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Dr. **** *** AI Research Scientist

$600 / day


A professional machine-learning scientist who holds a Ph.D. degree in Engineering with more than six years’ ML/AI experience. Highly motivated to leverage state-of-the-art ML/AI methods for solving complex problems in the real world. Strong capacity to critical thinking, autonomous learning, and efficient teamwork.


AutoencoderBayesian StatisticsClassificationCloud High Performance Computing - LinuxClusteringComputer Vision/Image Processing - Image generationDeep Learning - CNNDenoisingDiffusionDomain TransformEngineeringGANGradioMachine LearningMATLABMatplotlibNumPyOpenCVPandasPlotlyPymc3PYTHON - 6+ year experiencePyTorchPytorch-lightningRegistrationRegressionResearchScikit-LearnSegmentationSkimageSQLSuper-resolutionTensorFlowTransformer
AI ConsultantAI EthicistAI Research ScientistAI Software DeveloperMachine Learning Engineer


July 2018 - Jan 2022 Doctor of Philosophy – Engineering at University of New South Wales
March 2016 - March 2018 Master of Philosophy – Engineering at University of New South Wales
Sept 2012 - July 2014 Master of Engineering at China University of Geoscience
Sept 2008 - July 2012 Bachelor of Science at China University of Geoscience


March 2022 - Present Postdoctoral Fellow in Machine Learning at CSIRO

– Developed a Bayesian hierarchical model for linear regression between metal grades for uncertainty evaluation and reduced the posterior predictive variance of the metal grade
by up to 59.14% to aid stakeholders for decision-making. The related publication:
– Developed a Bayesian linear regression with Gaussian Mixture Likelihood for outlier detection in the linear regression between metal grades and reduced the mean square error up
to 99.5% compared with maximum likelihood estimation, supported decision-making for stakeholders.
– Designed a fine-tuning solution based on GAN for digital image denoising from large computer vision model – VGG19 and improved perceptual image quality (SSIM) by 8.78%.
– Developed advanced deep learning models (Transformer, LSTM, RNN) and statistical models (ARIMA) on time series data prediction from physical sensors for decision-making in
– Designed advanced deep learning models (Self-supervised learning: Noise2Void, Noise2Noise; Generative AI models: Diffusion related models, GAN based models; Supervised methods:
CNN based models) to denoise digital ore images and dramatically improved the speed of image reconstruction by 75%.
– Utilized Power BI to visualize data for stakeholders.
– Supported various colleagues on designing advanced deep learning algorithms (ControlNet, Diffusion, Transformer, GAN, CNN).
– Presented on internal/external seminars and conferences.
– Wrote/Published scientific journal papers.
– Supervised a Ph.D. student at UNSW on physical-guided machine learning for spectrum data analysis

Jan 2022 - March 2022 Machine Learning Specialist at UNSW

– Designed artificial neural networks for physical property prediction
– Implemented deep learning models for material characterization
– Conducted research on neural network architectures for predicting material behaviour
– Utilised machine learning algorithms to forecast physical properties
– Developed AI models to simulate material responses