2021 Summer Research At CreateLab

CreateLab is looking for talented, motivated masters and undergraduate students for its collaborative research in the 2021 Summer. The expected duration is from May 16, 2021 through August 15, 2021 (i.e., the summer semester at UIUC); however, we can also incorporate your schedule since the period of summer semesters could vary a lot depending on …

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To have you on the Team section

Welcome to the group! To have you on the team section, we will need the following information. Your name (no need to be your legal name; just use any name you commonly use) Your photo (jpg, png, etc.) A link to your website, LinkedIn page, GitHub page, or anything else Please send the above information …

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Welcome to Lab Handbook

The lab-specific information is only visible to the members. If this is the only post you see, you will need an additional privilege to view other posts in this category.

Admission Process at CS@Illinois

TL;DR The final decisions are made by the committee. To reach the final stage, each applicant must be supported by a certain number of faculty members. To give supports, faculty members assess application materials (e.g., letters, transcript, papers, etc.). This post shares limited information about IllinoisCS’ admission process. No sensitive information included. Only to help …

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Hello world!

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Hello News!

This is the first post for the news category. We will have more exciting news shortly in the future!

VerdictDB: Universal Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption, mostly due to the reluctance of traditional vendors to make radical changes and also due to the tight integration with specific platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and …

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Self-learning Data Systems

Database systems are becoming increasingly complex. Manually configuring a system for each different system environment is too time-consuming and, more than often, not even optimal. To build self-optimizing systems, I am investigating machine learning-based techniques that can continuously optimize the internals of data systems.

Visualization-Aware Sampling

You have billions of data points, but are you frustrated simply because they are just too big to visualize? This is a common problem we encounter when we work with large databases: my dataset is simply too big to interactively explore and gain useful insights from them. Our proposed approach, Visualization-Aware Sampling (VAS), makes the interactive visualization of big data possible …

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hello projects

This is the first post in the project category. Apparently, we don’t have anything to tell you yet!