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I served as the fourth debater in the debate team of School of Mathematics and Statistics. As the closing speaker, I was responsible for delivering final rebuttals, synthesizing arguments, and constructing impactful conclusions. In addition to competing, I took on a mentoring role, helping train recruits by organizing internal workshops and preparing research materials for incoming debaters.

I joined Young Sprout Association of Henan University and served as vice captain of Stellar Corps (chapter for high school students). Our team brought together members from diverse discipline backgrounds, including medicine, chemistry, physics, education, business and statistics. We collaborated with Lingqing Philanthropy Development Center in Shanghai and co-launched a long-term volunteer project with Yuqing Middle School in Baini Town, Yuqing County, Guizhou Province. We spent most vacations (winter, summer and national vacations) on volunteer teaching activities, student mentoring, and course design. Our team created a series of engaging extracurricular programs that integrated academic concepts with real-world challenges, such as:

• “Puzzle Tracker” – an interactive program introducing computer vision and image processing through puzzle-based games.
• “Hidden Gems in the Mountains” – a cultural exploration program combining local Miaoethnic herbal medicine rooted in Chinese cultural heritage.

Hope these efforts can inspire students to decode STEM principles through gamified challenges; discover cultural wisdom within real-world contexts; develop critical thinking via cross-disciplinary exploration. Moverover, this is not merely a top-down transmission of knowledge, but a two-way learning bridge that fosters mutual growth between them and teaching volunteers across generations. The project has been sustained for over two years, and I truly hope it will continue to grow and benefit more people in the years to come.

This project aims to collect and quantify stomatal phenotypes from live field-grown peanut plants using an intelligent recognition system. By integrating phenotypic, genotypic and environmental data, we can perform Genome-Wide Association Study (GWAS) and Genotype-by-Environment Interaction Analysis (G×E) to identify key and high-stable regulatory genes. The ultimate goal is to discover superior genetic resources for peanut breeding under variable environments and enhance its productivity and adaptability. In this project, I worked with Quanling Zhao and Prof. Xiaohui Yang, as well as Dr. Chenyang Du and Prof. Chen Miao from State Key Laboratory of Crop Stress Adaptation and Improvement, to finish the following works:

• GxEStat, a user-friendly R GUI package designed for statistical analysis of genotype-by-environment interactions. It integrates: (1) significance analysis based on the mixed effect model to determine whether genes or GxE interactions significantly affect traits; (2) single-gene and multi-gene stability analysis based on singular value effect decomposition, which further studies the interactive relationships between genes and environments, as well as the relative superiority or inferiority of genotypes across environments.
• StomaD², an end-to-end stomatal phenotyping system based on diffusion-based restoration detection network. StomaD² supports real-time imaging and quantification of stomatal phenotypes (e.g., density, conductance) in field conditions. It is also compatible with both monocot and dicot crops, and accepts both destructive and non-destructive microscopy images. Experimental results demonstrate that StomaD² achieves expert-level accuracy and high generalization across species, highlighting its potential for large-scale phenotyping, plant physiology research and precision agriculture.
• We propose a triphasic Hellinger Distance-based Intersection over Union (HDIoU) for oriented bounding box (OBB) regression, and integrate it into the training of YOLOv8-OBB on non-destructive leaf epidermal stomatal images. HDIoU utilizes a dynamic indicator to determine the current optimization focus during training, allowing the loss function to continuously transition across three objective phases within a single unified learning process. This triphasic design facilitates more accurate and efficient localization of target contours, yielding precise phenotypic information for stomatal studies. Furthermore, HDIoU is inherently scale-invariant, making it particularly effective for detecting small objects such as stomata in high-resolution biological imagery.

The primary objective of this project is to design, fine-tune and deploy a secure, internally-facing Audit Large Language Model (ALLM) to empower auditors at Henan Provincial Audit Department. Built atop Qwen2.5-VL, ALLM need deliver intelligent support for audit Q&A, regulatory compliance and table-data analysis, improving audit efficiency and accuracy. In this project, I worked with Lutong Zhang and Haonan Zhang to finish:

• Data Collection: Aggregating amounts of audit documentation, historical audit reports, government financial regulations (national and provincial level), accounting standards (e.g., Chinese GAAP), policy directives, compliance manuals, and best practice guidelines.
• Data Processing: Implementing data processing pipelines for heterogeneous audit sources, including: text extraction that applies PaddleOCR with regex-based digit correction for scanned documents, pdfplumber for layout-aware PDF, python-docx for DOCX files; spaCy + FinBERT for entity normalization of regulations/financial terms; DeepSeek-V2 classification for document categorization and UIE model for sensitivity tagging; LayoutLMv3 for context-aware semantic segmentation to preserve audit logic.
• QA Generation and Expert Annotation: Using Qwen-72B-Chat to produce question-answer pairs reflecting common audit scenarios, enriching the fine-tuning dataset; collaborating with senior auditors to annotate real audit cases, identifying key entities, risks, compliance issues, and generating relevant queries/responses.

In this work, we identify three critical issues (information loss, context-agnostic sampling and pattern inconsistency) in existing multi-scale feature frameworks and fusion operations; and propose the theoretically supported A\(^3\)-FPN to optimize multi-scale visual representation learning via the asymptotically disentangled design and content-aware attention modules.