About Me

At a Glance

I am a postgraduate student with some initial experience in medical image analysis, interested in embodied AI. My academic journey began with computer vision and object detection during my undergraduate studies, and I am fortunate to continue my studies in the field of artificial intelligence. I am now eager to explore cutting-edge technologies like embodied intelligence, multi-agent systems, and brain-computer interfaces.

Education: M.S. in AI & Adaptive Systems (In Progress), B.S. in Computer Science

Learning Focus: Medical AI, Computer Vision, Embodied Intelligence

Experience: Kaggle competitions (RNA 3D Folding, Podcast Listening Time, Calorie Expenditure), Challenge Cup competition, National-level innovation and entrepreneurship program, 2 software copyrights, 1 patent application

Research Learning and Participation

Machine Learning Identifies Exosome Features Related to Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology (2022)

Co-first author (third position): Developed machine learning algorithms for exosome feature analysis and classification in hepatocellular carcinoma research

Multi-omics and Machine Learning-driven CD8+ T Cell Heterogeneity Score for Prognosis

Molecular Therapy Nucleic Acids (2024)

Participated in implementing machine learning algorithms for key gene identification in HNSCC research

Using Multiomics and Machine Learning: Insights into Improving the Outcomes of Clear Cell Renal Cell Carcinoma via the SRD5A3-AS1/hsa-let-7e-5p/RRM2 Axis

ACS Omega (June 2025)

Implemented machine learning algorithms for identifying SRD5A3-AS1/hsa-let-7e-5p/RRM2 important features

Partial Project Experience

YOLOv11-LCDFS: Enhanced Smoking Detection With Low-light Enhancement

Worked on a YOLO-based architecture, studying specialized loss functions, attention mechanisms, upsampling techniques, and low-light enhancement for detection in challenging lighting conditions

PyTorch | Computer Vision | YOLO | Attention Mechanisms

Multi-modal Medical Image Analysis

Learning about clinical tabular data integration and CT multi-modal processing to support diagnostic accuracy

PyTorch | Deep Learning | Multi-modal Fusion

3D Medical Segmentation

Learning about medical image segmentation and 3D volumetric segmentation techniques

PyTorch | Deep Learning | 3D Segmentation

Partial Competitions and Awards

Stanford RNA 3D Folding Competition

Bronze Medal 143rd/1516 | Kaggle Global Competition (Deadline: May 23, 2025)

Predict Podcast Listening Time Competition

Ranked 116/3310 (Top 4%) | Kaggle Global Competition (June 1, 2025)

Predict Calorie Expenditure Competition

Ranked 178/4316 (Top 5%) | Kaggle Global Competition (May 1, 2025)

18th Challenge Cup College Student Competition

Bronze Medal Recipient | Zhejiang Province

4th National "Chuanzhi Cup" IT Skills Competition

Provincial Excellent Award | Zhejiang Province

Research Interests

Medical AI: Learning about deep learning applications in medical image analysis, disease prediction, and multi-modal clinical data integration

Embodied Intelligence: Learning about physical AI systems, studying robot learning, and trying to understand sensorimotor control

Computer Vision: Studying attention-based object detection and image segmentation

Multi-modal Learning: Starting to learn about Vision-Language-Action(VLA) models in embodied AI systems

Multi-Agent Systems: Taking first steps in understanding collaborative AI systems

Current Focus

As a student, I'm currently focused on building my knowledge in these areas:

Building Foundations

Studying core concepts in robotics, reinforcement learning, and computational perception

Practical Skills

Learning to work with simulation environments and developing small projects to apply theoretical knowledge

Multi-Agent Learning

Beginning to explore how multiple AI agents can interact, communicate, and solve problems collaboratively

Jiatao Yan