Ph.D. in Informatics - Intelligent and Interactive Systems Track

Build and test next-gen interactive technologies

If you’re interested in how people interact with digital systems and want to create technologies that can perceive, understand, and respond to people and their environment, the Intelligent and Interactive Systems (IIS) track is a great fit. You’ll explore areas like autonomous robots, human-robot interaction, wearable computing, music and computation, and motion planning. Through research and hands-on projects, you’ll develop new technologies and study how people engage with them, helping shape the future of intelligent systems.

What you'll learn

The Intelligent and Interactive Systems (IIS) track focuses on how humans interact with digital technologies and how to create smart systems that understand and respond to people and their environment. Students and faculty explore areas like AI, computer vision, human-robot interaction, music informatics, and wearable devices.

IIS technologies are becoming part of everyday life. Robots help with tasks, wearables track health, and cloud systems recognize sights and sounds. This track combines technology and human-centered research to build and study these interactive systems.

Graduate students work on a variety of topics such as autonomous robots, social robotics, activity recognition, and culturally aware technology design while gaining hands-on experience and strong theoretical knowledge.

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 more about R-House

Track faculty

Christopher Raphael
Track Director

Christopher Raphael

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

Randall Beer

Randall Beer

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.

David Crandall

David Crandall

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

Selma Sabanovic

Selma Sabanovic

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.

Justin Wood

Justin Wood

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

Chen Yu

Chen Yu

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

Track guide

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.

  • I501: Introduction to Informatics (3 cr.)
  • I502: Human-Centered Research Methods in Informatics Health Informatics Core Requirements (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.

  • 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.

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

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

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.

  • INFO I890 Thesis Readings and Research

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.

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

Qualifying exam

Written and oral examinations will be decided by the student’s committee based on his or her research interests. A typical exam can consist of writing a survey paper and two shorter papers each reporting on a project or problem assigned by the committee, or it can consist of an annotated bibliography preceded by an essay that describes the student’s interpretation of the relevant literature and how they situate their own interests and work within it. The written portion of the exam is followed by an oral exam to defend the written submissions.

Sample dissertation titles

Since IIS is new, we have so far graduated one doctoral student, Jangwon Lee, in 2018, with a thesis titled “Learning Activities from Human Demonstration Videos” (supervised by Crandall and Šabanović. Now at ObjectVideo Labs).

IIS core faculty have supervised Ph.D. students in other tracks who have successfully defended, including:

  • Bambach, Sven (2016). Analyzing hands with first-person computer vision. (Computer Science, supervised by Crandall. Now at Nationwide Children’s Hospital.)
  • Bennett, Casey (2015). Robotic faces: Exploring dynamical patterns of social interaction between humans and robots. (Informatics, Health Informatics track, supervised by Šabanović. Now at Raiven Healthcare LLC.)
  • Chen, Liang (2018). Human-interactive Optical Music Recognition and Music Renotation. (Informatics, Music Informatics track, supervised by Raphael. Now at Google.)
  • Duan, Kun (2014). Conditional Random Field Models for Structured Visual Object Recognition. (Computer Science, supervised by Crandall. Now at Snap, Inc.)
  • Fraune, Marlena (2018). Examining Effects of Groups and Intergroup Contexts on Human-Robot Interaction. (Cognitive Science and Social Psychology, supervised by Šabanović. Now at New Mexico State University.)
  • Gu, Yupeng (2015). Creating Expressive Piano Performance using Statistical Models. (Informatics, Music Informatics track, supervised by Raphael.)
  • Han, Yushen (2013). Score-informed Musical Source Separation and Reconstruction. (Informatics, Music Informatics track, supervised by Raphael. Now at Apple.)
  • Jin, Rong (2017). Graph-based Rhythm Interpretation in Optical Music Recognition. (Informatics, Music Informatics track, supervised by Raphael. Now at Facebook).
  • Johnson, Jeffrey (2018). Selective determinism for autonomous navigation in multi-agent systems. (Computer Science, supervised by Hauser and Crandall. Now at Uber.)
  • Korayem, Mohammed (2015). Social and egocentric image classification for scientific and privacy applications. (Computer Science, supervised by Crandall. Now at CareerBuilder, LLC.)
  • Lee, Heerin (2017). Collaborative Design for Intelligent Technologies. (Informatics, Computing Culture and Society track, supervised by Šabanović. Now at University of California San Diego).
  • Lee, Stefan (2016). Data-driven Computer Vision for Science and the Humanities. (Computer Science, supervised by Crandall. Now at Georgia Tech.)
  • Luo, Jingru (2015). Optimal Motion Planning for Manipulation and Legged Locomotion. (Computer Science, supervised by Hauser. Now at Bosch.)
  • Xu, Tian (Linger) (2018). Intelligence with Interaction: Understanding coordinated behaviors with developmental computational and robotic approaches. (Computer Science and Cognitive Science, supervised by Yu. Now Postdoc at Indiana University.)
  • Zhang, Haipeng (2014). Analyzing the Dynamics Between User-sensed Data and the Real World. (Computer Science, supervised by Crandall. Now at IBM Research.)
  • Zhang, Yajia (2015). Knowledge-driven Motion Planning. (Computer Science, supervised by Hauser. Now at Bosch.)

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