Huang Lin

Huang Lin

Ph.D. candidate specializing in machine learning for signal-based indoor localization. My research focuses on developing and evaluating deep learning models to improve accuracy and robustness in real-world environments.

About Me

I am a Ph.D. candidate specializing in machine learning and data-driven modeling for signal-based indoor positioning. My work focuses on designing and training deep learning models, building data processing pipelines, and applying statistical analysis to improve prediction accuracy and system performance.

I have hands-on experience as a machine learning engineer, developing and evaluating models for signal analysis, localization, and predictive tasks. I enjoy transforming complex data into actionable insights and creating efficient AI solutions for real-world applications.

Research & Technical Interests

Machine Learning and Deep Learning · Data Science and Predictive Modeling · Natural Language Processing · Computer Vision · Signal-Based Indoor Localization · AI Applications in Communication and Intelligent Systems

Professional Experience

Machine Learning Engineer (Contract), Mercor
Nov 2025 – Present

  • Designed structured task plans and data templates to standardize analytical workflows and improve reproducibility.
  • Translated complex problem statements into clear, data-driven task specifications to support automated evaluation.
  • Performed error and root-cause analysis on model-generated outputs, identifying data inconsistencies and implementing corrective improvements.
  • Partnered with cross-functional teams to clarify requirements, improve documentation, and enhance overall data workflow reliability.

Machine Learning Engineer (Intern), Ericsson
Mar 2021 – Apr 2023

  • Developed machine learning frameworks for 5G indoor localization and signal analysis.
  • Built data pipelines and feature-engineering workflows using Python, Pandas, and scikit-learn.
  • Trained and benchmarked deep learning models in TensorFlow and PyTorch.
  • Conducted data cleaning, exploratory analysis, and performance evaluation.

Sales Operations Analyst, China Telecom
Jul 2017 – Jan 2021

  • Designed SQL-based dashboards to monitor KPIs and identify sales trends.
  • Maintained customer data accuracy and generated reports for strategic decisions.
  • Collaborated with regional teams to enhance operational efficiency.

Education

Ph.D. in Engineering, University of Regina
Jan 2021 – Apr 2025 (Expected)
Research: Machine learning algorithms for signal-based indoor localization
Awards: Saskatchewan Innovation and Excellence Graduate Scholarship (2022, 2025)

B.Sc. in Electrical Engineering, South China Agricultural University
Sept 2013 – Jun 2017
Award: Second Prize Scholarship

Publications

Journal Articles

  • Lin, H., Li, S., Peng, W., & Peng, A. (2026). An enhanced group matching method with transformed fingerprints, similarity filtering, and adaptive selection for indoor localization. Internet of Things. [DOI]
  • Lin, H., Chen, Y., Li, S., & Peng, W. (2025). A dual-mode framework for indoor localization via temporal learning and knowledge distillation. Ad Hoc Networks. [DOI]
  • Lin, H., Li, S., & Peng, W. (2025). Group matching method for search-space reduction, development, proof, and comparison. Expert Systems with Applications. [DOI]
  • Lin, H., Purmehdi, H., Fei, X., Zhao, Y., Isac, A., Louafi, H., & Peng, W. (2023). Two-stage clustering for improved indoor positioning accuracy. Automation in Construction. [DOI]
  • Lin, H., Li, S., & Peng, W. A prior-probability-driven structured attention network for indoor localization. Neural Computing and Applications. (Under Review)
  • Lin, H., Li, S., Peng, W., & Purmehdi, H. Exploring sampling parameters and localization performance in a multi-layer, multi-structure 5G environment. Traitement du Signal. (Under Review)

Conference Papers

  • Nakhaeepishkesh, M., Peng, W., & Lin, H. (2024). A Self-Organizing Map Artificial Neural Network to Improve the K-Means Algorithm on the Classification of Different Cancers. Engineering Proceedings. [DOI]
  • Khorram, A., Lin, H., & Peng, W. (2024). A Novel SFDN+ DNN Approach for Efficient Hand Movement Recognition Using Surface Electromyography Signals. Engineering Proceedings. [DOI]
  • Lin, H., Purmehdi, H., Zhao, Y., & Peng, W. (2022). Building 5G Fingerprint Datasets for Accurate Indoor Positioning. In IEEE Future Networks World Forum (FNWF). IEEE. [DOI]
  • Pusapati, S., Selim, B., Nie, Y., Lin, H., & Peng, W. (2022). Simulation of NR-V2X in a 5G Environment using OMNeT++. In IEEE Future Networks World Forum (FNWF). IEEE. [DOI]

Technical Skills

Programming and Data Tools: Python, MATLAB, C/C++, SQL, R, Tableau
Machine Learning and Deep Learning Frameworks: PyTorch, TensorFlow, Keras, scikit-learn, Pandas, NumPy, OpenCV, Matplotlib, Seaborn
Core Techniques: Feature Engineering, Data Modeling, Exploratory Data Analysis, Statistical Analysis, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision

Certifications

  • IBM Data Science Professional Certificate
  • DeepLearning.AI Machine Learning Specialization
  • DeepLearning.AI NLP Specialization
  • NVIDIA Computer Vision for Industrial Inspection
  • NVIDIA Fundamentals of Deep Learning