Curriculum Vitae

Gary Beane CV - ML/AI Consultant with PhD in Materials Science, Google Advanced Data Analytics certified

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Contact Information

Email: gbeane66@gmail.com
LinkedIn: linkedin.com/in/gary-beane
GitHub: github.com/gbeane66
Google Scholar: scholar.google.com/citations?user=DdumthMAAAAJ
Website: garybeane.com

Professional Summary

ML/AI Consultant delivering production machine learning solutions across sensor intelligence, data pipeline architecture, and computer vision applications. PhD Materials Science with 10+ years research experience, now applying rigorous scientific methodology to solve real-world business problems. Proven track record in consulting engagements, open-source development, and deployed ML systems.

Core Competencies: Machine Learning Consulting • Sensor Intelligence & IoT • Data Pipeline Architecture • Computer Vision • Data Science Strategy • Scientific Computing • Production ML Deployment

Certifications & Education

Professional Certifications

Google Professional Certificate in Advanced Data Analytics (2024)
Advanced statistical analysis, predictive modeling, Python for data science, machine learning fundamentals

Ubuntu Linux Professional Certificate — Canonical

Academic Degrees

Doctor of Philosophy — Materials Science (2010–2014)
University of Melbourne

  • Commonwealth PhD Scholarship recipient
  • Research focus: Nanoscale optical properties and light-matter interactions
  • Developed novel experimental methodologies and statistical analysis techniques
  • Published in high-impact journals including ACS Nano and JPCL.

Bachelor of Science (Honours) — Physical Chemistry (2009)
University of Melbourne

  • First Class Honours
  • Thesis: Advanced spectroscopy and molecular dynamics

Bachelor of Science — Chemistry & Mathematical Physics (2004–2008)
University of Melbourne

  • First Class Honours
  • Double major: Chemistry and Mathematical Physics

Bachelor of Arts — Political Science (2004–2008)
University of Melbourne

  • First Class Honours
  • Focus: Public policy and governance

Technical Skills

Machine Learning & AI

Deep Learning: PyTorch, TensorFlow, Keras, Hugging Face Transformers
Computer Vision: OpenCLIP, CLIP, FastAI, U-Net, Detectron2
NLP: Transformers, BERT, GPT architectures
Reinforcement Learning: Stable-Baselines3, OpenAI Gym, PPO, DQN
Traditional ML: Scikit-learn, XGBoost, LightGBM, ensemble methods

Data Science & Analytics

Programming: Python, SQL, Julia, R
Data Processing: Pandas, NumPy, Polars, Dask, Apache Spark
Visualization: Matplotlib, Seaborn, Plotly, Tableau
Statistical Analysis: Hypothesis testing, A/B testing, experimental design
Tools: Jupyter, Quarto, Weights & Biases, MLflow

Software Engineering

Languages: Python, Rust, FORTRAN, JavaScript
DevOps: Docker, Kubernetes, Git, GitHub Actions, CI/CD
Cloud Platforms: AWS (EC2, S3, Lambda, SageMaker)
Web Frameworks: FastAPI, Flask, Gradio, Streamlit
Testing: pytest, unittest, integration testing, TDD
Databases: PostgreSQL, SQLite, MongoDB

Research & Scientific Computing

Methodologies: Experimental design, statistical analysis, signal processing
Scientific Libraries: NumPy, SciPy, SymPy, numerical methods
Instrumentation: Laboratory automation, data acquisition systems
Optics & Physics: Light scattering, spectroscopy, microscopy
High-Performance Computing: Parallel computing, optimization, Rust

Professional Experience

ML/AI Consultant

Independent — 2024 – Present

Delivering production machine learning solutions across sensor intelligence, data pipeline architecture, and data science applications. Project-based consulting focused on applied ML with clear business ROI, emphasizing hardware-adjacent and science-driven projects.

Sensor Intelligence & IoT - ML solutions for medical device sensors and biosensing applications - Anomaly detection and pattern recognition in sensor data - Signal processing pipelines for noisy real-world data - Data pipeline architecture for IoT systems - Edge AI optimization for embedded sensors

Data Analytics - Predictive modeling and performance analytics (including racing data) - Time-series analysis and forecasting - Statistical analysis and optimization frameworks - Real-time data processing pipelines - Data-to-insights pipeline development

Key Achievements: - Built privacy-first AI platform (Aani) approaching beta release - Enhanced sensor intelligence through ML-driven signal processing - Delivered production-ready ML systems with focus on real-world impact - Developed open-source scientific computing tools (pytmat, 42K+ downloads)

FLEET Research Fellow

Monash University, Clayton, VIC, Australia
November 2018 – 2025

  • Led multiple research projects from design through data analysis and publication
  • Designed, built, and commissioned world-class ultrafast laser optics laboratory
  • Managed faculty-wide research symposia as Chair of Early Career Network
  • Co-supervised two PhD students to successful thesis completion
  • Published high-impact research in leading materials science journals

Key Achievements: - Established new experimental capabilities for nanoscale optical measurements - Secured competitive research funding - Mentored early-career researchers and graduate students - Organized cross-disciplinary research events

Postdoctoral Research Fellow

University of Notre Dame, Notre Dame, IN, USA
November 2016 – November 2018

  • Supervised PhD student and managed laboratory operations
  • Designed and built Fourier imaging microscope leading to high-impact publications
  • Conducted transient absorbance microscopy measurements
  • Published multiple peer-reviewed papers in top-tier journals

Key Achievements: - Developed novel microscopy techniques - Collaborated with international research teams - Advanced ultrafast spectroscopy methodologies

Postdoctoral Research Fellow

University of California, Merced, CA, USA
March 2014 – November 2016

  • Supervised PhD and Masters students on quantum dot synthesis projects
  • Published high-impact papers on interfacial defects in core-shell quantum dots
  • Optimized ultrafast transient absorbance spectroscopy setup
  • Managed laboratory operations and safety protocols

Key Achievements: - Discovered novel mechanisms in quantum dot photophysics - Improved experimental throughput and data quality - Mentored graduate researchers

Publications & Research

Google Scholar: View full publication list
ORCID: 0000-0001-5312-0477

Selected high-impact publications in materials science, nanoscience, and optical spectroscopy. Publications cited 1000+ times with work appearing in journals including Nature Communications, ACS Nano, and Journal of Physical Chemistry.

Research Background: 10+ years investigating nanoscale materials, optical properties, and light-matter interactions. Expertise in experimental design, statistical analysis, and scientific computing now applied to ML/AI consulting.

Leadership & Community

Faculty of Science Early Career Network — Chair

Monash University (2020–2025)

  • Organized university-wide research symposia and networking events
  • Managed committee budget and strategic planning
  • Represented early-career researchers in faculty governance

Open Source Contributions

  • Creator and maintainer: pytmat
  • Active contributor to ML/AI community

Professional Development

Continuous Learning: - Hugging Face Deep Reinforcement Learning Course (Certificate) - FastAI Practical Deep Learning for Coders - Advanced Data Analytics (Google Professional Certificate) - Cloud computing and ML deployment

Technical Writing: - Documentation and tutorials - Code examples and reproducible notebooks

Areas of Interest

  • Applied Machine Learning & AI Consulting
  • Racing Data Analytics & Performance Optimization
  • Sensor Intelligence & IoT Applications
  • Computer Vision & Multimodal AI
  • Time-Series Forecasting & Predictive Modeling
  • Materials Science & Scientific ML
  • Production ML Deployment & MLOps
  • Data Science Strategy

References

Available upon request

Last updated: October 2025