1. Academic Background
I attended Shenzhen Hongling High School for my high school education. In my freshman year, I enrolled in the Experimental Class for Engineering at Xi'an Jiaotong University, specializing in electrical engineering. In my sophomore year, I transferred to the Artificial Intelligence Experimental Class at Xi'an Jiaotong University. In my junior year, I went to the University of Manitoba in Canada for an exchange program.
I have excelled in my studies. In my freshman year, I achieved a comprehensive GPA of 91.05, ranking 4th out of 1138 students and receiving the National Scholarship as the top student in my college. In my sophomore year, I achieved a comprehensive GPA of 92.43, ranking 3rd out of 70 students in my college and receiving the Megvii Scholarship. I have excellent English proficiency with a CET-4 score of 603 and a TOEFL score of 89. I have participated in various competitions and won the Silver Award in the International Genetically Engineered Machine Competition (iGEM) as the leader of the hardware and wet lab teams. As a presenter, I participated in the national-level innovation project "AR Scene Navigation in Short-Distance Scenarios," which stood out among 47 projects in the Telecommunications Department and received an excellent evaluation after the final defense. I also participated as a presenter in the national-level innovation project "Diagnosis and Clinical Decision-Making of Adrenal Tumors Based on Deep Learning," where I was responsible for building models and other critical tasks. This project also received an excellent evaluation after the defense. In the first two years of university, I have established a solid foundation in my major. I have taken a total of 7 math courses with a cumulative GPA of 94.6 and achieved high scores in artificial intelligence-related courses with a cumulative GPA of 94.6. Some of my course grades include: Computational Neural Engineering (99), Cognitive Psychology (99), Social Psychology (98), Probability, Statistics, and Random Processes (98), University Physics II (98), University Physics I (97), Data Structures and Algorithms (95), Linear Algebra Combined with Analytical Solution (94), Calculus I (93), Computer System Architecture (92), and Calculus II (92). Through a series of selections, I obtained funding from the China Scholarship Council (CSC) and became one of the three students from Xi'an Jiaotong University to study abroad at the University of Manitoba in Canada during my junior year. My diligent and serious attitude towards learning has been recognized by my teachers. Professor Pengju Ren from Xi'an Jiaotong University once commented, "Youcheng Li was one of the best students of mine in the last several years. Based on his solid knowledge, he quickly adapted to multiple tasks assigned to him and had innovative minds on the course."
2. Research Experience
During my middle school years, I set up the life ideal of medical-industrial crossover and serving the country with technology. Since my freshman year, I have participated in a total of eleven domestic and international university projects, covering a variety of fields such as computer vision, natural language processing, reinforcement learning, impulse neural networks and bioinformatics. I have explored my interest in medical image processing in active practice. Three representative research experiences are listed below.
First of all, I joined Prof. Jianru Xue's group at Xi'an Jiaotong University through the undergraduate "X Plan" research training program of the School of Artificial Intelligence from December 2020 to now, and I have been conducting research training under the guidance of Prof. Xue. During this period, I have read more than 50 papers in the field of computer vision and gained a preliminary understanding of the field, and I have also learned to use common machine learning frameworks, such as PyTorch, so that my programming skills have been improved. Meanwhile, under the guidance of Prof. Xue Jianru, I participated in the national college students' innovation project "AR live navigation in short-distance scene" as the host, responsible for image denoising and managing the team, and independently proposed a moving target removal algorithm based on mask-generation model. After the completion of the project, I carried out research on impulse neural networks and reinforcement learning under the guidance of Prof. Xue Jianru.
Secondly, from March 2021 to May 2022 I worked on pancreatic diagnosis based on enhanced CT images under the guidance of Prof. Cui Wei from the First Affiliated Hospital of Xi'an Jiaotong University and Prof. Li Chen from the School of Computer Science and Technology. As the only undergraduate student in the School of Artificial Intelligence within the team, I was mainly responsible for the implementation of the pancreatic cancer detection algorithm and lesion segmentation algorithm. During the year-long project study, I learned the characteristics and preferences of AI models in the medical field, and studied more than twenty relevant literatures, which laid a solid theoretical foundation for the subsequent research. Meanwhile, I mastered the ability to communicate with members from different disciplinary backgrounds during the project, which improved my teamwork ability. After the completion of this project, we hope to further dig deeper into the value of the dataset and algorithm performance, with a view to publishing the results in high-quality journals.
Finally, I have been working on segmentation algorithms for spatial transcriptomics data under the guidance of Prof. Pingzhao Hu at the University of Manitoba, Canada from December 2021 to present. The weekly group meetings have greatly improved my oral presentation skills and enhanced my English language proficiency. Based on the theoretical accumulation of computer vision and bioinformatics, I have studied more than ten spatial transcriptomics related literatures and proposed a weakly supervised spatial transcriptome point cloud segmentation algorithm.
3. Future Planning
My main research interest is medical image processing. I would like to understand the impact of AI technologies on medical diagnosis and focus on exploring machine learning frameworks applicable to medical histology data represented by weak supervision, small samples, and small targets. My research strength is a solid and comprehensive foundation in math, cognitive science, neuroscience, and machine learning. I am able to combine medical knowledge and machine learning theories to build models with the help of deep learning, reinforcement learning and other tools to study medical image processing algorithms and the intrinsic laws of medical histology data, just as my scientific practice in deep learning-based cancer diagnosis.