Name of moderator:Prof. Dr. Julia Rayz

Hello, Real World: From Universal Knowledge to Situational Awareness

For many applications, the advances in AI brough an unwelcomed realization that standard solutions no longer work for everything. One only has to think how a model developed by a single-discipline based on a dataset applicable to a somewhat-diverse population is applied to a population that was not included, with knowledge derived from other disciplines completely omitted, to appreciate the Mode 2 concepts. This talk will look at some issues that can be solved by an cross- and trans- disciplinary environments, and for the social groups that vastly differ from each other.

Julia Rayz is Professor of Computer and Information Technology in Purdue University. Her research interests are in the areas of Cognitive Science and Artificial Intelligence, focused on Knowledge Representation and Natural Language Understanding. In particular, she is interested in understanding implicit information from communication, detection of salient and novel information, and inferences that are made depending on various levels of knowledge. She has published over 150 papers in artificial intelligence, computational humor, computational semantics, fuzzy logic, information security, and robotic communication and intelligence. She has served on the editorial boards of HUMOR: International Journal of Humor Research, European Journal of Humour Research, and Journal of Integrated Design & Process Science. Rayz is a Board Member of the North American Fuzzy Information Processing Society and a director of the Applied Knowledge Representation and Natural Language Understanding (AKRaNLU) Laboratory.

Name of moderator:Mr. Yifei Hu

Approaching the research problems from the end user’s perspective

The so-called “research scope” defines neat boundaries that help researchers focus on more specific and manageable problems. However, those truly outstanding research projects aim for making perceivable improvements to real-world problems where the research boundaries become vague. From DeepMind’s AlphaGo to OpenAI’s GPT family, the end users were amazed by the advancement of technology in a tangible way, and their passionate involvement made further improvements possible. Approaching the research problems from the end user’s perspective is the key to successful and meaningful outcomes.

Yifei Hu is a Ph.D. Candidate at Purdue University. His recent research interests focus on evaluating and improving the user experience of natural language technology. He is also a full-stack software engineer working closely with the real-world problems and the users. His combined experience drives him to work on solutions with real impact to the world.

Name of moderator:Ms. Geetanjali Bihan

Cross-disciplinary thinking - A crucial ingredient for Mode 2 innovation in the 21st Century

The advent of information explosion and advances in computational prowess interspersed with tools allowing quick knowledge dissemination are accelerating scientific development. Researchers affiliated with Mode 2 science are not only observers but active participants in the changes to socio-cultural structures and their respective impacts on existing and future research goals and applications. Enhanced connectivity within the scientific network also allows for easier access to multidisciplinary research, paving the way forward for developments in transdisciplinary techniques. To ensure that we innovate in the right direction, it is crucial to harmonize links between different disciplines. Thus, cross-disciplinary thinking and awareness of transdisciplinary knowledge can guide researchers toward innovation that accounts for and works with an evolving sociocultural framework.

Geetanjali is a Ph.D. candidate at Purdue University, working on Natural Language Understanding with Dr. Julia Taylor Rayz at the AKRaNLU Lab at Purdue University. Geetanjali's research interests focus on studying semantics and privacy in natural language processing. The two specific areas she is currently working on are: 1) Incorporating structured lexical semantic knowledge within computational models of language, 2) Mitigating data privacy concerns encountered during language model training and fine-tuning. Before joining Ph.D. program, Geetanjali finished her M.S. (2020) in Computer and Information Technology at Purdue University. Prior to joining graduate school, Geetanjali worked as a Data Scientist for 2 years, after completing her undergraduate studies in Electronics and Communications Engineering at Delhi Technological University.

Name of moderator:Mr. Morteza Zangeneh Soroush

Your Brain Talks about Your Creativity

From school to the workplace, from drawing an artistic portrait to producing an aircraft, what we all need in almost all periods of our lives is creativity. Creativity is the game changer and the winning card in all competitions, tasks, and challenges in our lives. Despite the previous psychological studies in this field, to a large extent, it is not clear how our brain works during creativity tasks. My research interests mainly include exploring brain dynamics during creative tasks. I use neuroimaging techniques such as electroencephalography (EEG) as well as machine learning methods to investigate how the brain works when creativity happens, evolves, and turns into a practical and effective solution. In this presentation, I will talk about design creativity, its characteristics, and brain dynamics associated with the different steps of creativity tasks. I will introduce neuroscientific approaches to studying brain behavior in design creativity.

