Human-centered computing (HCC) focuses on developing tools that improve the relationship between people and technology so that people can concentrate on the problem rather than the technology. The ultimate goal of HCC is to make the computer invisible.
Human-centered computing involves designing, developing, and deploying human-centric computer systems. In this concentration students will learn techniques for human-computer interaction using gestures, mobile devices, large surfaces, and virtual environments. Students will also learn how to design and conduct human-subject experiments and understand the role of HCC in developing human-centric artificial intelligence systems. The concentration provides rich interdisciplinary training in computer vision, machine learning, design and psychology.
Learning Objectives
Upon successfully completing this program, students will be able to:
- Design interactive systems using state-of-the-art HCC techniques.
- Design and conduct human-subject experiments.
- Build complex 3D worlds for user interaction (e.g., virtual and augmented reality).
- Confidently pursue graduate studies or professional employment in HCC and computer science.
Potential Occupations
In addition to the career opportunities open to all computer science graduates, the HCC concentration opens career paths that include:
User experience designer, virtual and augmented reality developer, and human-centric developer for intelligent systems.
Effective Fall 2023
A minimum grade of C (2.000) is required in CO 150 and in all CS, DSCI, MATH, STAT, and Technical Elective courses which are required for graduation.
| Freshman | |||
|---|---|---|---|
| AUCC | Credits | ||
| CO 150 | College Composition (GT-CO2) | 1A | 3 |
| MATH 156 or 1601 | Mathematics for Computational Science I (GT-MA1) Calculus for Physical Scientists I (GT-MA1) | 1B | 4 |
| Select one group from the following:2 | 5-9 | ||
Group A: | |||
| Culture and Coding: Java (GT-AH3) Culture and Coding: Python (GT-AH3) | 3B | ||
| CS1--Introduction to Java Programming CS1--Computational Thinking with Java | |||
Group B: | |||
| 3B | |||
| Python for STEM | |||
| CS1--Introduction to Java Programming CS1--Computational Thinking with Java | |||
Group C: | |||
| 3B | |||
| CS1---No Prior Programming Experience | |||
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | 3B | 3 |
| Select at least two courses totaling a minimum of 7 credits from the following (one course must be or include the sequenced laboratory): | 3A | 7 | |
| Introduction to Astronomy (GT-SC2) | 3A | ||
| Human Origins and Variation (GT-SC2) | 3A | ||
| Principles of Animal Biology (GT-SC2) | 3A | ||
| Principles of Plant Biology (GT-SC1) | 3A | ||
| Fundamentals of Chemistry (GT-SC2) | 3A | ||
| General Chemistry I (GT-SC2) | 3A | ||
| Exploring Earth - Physical Geology (GT-SC2) | 3A | ||
| The Blue Planet - Geology of Our Environment (GT-SC2) | 3A | ||
| Geology of Natural Resources (GT-SC2) | 3A | ||
| Physical Geology for Scientists and Engineers | 3A | ||
| Honors Seminar: Knowing in the Sciences | 3A | ||
| Attributes of Living Systems (GT-SC1) | 3A | ||
| Biology of Organisms-Animals and Plants (GT-SC1) | 3A | ||
| Introductory Genetics: Applied/Population/Conservation/Ecological (GT-SC2) | 3A | ||
| Introductory Genetics: Molecular/Immunological/Developmental (GT-SC2) | 3A | ||
| Fundamentals of Ecology (GT-SC2) | 3A | ||
| Oceanography (GT-SC2) | 3A | ||
| General Physics I (GT-SC1) | 3A | ||
| General Physics II (GT-SC1) | 3A | ||
| Physics for Scientists and Engineers I (GT-SC1) | 3A | ||
| Physics for Scientists and Engineers II (GT-SC1) | 3A | ||
| Diversity, Equity, and Inclusion | 1C | 3 | |
| Electives3 | 1-5 | ||
| Total Credits | 26-34 | ||
| Sophomore | |||
| CS 165 | CS2--Data Structures | 4 | |
