Intelligent and Interactive Systems

Are you interested in studying interactions

between people and digital systems and in developing systems that perceive, understand, and interact with people and the environment? The intelligent and interactive systems (IIS) track may be for you. You’ll focus on areas such as:

  • Autonomous robots
  • Human-robot interaction
  • Wearable and ubiquitous computing
  • Music and Computation
  • Motion planning and control

Investigating and building technologies involves understanding relationships between people and computing systems, as well as developing systems that are better able to understand and interact. As an IIS student, you’ll explore theories, develop prototype technologies, and evaluate human responses to and interactions with those technologies.

Learn about related research

The R-House Human-Robot Interaction Lab hosts researchers, students, and visitors interested in studying human-robot interaction (HRI) through the design and evaluation of robotic technologies for everyday use. Lab members explore the connections between HRI, human social behavior and cognition, and the emerging social meanings and consequences through a variety of methods.

Learn about the R-House

Track Guide

Primary Track Faculty

: Track Director

Music informatics, artificial intelligence, accompaniment systems, computer generated musical analysis, musical signal processing, modeling of musical interpretation, computer generated musical analysis, data science, machine learning

Cognitive science, computational and theoretical biology. Understanding how coordinated behavior arises from the dynamical interaction of an animal’s nervous system, its body and its environment. Evolution and analysis of dynamical “nervous systems” for model agents, neuromechanical modeling of animals, biologically-inspired robotics, and dynamical systems approaches to behavior and cognition.

Computer vision, object recognition, 3d reconstruction, image processing, artificial intelligence, data mining, machine learning, social media, wearable computers.

Human-robot interaction, social robotics, human-centered computing, cross-cultural studies of technology design and use, science and technology studies, assistive technology, user-centered design and evaluation.

Bioinformatics and Computational Biology, Artificial Intelligence, Complex Networks and Systems, Intelligent Interactive Systems, Machine Learning, Cognitive Science

Cognitive science, developmental psychology, language learning, embodied social cognition, multimodal human-human and human-robot interactions, perception and action, data mining and computational modeling.


Required Courses

All courses provided by faculty in the Intelligent and Interactive Systems track, including the I609 Advanced Seminar, are open to and welcome students from other tracks and programs.

A student must successfully complete ninety (90) credit hours of graduate-level course work. The specific track requirements are listed below.

  • Informatics Core Requirements (6 cr.)
    • INFO I501 Introduction to Informatics (3 cr.)
    • INFO I502 Human-Centered Research Methods in Informatics (3 cr.)
  • Seminar Requirements (6 cr.)
    • INFO I609 Seminar I in Informatics (3 cr.)
    • INFO I709 Seminar II in Informatics (3 cr.)

NOTE: A student must take I609 and/or I709.

  • Research Rotation Requirement (6 cr.)
    • INFO I790 Informatics Research Rotation (3 cr.)

NOTE: A student must complete two rotations of I790. A third rotation will not count for course credit.

  • Theory and Methodology Requirement (12 cr.)

NOTE: These courses must be appropriate for a Ph.D. in Informatics.

  • Minor Requirements (9-12 cr.)

NOTE: Typical minors include Cognitive Science, Statistics, Computer Science, and Human-Computer Interaction.

  • Electives (12-30 cr.)

NOTE: A student must have all electives approved by the student's advisor and the Director of Informatics Graduate Studies prior to enrolling in the course.

  • Thesis Reading and Research (minimum of 21 cr. and maximum of 30 cr.)
    • INFO I890 Thesis Readings and Research
  • Breadth Requirement

All IIS track students are required to take both a course that will help them develop their technical skills in the field (e.g. artificial intelligence, computer vision, advanced prototyping), and a course that presents the conceptual and human-oriented aspects of the field (e.g. human-robot interaction, embodied cognition). Either course can be taken as their second seminar. The chosen course should be appropriate for the student’s professional development. In this way students will have the ability to communicate across the multiple disciplines that compose the domain of IIS research.

Optional Courses

In addition to required courses, faculty in the track offer courses that provide more targeted training is specific areas.

  • INFO-I 540 – Human Robot Interaction
  • INFO-I 590 – Vision for Intelligent Robotics
  • CSCI-B 551 – Elements of Artificial Intelligence
  • CSCI-B 554 – Probabilistic Approaches to Artificial Intelligence
  • CSCI-B 657 – Introduction to Computer Vision
  • CSCI-B 659 – Computer vision for intelligent robotics
  • COGS-Q 580 - Introduction To Dynamical Systems In Cognitive Science
  • INFO-I 547 - Music Information Processing: Audio

Additionally, a number of courses taught by other faculty are also relevant to the ISS track:

  • COGS-Q 511 – Introduction to Embodied Cognitive Sciences
  • COGS-Q 530 – Programming Methods in Cognitive Science
  • COGS-Q 550 – Models in Cognitive Science
  • COGS-Q 551 – Brain and Cognition
  • COGS Q-560 – Experimental Methods in Cognitive Science
  • COGS-Q 570 – Behavior-based Robotics
  • INFO-I 526 – Applied Machine Learning
  • INFO-I 530 – Field Deployments
  • INFO-I 534 – Seminar in Human-Computer Interaction
  • INFO-I 543 – Interaction Design Methods
  • INFO-I 549– Advanced Prototyping
  • INFO-I 586 – Artificial Life
  • INFO-I 590 – Relational Probabilistic Models
  • CSCI-B 552 – Knowledge Based Artificial Intelligence
  • CSCI-B 553 – Neural and Genetic Approaches to Artificial Intelligence
  • CSCI-B 555 – Introduction to Machine Learning
  • CSCI-B 651 – Natural Language Processing
  • CSCI-B 659 – Stochastic Optimization for Machine Learning
  • CSCI-B 659 – Reinforcement Learning for Artificial Intelligence
  • STAT-S 620 – Introduction to Statistical Theory
  • STAT-S 657 – Statistical Learning and High-Dimensional Data Analysis
  • STAT-S 681 – Statistical Machine Learning
  • STAT-S 682 – Introduction to Graphical Models
  • STAT-S 710 – Statistical Computing