601
536
二级C
Deep Learning Model Training/Fine-tuning, Data Processing Algorithms, Websocket/Http Service Framework, ONNX Inference Framework, ONNX Tool Chain, PyQt, Batch Scripts
Inference Framework, Model Quantization
AI Engineer
AI engineer with strong background in machine learning, deep learning, and inference/serving architecture. Extensive experience in AI algorithms, deep learning model building, programming languages, and big data technologies. Proficient in Python, TensorFlow, PyTorch, and C++. I have a strong self-motivation, a calm and practical personality, and a rigorous way of thinking. I am positive and optimistic, eager to engage with new things. When facing difficulties, I actively confront them and seek solutions.
——————–🚀2022——————–
🔥Algorithm:
- Posture recognition/gesture recognition algorithm research, writing, training, tuning
- Standard posture determination algorithm development
- RNN visualization teaching system – RNN visualization design, BPTT algorithm support & verification, question writing
- Laboratory test sheets OCR content recognition, structured extraction
——————–🚀2023——————–
🔥Algorithm:
- Delta robot arm solution simulation & verification
- Static gesture/posture recognition algorithm and action sequence recognition algorithm design and development based on 3D skeleton recognition, and backend service development and deployment
- Posture and gesture interaction system development
- Speech recognition/semantic matching algorithm development and backend service development and deployment
- Medical LLM deployment, data set collection
- SDK design and development – speech recognition/gesture recognition/posture recognition, etc.
- Femtosecond preoperative training system – iris recognition and scoring algorithm
- CT image segmentation, annotation and 3D reconstruction, automatic segmentation algorithm development
- Binocular recognition, fusion calibration algorithm development
- Infrared small target detection algorithm development
🔧Tools:
- Video merging tool (Based on FFMpeg)
- CPR posture process recognition desktop software development
——————–🚀2024-Today—————–
🔥Algorithm:
- Design and develop common CV algorithm libraries (multi-eye fusion algorithms, video, camera function algorithms)
- Design and develop an end2end onnx model inference algorithm libraries that integrate various deep learning algorithms (advanced API, with common SOTA deep learning algorithms, fast testing, integration, and deployment)
- Design and develop a general CS architecture service deployment framework including websocket/http (including flexible code forms and fast Docker deployment capabilities)
🫧Project Management:
- Write CI/CD configurations for Python package libraries, toolchains, and GUI projects
🔧Tools:
- Design and develop a posture editor for motion posture analysis, including a skeleton point detection tool, a point tracking tool, and a motion segmentation tool based on posture recognition algorithms
🗒️Patent & soft:
- CPR posture process recognition patent and software copyright
🚀Model and test related work:
- Convert various deep learning models trained based on mainstream frameworks (Tensorflow, PyTorch, Caffe, MxNet, etc.), automate conversion and data consistency testing. Form an inference model (ONNX), test data consistency before and after conversion, track problems for models that cannot be converted or have inconsistent data, and optimize the original model
- Test the inference performance and accuracy of the model on various hardware
🚀ONNX tool chains related work:
- Maintain ONNX related tool chains, including model creation, optimization, splitting, and weight visualization
- Develop various automated tools to simplify model conversion and testing processes
🚀Algorithm adaptation, model reproduction and demo related work:
- Maintain the self-developed model inference framework, add and upgrade various operators (the framework has CPU/GPU (CUDA)/MLU (Cambrian Deep Learning Computing Card) reasoning hardware acceleration capabilities)
- Add data pre-processing/post-processing algorithms to the self-developed model inference framework, and provide algorithm interfaces for subsequent development
- Test various SOTA deep learning models, select relatively new models for conversion and form preliminary reasoning demos (classification, detection, segmentation, migration, etc.)
- Investigate related technologies for autonomous driving, and conduct statistics on related models and feasible solutions, and form demos
🌟Scholarships:
- 2017-2018 Second-class Scholarship
- 2018-2019 First-class scholarship/Outstanding student leaders
🏆Participation:
- School “Challenge Cup” College Students’ Extracurricular Academic Science and Technology Works Competition Innovation Award
- School “Internet +” College Students’ Innovation and Entrepreneurship Competition Gold Award
- Jiangsu Province “Internet +” College Students’ Innovation and Entrepreneurship Competition First Prize
- Jiangsu Province Electronic Design Competition Second Prize
- National College Students FPGA Innovation Design Invitational Competition Second Prize, Enterprise Special Award
- The third prize in the China Science Core Cup University Student Internet of Things Application Design Invitational Competition