Shuxin Qiao

Applied Machine Learning · Intelligent Systems · Software Engineering

Google Scholar / LinkedIn / GitHub

About

I'm an applied machine learning practitioner and software developer working at the intersection of AI, software engineering, and real-world operations.

I currently work as an Analyst (Analytics & Machine Learning) at Singapore LNG (SLNG), where I develop data-driven models, analytics applications, and decision-support tools for energy operations. My work extends beyond individual models to the systems around them, including data pipelines, backend services, web applications, APIs, software architecture, and integration with operational workflows. I enjoy connecting these different layers to turn technical ideas into useful, reliable tools.

Previously, I worked as a Research Assistant and Software Developer at the University of Alberta, exploring scientific machine learning for subsurface energy engineering, particularly neural surrogate models for CO₂ storage simulations. My research also extended into deep reinforcement learning, LLM-based agent workflows, and remote-sensing applications.

My interests have gradually expanded from developing and evaluating models to designing complete intelligent systems. I am especially interested in problems that combine mathematical reasoning, machine learning, optimization, and practical software engineering.

Outside my professional work, I enjoy experimenting with reinforcement learning, interactive applications, visualization, and small independent projects. I like building things that are not only technically interesting, but also tangible, testable, and fun to use.

Experience

Analyst (Analytics & Machine Learning) Singapore LNG Corporation Pte Ltd / Singapore / Sep 2025 – Present

Research Assistant and Software Developer GeoResourceCloud Research Group, Department of Civil and Environmental Engineering, University of Alberta / Edmonton, Canada / Feb 2024 – Sep 2025

Education

Bachelor of Science, Statistics & Computing Science University of Alberta / Edmonton, Canada / 2018 – 2022

Publications

• 2026 / Deep reinforcement learning optimizing for geological CO₂ storage considering geomechanical and leakage risks
  Gamze Erdogan Erten, Bo Zhang, Shuxin Qiao, Walid Ben Saleh, Jeff Boisvert
  Geoenergy Science and Engineering, 262, 214483.

• 2025 / Geo-Resource Agent for Automated Reservoir and Mechanical Earth Model Characterization By Integrating Large-Language Models and Domain Specialized Toolbox
  Shuxin Qiao, Walid Ben Saleh, Ziming Xu, Vaniya Tariq, Bo Zhang
  International Geomechanics Conference (IGS 2025).

• 2025 / Deep Learning-Based Geomechanical Upscaling Technique for Reservoir Models Considering Lithological Heterogeneities and Discontinuities
  Xuehan Yin, Shuxin Qiao, Bo Zhang
  59th U.S. Rock Mechanics/Geomechanics Symposium (ARMA 2025), D041S056R002. DOI.

• 2025 / Coupled Flow-Geomechanics Surrogate Model with Flexible Boundary Conditions for Geological CO2 Storage Using Fourier Neural Operator Based Gated Recurrent Network
  Shuxin Qiao, Walid Ben Saleh, Bo Zhang
  SPE Reservoir Simulation Conference (SPE RSC 2025).

• 2024 / Smart Coupled Flow-Geomechanical Upscaling Technique for Oil Sands
  Xiaoyan Ou, Shuxin Qiao, Bo Zhang
  ARMA/DGS/SEG International Geomechanics Symposium (IGS 2024).

• 2024 / Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
  Willow Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Michael A. Chapman, Linlin Xu, Jonathan Li
  arXiv:2410.05717.

• 2023 / Nighttime Light Missing Data Retrieval Using Modis Version 6 Satellite Data and Mask Dilated Partial Convolutional Neural Network
  Xuanchen Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Lingfei Ma, Jonathan Li
  IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023).