aug 2022 - Jan 2025

  • Productized vision models for portrait segmentation and matting, monocular depth, object detection, and camera-oriented image processing.
  • Built repeatable training-to-deployment workflows spanning data preparation, training, quantization, export validation, runtime assumptions, and model replacement checks.
  • Integrated models into native C++ and Android camera pipelines with simulation, cross-platform builds, device deployment, and visual debugging.
  • Developed depth-aware bokeh and portrait-rendering workflows with hard-case datasets, batch comparison, golden cases, and release-review tooling.

Research Intern | National University of Singapore

Dec 2021 - Jul 2022

  • Conducted computer-vision research for construction-site safety, including dataset preparation, image-classification experiments, and spatial risk analysis.
  • Co-authored a peer-reviewed Automation in Construction paper on supervised and self-supervised feature representation learning.

Research Assistant | Shandong University

Sep 2019 - Jun 2021

Applied computer vision and machine learning to interdisciplinary psychology research, supporting dataset development, experiments, visualization, and reporting.

Selected Systems

Reliable Agent Workflow

Provider-neutral Python reference for retry, fallback, validated output contracts, confidence gates, traces, and executable hard cases.

Code and tests

Photography Mentor

Browser-local image diagnostics, retrieval, practice planning, review state, and evidence-aware coaching.

Open the system

Production Vision & Edge AI

Training-to-deployment workflows, quantization and export validation, native C++/Android integration, on-device debugging, and release checks.

Read selected system evidence

Technical Stack

  • AI and vision: PyTorch, TensorFlow, OpenCV, segmentation, matting, monocular depth, detection, optical flow, local retrieval.
  • Runtime and deployment: Python, C++, Android/NDK, TFLite, ONNX, ONNX Runtime, TensorRT, OpenGL/GLSL, Docker, FastAPI/Flask.
  • System quality: quantization, export validation, validated output contracts, retry and fallback references, hard-case suites, traces, release gates.

Education & Publication

Master of Technology, Intelligent Systems | NUS-ISS

2021 - 2022 | GPA 4.33 / 5.00

Bachelor of Engineering, Software Engineering | Shandong University

2017 - 2021 | GPA 86.86 / 100 | Outstanding Graduate

Peer-reviewed publication

Co-author, "Automated classification of 'cluttered' construction housekeeping images through supervised and self-supervised feature representation learning," Automation in Construction, 2023. DOI