Yan ** AI/ML Engineer

As an algorithm engineer on the brink of receiving a PhD in deep learning and computer vision, I bring a robust data-driven approach to problem-solving, complemented by strong engineering and research capabilities. Throughout my academic journey from 2017 to the present, I have successfully implemented machine and deep learning solutions tackling classification, segmentation, and reconstruction challenges. My proficiency spans over 7 years in Python, leveraging tools like NumPy, scikit-learn, Pandas, TensorFlow, and PyTorch to enhance data analysis workflows and automate pattern recognition processes. Additionally, I have applied Matlab for research purposes and industrial C programming for hardware design verification simulations. My experience extends to drafting research grant proposals, leading project management efforts, and fostering collaboration across interdisciplinary teams in both academic and commercial environments. With effective communication skills refined through tutoring, conference presentations, and collaborative engagements, I am well-equipped to drive innovation and deliver impactful solutions in the field of deep learning and computer vision.

Skills

Boosting, Computer Vision, CUDA, Deep Learning (CNN, GAN), Docker, GIT, Machine Learning (SVM), NumPy, Pandas, Python (TensorFlow, PyTorch, Scikit-Learn), SQL (MySQL, SQL Server, SQLite), Transformer, XGBoost.

Education

2020 - 2024 Doctor of Philosophy at University of New South Wales
July 2021 - June 2022 Visiting Ph.D at ShanghaiTech University
2017 - 2020 Master of Engineering at Northwestern Polytechnical University
2013 - 2017 Bachelor of Engineering at Northwestern Polytechnical University

Experience

Oct 2020 - Present PhD Researcher at Metal Artifacts Reduction for Clinical Data
– Cleaned images from hospitals, selected proper images and built an in-house dataset to start this study.
– Developed an unsupervised deep framework under the CT imaging principle using the inherent physic domain consistency to address unavailable ground truth problems in clinics.
– Proposed to evaluate algorithm indirectly by the subsequent segmentation to prove clinical effectiveness.
– Collaborated with other researchers exploring domain consistency from experiments and published a technical paper.
– Designed a framework specifically eliminating drawbacks of imbalanced data to increase true positives and false negatives (hard to trade-off in airway segmentation) simultaneously.
– Analysed tubular airway tree from a topological view and designed/implemented differentiated distance map and surface loss functions to improve clinically important metrics, i.e., the continuity.
– Presented my research outcomes at an international conference.
– Applying Point Clouds on Tubules in Medical Image Analysis.
– Converted the volumetric leakages to point clouds segmentation to leverage the sparsity of tubules and converted the breakage filling to point clouds regression of tube extension direction and length to overcome the initial problem of fine-grained shape inconsistency in point clouds completion.
– Designed a deformable module to enhance the invariance of point clouds in learning.
– Proposed directional aggregation operation to overcome direction information missing in point clouds and improve the continuity learning to build a complete airway tree.
– Developed a pipeline of airway segmentation to achieve good results for better navigation in the surgery.

Sept 2018 - Present Academic Tutor at University of New South Wales
– Conducted machine/deep learning, computer vision, and data analysis tutorials in accessible terms, adapting to diverse student skill levels to ensure universally satisfactory learning outcomes.
– Communicated with each student in the classroom to encourage participation and to create and maintain an inviting classroom environment.

Sept 2019 - Apr 2020 M.E. Researcher at Brain Tumour Segmentation
– Designed a deep learning method addressing multi-modality image merge to utilize data effectively.
– Designed a multi-level decoder to enlarge the scale of the network and avoid training collapse problems.
– Participated in the BraTs Challenge, achieved top-rank performance, and published two technical papers.

Apr 2020 - Nov 2020 Algorithm Engineer at SigmaStar Technology Ltd
– Deployed deep learning algorithms and debugged our quantization algorithm (Python) to improve robustness and tested the performance of algorithms provided by customers on our chip, providing reports to customers.
– Developed the chip simulation (C programming) and conducted bit-true with IC engineers to verify the correctness of chip design.

Apr 2019 - July 2019 Software Engineer at Cooperation Project from master group
– Packaged the algorithms of our group under the API guidelines (Python) and deployed algorithms to the standard platform.
– Analysed data source, format, and size in various clinical environments to make our algorithm work.

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