Jerry Zeng
Master of Science in Information Networking @CMU
2022.05 〜 2023.06
🔄 . 📈
16th National University Student Innovation Practice Project
Achievements
Our research project was honored with the "Outstanding Project" Conclusion Rating , a singular distinction in the department, during the 16th National University Student Innovation Practice Project. For a deeper dive into our work, more details can be found in our Project Presentation Report PowerPoint.
Sole
As a contributing author, our paper: Ruiqi Liu, Yujiang Wu, Jie Zeng, Jia Zhang, Jinyong Gao, and Min Gao* Multi-Time Scale Aware Host Task Preferred Learning for WEEE Return Prediction was accepted by EAI COLLABORATECOM 2023.
CCF C
As a contributing author, our paper: Yujiang Wu, Ruiqi Liu, Jie Zeng, Quanwu Zhao, Jinyong Gao, Jia Zhang, and Min Gao* Dual Transfer Driven Multi-Source Domain Adaption for WEEE Reverse Logistics Return Prediction is currently under review after minor-revision resubmission in the renowned JCR Q1 journal ESWA.
IF = 8.5
In line with our innovative research, we've applied for two national invention patents from China National Intellectual Property Administration, which are presently under review.
×2
Secured two software copyrights for our state-of-the-art visualization dashboard and system. For those interested in a deeper dive, I've made the visualization front-end available on my GitHub repository.
×2Overview
Our core mission was to address the intricacies of reverse logistics recycling, especially in environments with sparse and noisy data. The goal was to craft robust algorithms that could seamlessly integrate diverse data types, ensuring both enhanced accuracy and efficient data processing, while also providing an interactive user experience.
🌟 Dual-Transfer Model
Developed a dual-transfer model that merged various data types, enhancing the accuracy of predictions. This model was pivotal in ensuring that diverse data sources were harmoniously integrated.
📊 Multi-Task Learning
Crafted an approach that combined multi-task learning with domain adaptation. This ensured efficient data processing, minimized errors, and made the model adaptable to different data domains.
🖥️ Interactive Dashboard
Utilized the Vue framework to develop an intuitive front-end interface, presenting an interactive dashboard that highlights real-time predictions on the recycling process. This user-friendly platform elevates user engagement by offering a dynamic overview, allowing users to witness the predictions unfold in real time. Feel free to explore the associated code on GitHub repository.