Academic Excellence Award

I am now pursuing my MS in Computer Science degree at Columbia University after achieving my BS in Computer Science degree at Peking University.I am looking for internship opportunities in Software Development Engineer, Machine Learning Engineer and can be reached at jx2467@columbia.edu.

I have previous internship experience working as computer vision algorithm engineer at National University of Singapore and DeepWise, where I have gained lots of technical experience in Algorithms, Computer Vision and communication skills.

Dependable and hardworking, I can pick up new skills and methods of working with ease while applying previously learnt skills efficiently.My technical skills include Python, C/C++, PyTorch, TensorFlow, GitHub, LaTeX, and so on.

Columbia University

Peking University

As Machine Learning Engineer Intern at National University of Singapore, I collaborated with NExT++ team, extending technical solutions for adversarial face detection through model design and integration.I also proposed new adversarial attack strategy, defining more potential risks for defensive models.

Key Achievements:
- Proposed a more covert and effective adversarial method using Python, achieving 98.7% success rate when attacking best defensive models.
- Implemented a generic defensive model by combining a traditional DeepFake detector with a multi-pathway perturbation detector using PyTorch, leading to 99.3% accuracy in defending against adversarial attacks.
- Demonstrated the effectiveness of our model in defending against variant kinds of white-box and black-box attacks compared with previous state-of-the-arts on two public DeepFake datasets.
- Applied for a patent on the proposal of our defensive model.

Machine Learning Engineer Intern

As Computer Vision Engineer Intern at DeepWise, I collaborated with Lung Cancer Detection team, extending technical solutions for lesion detection through model construction and combination.I also derived novel technology strategy, leveraging 2D images for 3D models pre-training, which can be adopted for other medical image tasks.

Key Achievements:
- Proposed a generic model to employ 3D network for 2D lesion detection in CT slices using PyTorch, which proved to be computational and memory efficient and achieved best performance (0.7764 MAP) on the DeepLesion dataset.
- Derived a novel and effective method to adopt 2D natural images to pre-train 3D networks and proved the efficiency of the method being generalized to variant kinds of medical image tasks.
- Published a paper on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020.

Computer Vision Engineer Intern

Jincheng Xu

English

Full professional proficiency

Chinese

Native or bilingual proficiency

Graduate Student

HTML5

GitHub

Python

Data Analysis

Time Management

Machine Learning

Computer Vision

Tensorflow

Data Mining

LaTeX

Pytorch

Data structures and algorithms

Record Keeping Skills

Systems Architecture

Analytical skills (data driven)

Process Improvement

Verbal communication

Willingness to learn

creativity

Organization

written communication

Integrity

Critical thinking

Teamwork

Adaptability

Effective communication

Dependability

Problem-Solving

Empathy

Open-mindedness

Detail Oriented and Organized

Jincheng Xu

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