| CS 220 | Discrete Structures and their Applications | 4 | |
| Select one group from the following: | 4-5 | ||
| Group A | |||
| Software Development | |||
| C++ Fundamentals | |||
| Group B | |||
| Software Development with C++ | |||
| Select one course from the following: | 4 | ||
| Computer Systems Foundations | |||
| Computer Organization | |||
| Select one course from the following: | 3-4 | ||
| Linear Algebra for Data Science | |||
| Linear Algebra I | |||
| Select one course from the following: | 1-3 | ||
| Introduction to Applied Statistical Methods | |||
| Statistics Supplement: General Applications | |||
| Introduction to Biostatistics | |||
| Intro to Theory and Practice of Statistics | |||
| Social and Behavioral Sciences | 3C | 3 | |
| Historical Perspectives | 3D | 3 | |
| Electives | 0-4 | ||
| Total Credits | 26-34 | ||
| Junior | |||
| CS 314 | Software Engineering | 4A,4B | 3 |
| CS 320 | Algorithms--Theory and Practice | 3 | |
| CS 345 | Machine Learning Foundations and Practice | 3 | |
| CS 370 | Operating Systems | 3 | |
| Select one course from the following: | 3 | ||
| Design Thinking Toolbox: Mixed Reality Design | |||
| Modern Web Applications | |||
Any CS course numbered 400- or above excluding CS 480-499 | |||
| Technical Electives (see list below) | 6 | ||
| Advanced Writing | 2 | 3 | |
| Electives | 6 | ||
| Total Credits | 30 | ||
| Senior | |||
| CS 464 | Principles of Human-Computer Interaction | 4C | 4 |
| Select two courses from the following: | 8 | ||
| Introduction to Computer Graphics | |||
| Introduction to Artificial Intelligence | |||
| Introduction to Machine Learning | |||
| Engaging in Virtual Worlds | |||
| CS course numbered 300- or above, excluding 380-399 and 480-499 | 3 | ||
| Technical Electives (see list below) | 3 | ||
| Electives4 | 12 | ||
| Total Credits | 30 | ||
| Program Total Credits: | 120 | ||
- 1
MATH 156 recommended for computer science majors who do not already have MATH 160 credit.
- 2
Recommended sequence for most incoming students is Group A: CS 150B to CS 164.
- 3
CS 192 or other seminar course is a recommended elective for incoming, first semester, students.
- 4
Select enough elective credits to bring the program total to a minimum of 120 credits, of which at least 42 must be Upper-Division (300- to 400-level).
Technical Electives
Select a minimum of 9 credits, of which 6 credits must be upper-division.
| Code | Title | Credits |
|---|---|---|
| Any IDEA course numbered 300- or above, excluding 380-399 and 480-499 | ||
| Any STAT course numbered 300- or above, excluding 301, 302A, 307, 315, 380-399 and 480-499 | ||
| IDEA 210 | Introduction to Design Thinking (GT-AH1) | 3 |
| PSY 252 | Mind, Brain, and Behavior | 3 |
| PSY 253 | Human Factors and Engineering Psychology | 3 |
| PSY 452 | Cognitive Psychology | 3 |
| PSY 454 | Biological Psychology | 3 |
| PSY 456 | Sensation and Perception | 3 |
| PSY 458 | Cognitive Neuroscience | 3 |
Distinctive Requirements for Degree Program:
To prepare for first semester: The curriculum for the Computer Science major assumes students enter college prepared to take calculus. Entering students who are not prepared to take calculus will need to fulfill pre-calculus requirements in the first semester. Those pre-calculus requirements are listed as benchmark courses in Freshman Semester 1 below. All students must maintain a C (2.000) or better in CO 150 and in all CS, DSCI, MATH, and STAT and Technical Elective courses which are required for graduation.4
| Freshman | |||||
|---|---|---|---|---|---|
| Semester 1 | Critical | Recommended | AUCC | Credits | |
| CO 150 | College Composition (GT-CO2) | X | 1A | 3 | |
| First course from Group A, B, or C (See options in Concentration Requirements Tab) | X | 3 | |||
| Department Approved Science (See list on Concentration Requirements Tab) | X | 3A | 3 | ||
