chenceshi.com

What It Is

chenceshi.com is the academic personal homepage of Chence Shi. According to the site, he is a PhD student at Mila (Montreal Institute for Learning Algorithms), with research focused on generative models, geometric deep learning, graph representation learning, and AI for Science, especially modeling structured multimodal data such as graphs, proteins, and molecules. It is closer to a “scholar profile / research portfolio” than a commercial SaaS product, course platform, or developer tool.

Core Features

The site mainly provides a personal bio, contact information, research interests, selected papers, and links to open-source projects. The publication list covers venues such as ICLR, ICML, NeurIPS, and CIKM, with topics including protein sequence-structure co-design, protein-ligand docking, molecular conformation generation, retrosynthesis prediction, and molecular graph generation. Many entries include links to PDFs, code, slides, video recordings, or project pages, making it easier for researchers to access the original papers and reproduce experiments. The page also lists TorchDrug, a machine learning platform for drug discovery, as well as repositories related to recommender systems.

Pricing

The website itself is free and publicly accessible. There is no account system, subscription plan, or commercial pricing information. Its value mainly comes from the publicly available academic materials and external resource links.

Pros and Cons

The main strengths are its strong focus, high-quality publications, and clear overview of the author’s academic trajectory in AI for drug discovery and geometric generative models. The code and paper links are fairly complete, which is useful for reproducibility and literature review. The downsides are that the page is relatively static and lacks a blog, tutorials, dataset explanations, or a structured learning path. General users may also find the technical details difficult to understand. In addition, some external links rely on platforms such as Github, Google Scholar, and Google Colab.

Who It’s For

It is suitable for researchers, students, and engineers working in machine learning, computational biology, drug discovery, and graph neural networks, as well as collaborators or recruiters who want to assess the author’s academic background. It is not suitable for users looking for online courses, commercial software, or general-purpose AI tools.

Access from China

Whether the main site can be accessed reliably depends on its hosting environment. Github links on the page are usually accessible but may be slow or unstable, while Google Scholar and Google Colab are restricted in mainland China. Overall, access should be considered “partially restricted.”