Skip to main content

Research Areas

Last updated: August 25, 2026

Before diving into AI research, you need to understand what the major research areas in AI are and how they relate to one another. This section surveys major areas in AI research, grounded in the work of active researchers. Towards the end, you should be able to narrow your interest to one or two areas worth exploring further in Research Topics.

Major Areas​

The 8 areas below are actively represented by AI Faculty in the Department of Computer Science at Emory University, so you will be able to connect each one to real, ongoing research (and potential advisors) right here on campus. It is common for a single faculty member to work across multiple areas, especially now that the boundaries between AI research areas are increasingly blurred.

tip

Broad venues such as AAAI 2026 and IJCAI 2026 publish across many areas and are useful for surveying the field, but they are not themselves a single research specialty.

Machine Learning​

The study of algorithms and models that learn patterns from data, including foundational theory, deep learning architectures, and training methods that underlie most modern AI systems.

warning

This area is both a research specialty in its own right (new learning methods, architectures, and theory) and a toolkit used across the other areas. Choose ML only if your focus is the learning method itself, not an application domain that merely uses ML.

Natural Language Processing​

The study of how machines understand, generate, and reason with human language, including large language models, dialogue systems, information extraction, and machine translation.

Computer Vision​

The study of how machines interpret and reason about visual data, including image recognition, generation, video understanding, and 3D reconstruction.

Data Mining​

The study of discovering patterns, structure, and knowledge from large-scale data, including graph mining, recommendation, and database systems for data-intensive applications.

Information Retrieval​

The study of finding and ranking relevant information from large collections, including search engines, recommender systems, and question answering over documents or the web.

AI for Science​

The application of AI methods to accelerate scientific discovery, including computational biology, molecular modeling, materials science, and other domains where models help generate, test, or interpret scientific hypotheses.

AI for Systems​

The use of AI to design, manage, and optimize computing systems themselves, including distributed systems, cloud and edge infrastructure, scheduling, and energy-efficient resource management.

Human-Computer Interaction​

The study of how people interact with computing systems, including interface design, usability, and how humans interact with and are affected by AI systems.

info

HCI is not always classified as a core AI area, but AI increasingly shapes how people interact with systems (e.g., conversational agents, explainable AI, human-AI collaboration), making it a relevant adjacent field for AI researchers.

Exercise​

  1. Navigate to [People] in Canvas, type "Research Areas" in the search bar, and form a team of three members. (3 min)
  2. As a team, select three faculty members from AI Faculty whose research sounds interesting, and assign one faculty member to each team member. (5 min)
  3. For your assigned faculty member, identify: which area(s) of AI their research falls under, 1-2 recent key projects or publications, and what problem their work currently focuses on. (10 min)
  4. For each team member, develop two potential research ideas that align with your assigned faculty member's research areas. Share your findings and ideas with your team. (10 min)
  5. As a team, create a concise slide presentation summarizing ideas from steps 3-4, and submit it to [Assignments → Exercises → Research Areas] in Canvas. (10 min)
  6. Prepare to deliver a short (5-minute) presentation of your slides in class.