| Diversity, Equity, and Inclusion | X | 1C | 3 | ||
| Electives | X | 1-5 | |||
| MATH 117, MATH 118, and MATH 124 must be completed by the end of Semester 1, if necessary. | X | ||||
| Total Credits | 13-17 | ||||
| Semester 2 | Critical | Recommended | AUCC | Credits | |
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | X | 3B | 3 | |
| MATH 156 or 160 | Mathematics for Computational Science I (GT-MA1) Calculus for Physical Scientists I (GT-MA1) | X | 1B | 4 | |
| Remaining course(s) from Group A, B, or C (See options in Concentration Requirements Tab) | X | 2-6 | |||
| Department Approved Science w/lab (See list on Concentration Requirements Tab) | X | 3A | 4 | ||
| MATH 125 and MATH 126 must be completed by the end of Semester 2, if necessary. | X | ||||
| Total Credits | 13-17 | ||||
| Sophomore | |||||
| Semester 3 | Critical | Recommended | AUCC | Credits | |
| CS 165 | CS2--Data Structures | X | 4 | ||
| CS 220 | Discrete Structures and their Applications | X | 4 | ||
| Select one course from the following: | X | 1-3 | |||
| Introduction to Applied Statistical Methods | |||||
| Statistics Supplement: General Applications | |||||
| Introduction to Biostatistics | |||||
| Intro to Theory and Practice of Statistics | |||||
| Social and Behavioral Sciences | X | 3C | 3 | ||
| Elective | X | 0-2 | |||
| MATH 156 or MATH 160 must be completed by the end of Semester 3. | X | ||||
| Total Credits | 12-16 | ||||
| Semester 4 | Critical | Recommended | AUCC | Credits | |
| Select one group from the following: | X | 4-5 | |||
| Group A | |||||
| Software Development | |||||
| C++ Fundamentals | |||||
| Group B | |||||
| Software Development with C++ | |||||
| Select one course from the following: | X | 4 | |||
| Computer Systems Foundations | |||||
| Computer Organization | |||||
| Select one course from the following: | X | 3-4 | |||
| Linear Algebra for Data Science | |||||
| Linear Algebra I | |||||
| Historical Perspectives | X | 3D | 3 | ||
| Elective | X | 0-2 | |||
| CS 220, CS 270, and DSCI 369 or MATH 369 must be completed by the end of Semester 4. | X | ||||
| Total Credits | 14-18 | ||||
| Junior | |||||
| Semester 5 | Critical | Recommended | AUCC | Credits | |
| CS 320 | Algorithms--Theory and Practice | X | 3 | ||
| CS 370 | Operating Systems | X | 3 | ||
| Select one course from the following: | X | 3 | |||
| Design Thinking Toolbox: Mixed Reality Design | |||||
| Modern Web Applications | |||||
Any CS course numbered 400- or above excluding CS 480-499 | |||||
| Technical Elective (See List on Concentration Requirements tab.) | X | 3 | |||
| Advanced Writing | X | 2 | 3 | ||
| CS 253 must be completed by the end of Semester 5. | X | ||||
| Total Credits | 15 | ||||
| Semester 6 | Critical | Recommended | AUCC | Credits | |
| CS 314 | Software Engineering | X | 4A,4B | 3 | |
| CS 345 | Machine Learning Foundations and Practice | X | 3 | ||
| Technical Elective Course (See List on Concentration Requirements tab.) | X | 3 | |||
| Electives | X | 6 | |||
| CS 320 and CS 370 must be completed by the end of Semester 6. | X | ||||
| Total Credits | 15 | ||||
| Senior | |||||
| Semester 7 | Critical | Recommended | AUCC | Credits | |
| Pick Two CS Depth Courses (See List on Concentration Requirements tab.) | X | 8 | |||
| Technical Electives (See List on Concentration Requirements tab.) | X | 3 | |||
| Elective | X | 3 | |||
| Total Credits | 14 | ||||
| Semester 8 | Critical | Recommended | AUCC | Credits | |
| CS 464 | Principles of Human-Computer Interaction | X | 4C | 4 | |
| CS*** Course numbered 300- or above | X | 3 | |||
| Electives | X | 9 | |||
| The benchmark courses for the 8th semester are the remaining courses in the entire program of study. | X | ||||
| Total Credits | 16 | ||||
| Program Total Credits: | 120 | ||||