Statement and Highlights

Truly interdisciplinary education is accomplished while accepting a diversity of knowledge, methods, beliefs, and approaches and while looking for solutions to realistic problems, regardless of the discipline. My teaching aims at providing students with an interdisciplinary view of dynamic decision making. At Carnegie Mellon University, I have worked tirelessly to promote and build intellectual diversity among undergraduate and graduate students through direct classroom teaching and through one-on-one advising of students and post-docs that are part of my research program.

One of the highlights of my career has been the direct interaction with post-doctoral fellows, graduate, undergraduate students, and visiting scholars. My post-doctoral fellows have moved positions in academia, industry and government. For example, many of my post-docs are now tenured or on tenure-track in universities including: Georgia Tech, University of Illinois at Urbana-Champaign, University of Balearic Islands, Indian Institute of Technology at Mandi, University of North Carolina, University of Michigan, Louisiana State University, University Warwick, University of Washington, and the University of Texas, El Paso. Other post-docs have taken the government route, going to the Army Research Laboratories, and others have taken the private sector route (e.g., Pinterest, Inc.).

I am always looking for students that would like to learn about dynamic decisions from a behavioral and computational perspective. For current opportunities, please read the “Join Us” section of our website.

Current Coursework

88-312 Decision Models and Games
By Coty Gonzalez
Carnegie Mellon University

Spring 2022

Humans often make decisions in changing and uncertain situations. A car driver entering a new city must adjust decisions rapidly while moving along heavy traffic; firefighter crews entering a burning building must maintain awareness of the development of fire; citizens in a country must change their activities based on the evolution of a pandemic and the restrictions imposed. While challenging, humans are adaptable species. We plan and re-adjust our plans to changing conditions; we keep aware of potentially new courses of action; and we manage our limited time, information, and attention to changing environments. How do humans make decisions in dynamic situations?

This course will explore human decision making as a dynamic process resulting from human interactions with the environment. The course uses decision games to illustrate how humans learn and adapt to changing conditions of choice, and computational models to simulate decision processes and environmental dynamics.

Decision Models and Games will provide: (1) foundational perspectives for using models to represent the dynamics of environments and human decision processes; (2) tools to build computational models of human decision making and of dynamic environments; and (3) practical illustrations of how models and games can be used to understand and generate solutions to a wide range of decision problems, from simple choices to large scale consequential decisions.

Past Coursework

88-380 Dynamic Decisions
By Coty Gonzalez
Carnegie Mellon University

Spring 2016 - Fall 2021

Decisions we make every day may range from simple to highly complex. For example, during driving we make many decisions are effortless and routine (judging the distance to the front car, the speed, the directions); while other decisions such as allocating limited time over multiple school projects in the presence of overwhelming distractions may be very complex. These and many of the decisions we make over-time are, however, very similar: they are made in the presence of environmental change and in the absence of explicit information regarding probabilities and potential outcomes from decisions made. Some decisions appear simple and others complex because they depend on the experiences decision makers hold and on how such experiences are acquired and used in context. The way humans make dynamic decisions depend on individualized experience, cognitive abilities, and their interaction with the particular conditions of the decision environment.

In this course, students will understand how decisions are made from experience, in different dynamic situations, how our cognitive processes (e.g., attention, memory, risk tendencies, and other factors), and how the characteristics of the environments (e.g., time constraints, workload, dynamic change) influence the way those decisions are made. Students will be introduced to different topics of dynamic decision processes by analyzing the sources of error in complex problems, such as cases of accidents and disasters (natural or man-made) in multiple contexts (e.g., aviation, management, military strategy, and others). Students will also use simulations of dynamic systems (e.g., microworlds/decision games) to understand how humans learn and adapt to changing conditions of choice. Finally, students will learn to construct models of dynamic systems and represent them in actionable simulations. Students will learn to conduct simulations of different model scenarios to make predictions and interpret simulation results to provide decision recommendations.

90-777 A2 Intermediate Statistics - Carnegie Mellon University

Fall 2015

Statistics is the science of summarizing, analyzing, and interpreting data. This class is primarily intended to provide you with a practical view of statistics: how statistics can be a meaningful and useful science with a broad scope of applications in business, government, and everyday life. Emphasis will be placed on understanding statistical processes and tools and how you may use those to make conclusions and inference from data sets. The course is divided into four distinct parts:

  • Descriptive statistics, calculation and interpretation of statistical measures to describe raw data.
  • Introductory probability theory and key probability distributions.
  • Fundamentals of statistical inference, hypotheses testing.
  • Analyses of variance and linear regression.

The objectives of the course are to provide students with the ability to:

  • Identify and interpret patterns in raw data;
  • Understand basic ideas of probability;
  • Perform and interpret elementary statistical inferences;
  • Perform and interpret ANOVAs and Regression analyses;
  • Identify meaningful use of statistics in multiple applied problems.

08-775 Cognitive Perspective in Human-Computer Interaction - Carnegie Mellon University

Spring 2010

In this course, we will learn the most up-to-date research in the Psychology of Human-Computer Interaction. The course is divided into themes of relevance for understanding the psychology of HCI. Each week we will discuss a major theme. I will also lecture on the structure of the course and the introduction to Human-Information Processing in the first class.

