Esa Rantanen Headshot

Esa Rantanen

Associate Professor

Department of Psychology
College of Liberal Arts
emrgsh@rit.edu
585-475-4412
Office Hours
Mondays and Fridays 1000???1100
Office Location
EAS-2353

Esa Rantanen

Associate Professor

Department of Psychology
College of Liberal Arts

Education

BS, MS, Embry-Riddle Aeronautical University; MS, Ph.D., Pennsylvania State University

Bio

Professor Rantanen trained as an airline pilot and worked as an air traffic controller before going back to school and into an academic career. Before joining the psychology faculty at RIT in 2007, Professor Rantanen was in the faculty of Institute of Aviation of the University of Illinois at Urbana-Champaign.

Professor Rantanen teaches the PSYC 642 Graduate Research Methods, PSYC 714 Graduate Engineering Psychology, and PSYC 234 Industrial and Organizational Psychology courses.

Professor Rantanen's theoretical research interests lie in the areas of human factors in complex and dynamic systems, human performance measurement and modeling, mental workload, decision making, and human error and reliability. His primary research focus is on the role of time and temporal mental models in human performance. Professor Rantanen is currently pursuing research in three applied domains: Air traffic control (air traffic controllers' situation awareness), healthcare (human error in medicine), and process control (control room displays).

Professor Rantanen is a member of the Human Factors and Ergonomics Society (HFES; since 1993), Institute of Electrical and Electronics Engineers (IEEE Systems, Man, and Cybernetics Society; since 1997), and Association for Aviation Psychology (AAP; since 1999). He serves in the editorial boards of journals 'Human Factors' and 'International Journal of Aviation Psychology'. 

emrgsh@rit.edu
585-475-4412
Personal Links
Curriculum Vitae
LinkedIn

Select Scholarship

Dr. Esa Rantanen is a member of the faculty team of the AWARE-AI NSF Research Traineeship (NRT) Program. Graduate students from associated RIT PhD and MS programs are invited to review information on how to apply and benefits for Trainees at our website: https://rit.edu/nrtai. Women, Deaf or Hard-of-Hearing, and African American, Latino/a American, or Native American students are especially encouraged to apply.

Currently Teaching

COGS-500
Foundations in Research
3 Credits
This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations.
COGS-600
Foundations in Research
3 Credits
This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations.
IDAI-720
Research Methods for Artificial Intelligence
3 Credits
Hallmarks of AI are systems that perform human-like behaviors, and AI systems rely on continuous preparation and deployment of data resources as new tasks emerge. In this course, students develop their conceptual, applied, and critical understanding about (1) experimental principles and methods guiding the collection, validation, and deployment of human data resources for AI systems; (2) human-centered AI concepts and techniques including dataset bias, debiasing, AI fairness, humans-in-the loop methods, explainable AI, trust), and (3) best practices for technical writing and presentation about AI. As a milestone, based on research review, students will write and present an experimental design proposal for dataset elicitation followed by computational experimentation, with description and visualization of the intended experiment setup, as well as critical reflection of benefits, limitations, and implications in the context of AI system development and deployment.
ISEE-734
Graduate Engineering Psychology
3 Credits
In this course the students will learn to recognize the integrated (systems) nature of Engineering Psychology, the centrality of human beings in systems design, and to use the topics covered and the available knowledge base to adapt the environment to people. This course will cover several fundamental models of human information processing in the context of human-system interactions. The models may include but are not limited to Signal Detection Theory, Information Theory, theories of attention, both normative and naturalistic decision making-models, Control Theory, and the Lens Model of Brunswick, as well as models of the human as a physical engine, that is, anthropometry, biomechanics, and work physiology. Most topics include readings in addition to the course text as well as a lab exercise with a detailed lab report.
PSYC-510
Senior Project in Psychology
3 Credits
This course is intended for students in the psychology major to demonstrate experimental research expertise, while being guided by faculty advisors. The topic to be studied is up to the student, who must find a faculty advisor before signing up for the course. Students will be supervised by the advisor as they conduct their literature review, develop the research question or hypothesis, develop the study methodology and materials, construct all necessary IRB materials, run subjects, and analyze the results of their study. This course will culminate in an APA style paper and poster presentation reporting the results of the research. Because Senior Project is the culmination of a student’s scientific research learning experience in the psychology major, it is expected that the project will be somewhat novel, will extend the theoretical understanding of their previous work (or of the previous work of another researcher), and go well beyond any similar projects that they might have done in any of their previous courses.
PSYC-714
Graduate Engineering Psychology
3 Credits
In this course the students will learn to recognize the integrated (systems) nature of Engineering Psychology, the centrality of human beings in systems design, and to use the topics covered and the available knowledge base to adapt the environment to people. This course will cover several fundamental models of human information processing in the context of human-system interactions. The models may include but are not limited to Signal Detection Theory, Information Theory, theories of attention, both normative and naturalistic decision-making models, Control Theory, and the Lens Model of Brunswick, as well as models of the human as a physical engine, that is, anthropometry, biomechanics, and work physiology. Most topics include readings in addition to the course text as well as a lab exercise with a detailed lab report.
PSYC-719
Human Factors in Artificial Intelligence
1 - 4 Credits
This course will provide students with fundamental information for human-centered design of applications of artificial intelligence. There are three parts to the course: The first part is about methods of design and evaluation. The second part introduces students to the psychology of sensation and perception, memory, attention, judgment, decision-making, and problem solving, as well as human error and reliability. Finally, students will become familiar with design principles as they apply to displays and controls, human-computer interaction, human-automation interaction, and human-centered automation. Guest lectures and case studies will be examined to illustrate topics covered in it and to provide a survey of the current state of AI research, development, and controversies. Ethics and moral responsibility in technology development, with links to current policy debates, are also discussed in this context.
PSYC-752
Thesis Proposal
3 Credits
The Thesis courses will vary widely but will fulfill the work plan agreed by the student and the adviser. The guiding principles of the Thesis Proposal course are to initiate thesis research including selecting a thesis advisor, choosing and defining a topic, surveying relevant research literature, and planning the research. To complete the course, the student will successfully submit and defend a thesis proposal, which is a detailed and complete plan of the thesis research. The thesis proposal should include exhaustive review of relevant literature, statement of the student's thesis, formulation of hypotheses, operational definitions of independent and dependent variables, and a detailed procedure for carrying out the research. The proposal may also include a section on anticipated results with a detailed plan for analysis of data.

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