Morteza Zangeneh Soroush was born in May 1985 in Iran. He has been working on several topics such as design and creativity cognition, computational and clinical neuroscience, sleep medicine, and nonlinear time series analysis for almost 15 years. Morteza has published over 25 peer-reviewed papers and over 20 books. He has two national patents and was awarded as the best student author in biomedical engineering. Morteza co-founded a research and educational institute to prepare university graduates for the market and industry. Morteza has been working as an associate research assistant in several universities and research institutions for almost 15 years. His research studies mainly include creativity cognition, neuroscientific, and machine learning methods in design and creativity.

Name of moderator:Mr. Hamed Shirazi

Using the TASKS framework to understand users and system interaction barriers

Designing information systems interfaces based on human cognitive abilities has become one of the most important trends in recent years. Different approaches and methods are developed and proposed under the umbrella of the Human-centered design (HCD) philosophy. Understanding users is one of the most critical characteristics of HCD. Understanding users is a recursive knowledge acquisition process that employs different disciplines such as psychology, sociology, cognitive science, ethnography, and so on. It leads to a deep insight into users’ needs, wants and desires and a well-defined design problem. However, it is a challenging task for design teams. Here, the implementation barriers of this task are presented. Next, it will be discussed how the TASKS framework could be employed as an enabler to help HCD designers understand their users’ cognitive capabilities better. TASKS framework is a newly established framework that helps researchers to detect implementation barriers. The framework has been defined based on an implementation science notion that defines all human activities as tasks. The implementer is an individual or an organization who accomplishes the task. The TASKS framework relies on the following premise: 1) we need an optimal level of mental stress to achieve our best mental performance, and 2) mental stress relates to perceived workload, as well as Affect, Skill, and Knowledge of implementer. The interaction of users with any system could be studied through the lens of this framework.

Hamed A. Shirazi is a Ph.D. student at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada. He is a member of the Concordia Design Lab under the supervision of Dr. Zeng. He has an MEng in Quality System Engineering from Concordia University. His research focuses on design methodologies, particularly Human-Centered Design. In addition, he is studying behavior intervention by design specifically negative behavior such as procrastination. He worked as a senior engineer for manufacturing companies for more than 12 years before starting his studies at Concordia University.

Name of moderator:Ms. Mengting Zhao

A framework for quantifying cognitive states from EEG under complex cognitive activities just like design

This research aims to quantify the changes in cognitive control and workload from EEG signals when people are conducting complex cognitive activities. Taking design activities as a starting point, the present study conducted a series of theoretical analyses and literature reviews to identify the opportunities and challenges for applying EEG to quantify designers' cognitive changes, including cognitive workload and mental capacity. A new framework has been proposed to address the identified challenges as a result of our past research attempts and theoretical analyses. Afterward, the framework was applied to quantify cognitive workload and cognitive control within and beyond the context of design.

As a postdoctoral fellow in DesignLab, Mengting Zhao holds a Doctorate degree in information systems engineering from Concordia University (2022), a Master’s degree in electrical engineering from Paris-Saclay University (2017), and a Bachelor’s degree in information engineering from Xidian University (2014). Mengting Zhao’s research interests include cognitive science, neuroscience, data analysis, and learning behaviours. She has been working in DesignLab for more than five years where she published several papers and contributed to projects in collaboration with the industry and other research teams. Her current research focuses on the EEG-based quantification of cognitive states and the recognition of different behavioural patterns when people are performing complex cognitive activities.

Name of moderator:Ms. Jiami Yang

Knowledge: the good, the bad, and the ways for designer creativity

Design is a highly nonlinear chaotic dynamic process with many possible solutions, some of which can be creative. The chaotic nonlinearity of design dynamics triggers mental stresses in designers, whose creativity happens only when their mental stresses are at an optimal level. Following a deductive approach, this paper investigates how knowledge can contribute to designer creativity by uncovering knowledge's (good and bad) roles in the design process, based on which three ways are recommended to use knowledge properly in design. The assumption is that all designs follow one governing equation, which is a recursive integration of three basic design activities: formulation, evaluation and synthesis. The difference between designs of various fields and different kinds (routine, innovative and creative) lies in the range, content, size and nature of the design space in which the design governing equation works. The design governing equation implies nonlinear chaotic design dynamics, whose solutions are sensitive to its initial conditions and can be routine, innovative or creative. The design governing equation is solved and reformulated by the designer's creativity capability. Therefore, design researchers, practitioners and educators should cohesively look at both designer's knowledge/experience and the designer's creative thinking process.

Ms. Yang is a Master's student at Concordia Institute for Information Systems Engineering at Concordia University and a Research Assistant at the Cumming School of Medicine at the University of Calgary. My major research interest is design science and its applications to creative behaviour. Particularly, I investigate the impact of mental stress on designer creativity.