Deep Learning and Computer Vision Specialist
7 days ago
Transforming the field of deep learning and computer vision requires a multidisciplinary approach that combines robust data-driven methodologies with strong engineering capabilities. As an expert in this area, I have developed impactful solutions to complex challenges in classification, segmentation, and reconstruction.
Key Expertise- Boosting for enhanced model performance
- Computer Vision for accurate image analysis
- CUDA for optimized GPU acceleration
- Deep Learning (CNN, GAN) for sophisticated pattern recognition
- Docker for streamlined deployment and testing
- GIT for version control and collaboration
- Machine Learning (SVM) for predictive modeling
- NumPy for efficient numerical computations
- Pandas for data manipulation and analysis
- Python (TensorFlow, PyTorch, Scikit-Learn) for flexible development and prototyping
- SQL (MySQL, SQL Server, SQLite) for reliable database management
- Transformer for state-of-the-art language models
- XGBoost for scalable and interpretable boosting
Professional Experience Highlights
PhD Researcher at Achieving Clinical Excellence- Developed an unsupervised deep framework under the CT imaging principle using inherent physics domain consistency to address unavailable ground truth problems in clinics.
- Proposed evaluating algorithm indirectly by subsequent segmentation to prove clinical effectiveness.
- Collaborated with researchers exploring domain consistency from experiments and published a technical paper.
- Designed a framework eliminating drawbacks of imbalanced data to increase true positives and false negatives simultaneously.
- Analysed tubular airway tree from a topological view and designed/implemented differentiated distance map and surface loss functions to improve clinically important metrics.
- Presented research outcomes at an international conference.
- Conducted machine/deep learning, computer vision, and data analysis tutorials in accessible terms, adapting to diverse student skill levels.
- Communicated with each student in the classroom to encourage participation and create a welcoming environment.
- 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, achieving top-rank performance and publishing two technical papers.
- 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.
- Developed the chip simulation (C programming) and conducted bit-true with IC engineers to verify the correctness of chip design.
- 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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