Matt Wichlinski
Partner (Workplace And Employment) at HopgoodGanim Lawyers
Queensland, Australia
Summary
🤩
Rockstar🎓
Top SchoolMatt Wichlinski is a Partner specialising in workplace and employment law with 11 years’ experience advising government bodies and major private and public sector organisations across industrial relations, WHS and complex employment disputes. He combines courtroom-proven litigation skills with hands-on policy and HR advisory work, having led high-stakes matters from unfair dismissals to enterprise bargaining and public interest investigations. Based in Queensland, Matt is known for pragmatic problem-solving, building client relationships, and delivering outcomes that exceed expectations. Unusually for a senior lawyer, he also contributes to machine learning projects on GitHub—bringing a data-driven, technical curiosity to legal strategy and process improvement. Collected across top national firms and local government, his background reflects both procedural rigour and a track record of managing sensitive, high-profile matters.
11 years of coding experience
8 years of employment as a software developer
The Southport SchoolGraduate Diploma of Legal Practice, Graduate Diploma of Legal Practice at Bond University
Github Skills (15)
neural-network10
keras10
machine-learning10
lstm10
genetic-algorithm10
deep-learning10
tensorflow10
python10
reinforcement-learning10
classification10
classify10
faster-rcnn9
mask-rcnn9
pygame8
game-development7
Programming languages (8)
Github contributions (5)
harvitronix/reinforcement-learning-car
Feb 2016 - Sep 2017
Using reinforcement learning to teach a car to avoid obstacles.
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 230 commits, 12 PRs in 1 year 7 months
Contributions summary:Matt's contributions primarily revolve around training a car to avoid obstacles using reinforcement learning. They focused on implementing and refining the neural network model, experimenting with sensor configurations, and adjusting reward functions to improve performance. The user also made changes to the game environment and training parameters to optimize the learning process. Their work involved iterative improvements to the car's navigation capabilities within the simulated environment.
obstaclespythonreinforcement-learningdeep-reinforcement-learningmonte-carlo-tree-search
harvitronix/five-video-classification-methods
Mar 2017 - May 2019
Code that accompanies my blog post outlining five video classification methods in Keras and TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:1 release, 106 commits, 29 PRs in 2 years 2 months
Contributions summary:Matt contributed to the development and refinement of video classification methods using Keras and TensorFlow. Their commits focused on updating and optimizing model architectures, particularly for LSTM, CRNN, and Conv3D models, aligning them with the final version described in a blog post. They added functionality to load sequences into memory and made improvements to the training and validation processes, including migrating to newer Keras arguments, demonstrating a focus on model training and performance.
my-blogdeep-learningoutliningmachine-learningvideo-classification
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