Program Overview

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Informatics, PhD

The Doctor of Philosophy in Informatics is a research-oriented degree that focuses on the study of informatics, including both foundation and application areas. The program is designed to provide students with broad and deep knowledge of informatics, as well as the ability to conduct original research in their area of specialty.

Admission

The admissions process consists of a formal application, specifying experiences, courses, interests, and letters of recommendation. The Informatics PhD Program will admit graduate students who are approved by the Governing committee in conjunction with representatives of the Areas. With the approval of the appropriate committees, students may be admitted to the program with only a Bachelor's degree. They will work with their Advisory Committee to define appropriate courses to fulfill the 32 hours of Masters-level work.

Financial Aid

Fellowships, research assistantships, and teaching assistantships (all of which include tuition and partial fee waivers) are awarded on a competitive basis. All applicants, regardless of U.S. citizenship, whose native language is not English and who wish to be considered for teaching assistantships must demonstrate spoken English language proficiency by achieving a minimum score of 24 on the speaking subsection of the TOEFL iBT, or 8 on the speaking subsection of the IELTS.

Degree Requirements

The degree requirements for the PhD in Informatics include:

• Completion of a minimum of 64 hours of graduate credit for students entering with a Master's degree
• Completion of a minimum of 96 hours of graduate credit for students entering without a Master's degree
• Completion of the following courses:
  • INFO 500: Orientation Seminar (taken twice: once for 0 hours, once for 1 hour)
  • Research Practicum (8 hours)
  • Applications Courses (2 courses at the 500 level from approved list)
  • Foundations Courses (2 courses at the 500 level from approved list)
  • Electives (7 hours)
  • INFO 599: Thesis Research (32 hours)
• Passing of the qualifying exam, preliminary exam, and final exam/dissertation defense
• Deposit of the dissertation

Applications Courses

Students must select 2 courses at the 500 level from the following list:

• ANSC 542: Applied Bioinformatics
• ARTD 501: Industrial Design I: From Inquiry to Ideation
• ARTS 443: Time Arts II
• ARTS 444: Interaction II
• CHBE 571: Bioinformatics
• CPSC 565: Perl & UNIX for Bioinformatics
• CPSC 567: Bioinformatics & Systems Biol
• DANC 532: Digital Media for Dancers
• DANC 550: Advanced Research in Dance
• ECE 537: Speech Processing Fundamentals
• EPSY 587: Hierarchical Linear Models
• EPSY 589: Categorical Data Analysis in Educational Psychology
• IE 510: Applied Nonlinear Programming
• IE 511: Integer Programming
• INFO 448: Computer Music
• INFO 555: Advanced Educational Technologies for Engagement and Interactive Learning
• LING 501: Syntax I
• LING 502: Phonology I
• LING 507: Formal Semantics I
• LING 520: Acoustic Phonetics
• IS 506: Human-Centered Information Systems
• IS 524: Data Governance
• IS 525: Data Warehousing and Business Intelligence
• IS 526: Building Advanced Interactive Systems
• IS 556: Internet of Things
• IS 557: Applied Machine Learning: Team Projects
• IS 586: Usability Engineering
• MUS 407: Elect Music Techniques I
• MUS 409: Elec Music Techniques II
• MUS 506: Graduate Level Composition
• MUS 507: Sem in Music Comp and Theory
• NUTR 511: Regulation of Metabolism
• PATH 516: Epidemiology Infectious Dis
• PATH 517: Principle/Method Epidemiology
• PATH 560: Spatial Epidemiology
• PS 530: Quant Pol Analysis I
• PS 531: Quant Pol Analysis II
• THEA 419: Theatrical CAD Drafting
• THEA 430: Technical Direction I
• THEA 437: Software for Lighting Design
• THEA 453: Introduction to Theatre Sound
• THEA 454: Sound Design I
• THEA 455: Sound Design II
• THEA 550: Colloquium Design & Theat Tech
• UP 519: Advanced Applications of GIS

Foundations Courses

Students must select 2 courses at the 500 level from the following list:

• CPSC 540: Applied Statistical Methods II
• CPSC 541: Regression Analysis
• CS 414: Multimedia Systems
• CS 418: Interactive Computer Graphics
• CS 419: Production Computer Graphics
• CS 427: Software Engineering I
• CS 438: Communication Networks
• CS 440: Artificial Intelligence
• CS 446: Machine Learning
• CS 465: User Interface Design
• CS 511: Advanced Data Management
• CS 512: Data Mining Principles
• CS 519: Scientific Visualization
• CS 546: Advanced Topics in Natural Language Processing
• CS 558: Topics in Numerical Analysis
• CS 565: Human-Computer Interaction
• ECE 417: Multimedia Signal Processing
• ECE 418: Image & Video Processing
• ECE 420: Embedded DSP Laboratory
• ECE 437: Sensors and Instrumentation
• ECE 439: Wireless Networks
• ECE 453: Wireless Communication Systems
• ECE 470: Introduction to Robotics
• ECE 473: Fund of Engrg Acoustics
• ECE 511: Computer Architecture
• ECE 512: Computer Microarchitecture
• ECE 513: Vector Space Signal Processing
• ECE 517: Nonlinear & Adaptive Control
• ECE 537: Speech Processing Fundamentals
• ECE 544: Topics in Signal Processing
• ECE 547: Topics in Image Processing
• ECE 549: Computer Vision
• ECE 551: Digital Signal Processing II
• ECE 558: Digital Imaging
• ECE 580: Optimiz by Vector Space Methds
• ECE 594: Math Models of Language
• EPSY 580: Statistical Inference in Education
• EPSY 581: Applied Regression Analysis
• EPSY 582: Advanced Statistical Methods
• EPSY 587: Hierarchical Linear Models
• EPSY 588: Covar Struct and Factor Models
• IS 504: Sociotechnical Information Systems
• IS 507: Data, Statistical Models and Information
• IS 515: Information Modeling
• IS 517: Methods of Data Science
• IS 519: Research Design in Information Science
• IS 527: Network Analysis
• IS 537: Theory & Practice of Data Cleaning
• IS 547: Foundations of Data Curation
• IS 545: Advanced Data Visualization
• IS 575: Metadata in Theory & Practice
• IS 577: Data Mining
• IS 596: Advanced Topics in Human-Centered Design & Systems
• MATH 580: Combinatorial Mathematics
• PSYC 514: Seminar in Cognitive Science
• PSYC 588: Covar Struct and Factor Models
• PSYC 594: Multivar Anlys in Psych and Ed
• STAT 510: Mathematical Statistics
• STAT 525: Topics in Computational Statistics
• STAT 542: Statistical Learning
• STAT 571: Multivariate Analysis
• STAT 587: Hierarchical Linear Models

Learning Outcomes

The learning outcomes for the PhD in Informatics include:

• Acquiring broad and deep knowledge of informatics, including both foundation and application areas
• Demonstrating the ability to conduct informatics research in their area of specialty through developing an original piece of scholarship
• Demonstrating skills in oral and written communication sufficient to publish and present work in their field and to prepare grant proposals
• Interacting with people from diverse backgrounds as both leaders and team members with integrity and professionalism
• Being aware of ethical issues regarding research, including the use of human subjects, research misconduct, and publication practice

Programs in Informatics

The University of Illinois at Urbana-Champaign offers the following programs in Informatics:

• Undergraduate Minor: Informatics Minor
• Graduate Majors:
  • Bioinformatics, MS
    • Animal Sciences Concentration
    • Computer Science Concentration
    • Crop Science Concentration
    • Information Sciences Concentration
  • Informatics, PhD