05-413/813 Human Factors

Summer 2009 - Taught in Nowy Sacz, Poland
Fall 2008 - Taught at Carnegie Mellon University

In this course you will learn basic methods and principles to investigate and analyze problems that involve human factors such as: perception, cognition, decision making and human errors; and you will also learn to use technology design to help improve these processes and avoid error. By the end of the course, you should be able to:

  • Appreciate the breadth and depth of the Human Factors discipline.
  • Apply Human Factors (HF) methods and principles to the evaluation and design of systems in the world around you.
  • Understand human limitations and capabilities and how they impact the design of controls, displays, and related devices.
  • Appreciate how human factors can influence the design and resulting effectiveness of human-system interactions.
  • Demonstrate the critical thinking skills of a Human Factors consultant.

88-431 Dynamic Decision Making

Summer 2009 - Taught in Nowy Sacz, Poland
Summer 2007 - Taught in Doha, Qatar

This is a course in dynamic decision making, introduced at the Carnegie Mellon University Qatar campus. In this course students learn to become a better dynamic decision maker through the use if Microworlds and MFSs. The simulators help learning and acquiring of experience to control dynamic systems, react under time constraints, and gather information and adapt decisions in a rapidly changing environment. The course teaches to to recognize and deal wtih situations where policy interventions are likely to be delayed, diluted, or defeated by unanticipated reactions and side effects.

BDR Seminar


Organized the BDR Seminar schedule.

67-271 Fundamentals of System Development - Carnegie Mellon University

Fall 2005
Fall 2004
Fall 2003
Fall 2002

This is an introductory course in software systems analysis, design and project management. It is a required course in the IS major and minor sequence. In this course students learn the fundamental theory, methods and techniques needed to develop complex information systems projects. The course is organized according to a Software Development Process (SDP) including phases common to many development strategies.

88-368/05610 Introduction to Human-Computer Interaction - Carnegie Mellon University

Spring 2002, 2001

This course provides an overview and introduction to the field of human-computer interaction. It introduces students to tools, techniques, and sources of information about HCI. The course increases awareness of good and bad design through observation of existing technology. Using a systematic approach to design, the course introduces students to the basic skills of task analysis, and analytic and empirical evaluation methods.

88-275 Information Systems Applications - Carnegie Mellon University

Fall 2001, 2000

In this course students design and implement a usable information system for a real client. The client may be affiliated with the university, government, business, or non-profit agency. Students are assigned to teams to work on these projects to produce operational, fully documented and tested computer-based information systems. I supervise the projects throughout the development process.

Courses Taught at the Benemerita Universidad Autonoma de Puebla

1998-1999 Lecturer. Executive Program for Volkswagen's consulting company in information technology: VW GEDAS, S.A. Courses hosted by Benemerita Universidad Autonoma de Puebla (BUAP). Puebla, Mexico.

  • August 19-20, 1999. 10 hours tutorial: "WWW Usability Testing: A Practical Experience".
  • January 25-26, 1999. 10 hours tutorial: "Usability Engineering: Practical Evaluation".
  • July 7-9, 1998. 10 hours tutorial: Perceptual and Cognitive Principles of Human-Computer Interaction: Theory and practice".

Courses Taught at the University of the Americas-Puebla

1996-1997 Assistant & Associate Professor. Department of Computer Engineering. University of the Americas-Puebla (UDLA), Cholulua, Puebla, Mexico.

Master in Computer Engineering Courses:

  • IS621. "Software Engineering" Spring 1997:
    This course discussed topics of software development: general systems concepts, CASE tools, software management and systems analysis and design. Analysis and design methodologies: Structured Analysis and Design, Jackson's structured development, Object-Oriented Analysis and Design (Coad & Yourdon), and Object Modeling Technique (OMT by Rumbaugh).
  • IS650. "Decision Support Systems". Spring, 1996:
    This elective course conveys basis of decision-making research, theoretical frameworks for Decision-Support Systems (DSS) and DSS development and techniques.
  • IS291. "Seminar in Computer Topics II". Spring 1996:
    The course consisted of weekly presentations by experts in different areas: Multimedia, computer Graphic Design, Computer Produced Music, Animation, and Virtual Reality. Students attended lectures and summarized their understanding.
  • IS323. "Software Engineering I". Summer 1996, 1997:
    This course introduces principles of Information Systems, Information Systems Life Cycle, and Structured Analysis and Design methodologies.
  • IS325. "Software Engineering II". Spring 1996, 1997:
    This course presents the techniques for project planning, scheduling, and cost estimation. Lectures focus on Object Oriented Analysis and Design: Object Modeling Technique (OMT by Rumbaugh).
  • IS442. "Introduction to Human-Computer Interaction". Spring, 1996:
    This elective course in the Computer Engineering curriculum conveys the impact of good and bad interface design, and helps students develop an ability to analyze interaction problems. Allows students to practice different methodologies for interface design and evaluation.