Useful Background

This section outlines the skills and experience that are beneficial for contributing effectively to a research competition team. While formal research experience is not a prerequisite, a strong foundation in related areas is essential.

Foundational Experience and Eligibility

Official eligibility extends to all Georgia Tech students (undergraduate, graduate, online) and alumni. Beyond this, we look for individuals who have demonstrated experience with complex projects, either through project-heavy coursework or full-time software engineering roles. The ability to work with large codebases and navigate complex systems is crucial.

Equally important are transferable organizational skills. Experience in roles similar to program management, where you are responsible for scheduling meetings, tracking progress, and communicating requirements and timelines, is highly valuable. These skills are fundamental to the successful coordination of a research team.

Core Technical Skills

A broad set of technical skills underpins success in applied research competitions. While no single person is expected to be an expert in all areas, proficiency in several is expected.

Mathematical Foundations

A working knowledge of certain mathematical concepts is frequently required. An understanding of linear algebra is essential for working with the embedding spaces common in modern machine learning, including concepts like dimensionality reduction. Probability and statistics are critical for designing experiments and determining if the results are statistically significant. A basic understanding of calculus is also beneficial.

Machine Learning and Data Engineering

You should be familiar with fundamental machine learning concepts such as the distinction between classification and regression, and the purpose of data splits. Proficiency with the modern machine learning stack is key, including PyTorch and the Hugging Face ecosystem. It is helpful to understand core concepts behind large language models, such as the Transformer architecture, attention mechanisms, and fine-tuning strategies like parameter-efficient fine-tuning (PEFT). Strong data engineering skills are also highly transferable. This includes the ability to build data pipelines (e.g., converting data from XML to Parquet), parallelize jobs for distributed systems, and work with datasets that are larger than memory.

Information Retrieval and Systems

Many competitions involve information retrieval. Experience with search concepts like BM25 and cosine similarity, as well as search systems like Faiss, Anserini, or Elasticsearch, is a significant advantage. General software and systems engineering proficiency is non-negotiable. You must be comfortable with the Linux terminal, version control with Git (and platforms like GitHub/GitLab), and containerization with tools like Docker. The ability to quickly learn and integrate new tools into a workflow is essential.

Research Methodology

Finally, familiarity with the fundamentals of the research process is beneficial. This includes knowing how to conduct a literature review, how to structure a research proposal, and how to effectively read and analyze academic papers. Resources like the "Mining of Massive Datasets" and "Introduction to Information Retrieval" textbooks, along with tutorials like "the missing semester of your CS education," can help build this foundation.

It is important to note that this group is not intended for individuals undertaking their first major technical project. The expected workload is approximately 150 hours over a semester, equivalent to a 3-unit course. If you do not yet have experience with foundational data analysis tools like Pandas or NumPy, you are encouraged to take a project-heavy course and join the group in a future semester.