Our research and areas of expertise are broad and constantly expanding, often in partnership with other IU faculty and departments.
From the Precision Health Initiative to a quantum information science project to a large study on machine learning and human learning, we are leading the technological revolution to places not yet imagined.
Theoretical computer science focuses on the very foundations of computing. It is concerned with abstract models of computation (such as the Turing machine, von Neumann architecture, or quantum computers), how real-world computers relate to these models, and the types of problems these models can solve. It also deals with designing efficient algorithms that can solve foundational problems and analyzing the algorithms’ performance mathematically.
See faculty in algorithms and theoretical computer scienceArtificial intelligence research encompasses foundational areas such as knowledge representation, reasoning, planning, and decision making, as well as applied areas such as vision, speech, and music processing. Machine learning is a highly influential subfield of artificial intelligence that is concerned with the development of systems that learn from experience and the use of large training data to improve their performance on specific tasks.
See faculty in artificial intelligence and machine learningBioinformatics and computational biology are concerned with processing and managing large-scale biomolecular (especially sequential) data to better understand living systems and predict their behavior. These areas also focus on the discovery and analysis of the root causes of diseases—for example, in the form of genomic alterations—and investigate how they affect cellular systems.
See faculty in bioinformatics and computational biologyComputer vision, speech, and music processing are among the most significant application areas of machine learning and AI. Research focuses on visual and auditory pattern recognition, 3D image reconstruction, visual and speech classification, vision and speech processing for robotics, musical signal processing, and computer music generation.
See faculty in computer vision, speech, and music processingResearch in databases and data mining is concerned with the improvement of data organization with the primary goal of providing efficient and meaningful access to information, explicitly presented or implicitly included in large data sets with diverse types and structures.
See faculty in databases and data miningProgramming languages provide means of expressing computational tasks in a succinct, flexible, secure, reliable, efficient, and reusable manner. Research involves the design and implementation of new languages and language constructs, and analysis and improvement of existing languages through formal methods and proofs.
See faculty in programming languagesSecurity and privacy research at IU focuses on areas such as systems, software, and network security as well as privacy-preserving processing of biomedical data, to provide means of protection from various forms of outside attacks.
See faculty in security and privacyThis area focuses on designing and optimizing hardware and software to more efficiently perform large-scale computations, especially through the use of parallelism. This research also focuses on theoretical and applied work on computer architecture.
See faculty in systems and high performance computingResearch in teaching and learning involves the examination of pedagogy practices, student learning outcomes, and strategies for collecting and analyzing data related to teaching computer science, with the goal of improving the student learning experience.
See faculty in teaching and learningIn addition to our doctoral students’ theses, many master’s students and undergrads contribute to research projects. The Luddy School of Informatics, Computing, and Engineering has research programs for undergrads, and you can contact faculty members directly to ask about working on their research or doing an independent study project.
Focuses on algorithm development for machine learning, including theoretical foundations and large-scale applications.
Explores new applications and computational techniques to make supercomputing and all advanced cyberinfrastructure tools more effective.
