Sept. 30, 2024
After developing and testing the Physical Chemistry Experiment Tool (PCET) internally, we released the tool to students from the School of Life Sciences who were actively engaged in physical chemistry experiments. The tool provided a user-friendly way for students to process their experimental data, and it received positive feedback for its simplicity and effectiveness. In addition to the PCET, we offered several sample datasets for the scRNA-seq workflow. These datasets were designed to give students, particularly those who hadn't previously encountered sequencing, the opportunity to explore and experience the data analysis workflow in a hands-on manner.
Sept. 21, 2024
The next stage of our project involved the original development of a Physical Chemistry Experiment Tool (PCET) tailored for students from the School of Life Sciences. Alongside this, we focused on optimizing the frontend design of the community section, aiming for a more user-friendly and visually appealing interface. To enhance the resource offerings on the platform, we gathered course materials related to CS and BIO from within the university. Additionally, we shared video recordings of lectures provided by our advisor during the learning phase, along with research materials compiled by the biology team over the course of the project cycle. This step allowed us to offer a broader range of resources and foster collaboration among users from different fields.
Sept. 1, 2024
Our next step was conducting the first round of testing and iteration within the team. All members participated in testing the website, identifying bugs encountered during usage, and assessing the potential challenges that arise when multiple users access the site simultaneously. We gathered feedback from everyone and initiated our first round of optimizations. This included addressing differences in performance across various browsers, fixing bugs that occurred during the execution of different features, and handling more diverse user behaviors to improve overall functionality.
Aug. 15, 2024
After gaining a deeper understanding of model architectures, the backend team focused on replicating code and conducting training and validation on the UNeXt model. Building on this foundation, we also explored and grasped the workings of SAM (Segment Anything Model) for image segmentation. We encapsulated the model into a more modular form and collaborated closely with the frontend team to transform it into a user-friendly tool, making it accessible and practical for end-users.
June 30, 2024
In the following phase, the frontend team began working on implementing key features for the platform, including sections like forum, course resources sharing, and teaching, to enrich the community experience. Additionally, a feedback system was introduced to collect user insights, which would help us optimize the platform further. During this period, communication between the frontend and backend teams intensified. With a clearer understanding of the models' functionality, we started designing frontend interfaces for scRNA-seq workflows and image segmentation tools, integrating these advanced features into the platform. This collaborative effort significantly contributed to the overall progress and functionality of the system.
May 24, 2024
Following our initial study of Django, we shifted our focus to hands-on server usage. We explored the Linux environment and learned how to use essential tools like Nginx for serving web traffic and Gunicorn for running our Django applications in production. The primary objective during this phase was to deploy the login and registration functionality we had developed locally onto a live server. This process taught us how to manage web traffic, handle server requests, and maintain a stable deployment environment. The excitement peaked when we were able to search for our website online and access it through a browser—an important milestone in our project development journey!
May 20, 2024
In the first three months of the spring semester, our backend team has been deeply engaged in studying deep learning techniques and the mathematical concepts that underpin them. We concentrated on understanding the fundamentals of PyTorch and explored model architectures like Transformer and UNet, which are essential for our project. During this period, we primarily focused on learning by reproducing key experiments from academic papers, which allowed us to gain a deeper understanding of the theory and application of these models. This groundwork has strengthened our ability to implement advanced models and is a critical step toward building innovative solutions in our project. As we move forward, we plan to start integrating these models into our system for practical applications.
May 1, 2024
In the first month of the spring semester, we focused on learning the Django framework and MySQL database operations. We learned how to use these tools to build a local website. Each student was tasked with developing a simple project using Django—some created a lottery feature, others developed a forum, and some implemented login and registration functionalities, among other projects.
Feb. 22, 2024
During the winter break, we focused on learning the fundamentals of frontend development, including HTML, CSS, and JavaScript. Each student completed a personal homepage as the final project for this phase. Throughout the process, we gained valuable skills and knowledge in web development.