Date: Monday, November 18
Time: 08:00 AM - 12:00 PM
Location: B60 R101
Workshop Overview:
The workshop starts with an introduction on diverse notions of causality that are embraced by information systems researchers. A key focus will be on Qualitative Comparative Analysis (QCA), an emerging approach which integrates the strengths of case-oriented qualitative methods and variable-oriented quantitative methods. The workshop gives an opportunity for researchers to expand their toolkit and adopt an inclusive and open-minded attitude toward scholarship to engage with complex and messy problems where no single approach is likely to serve as a silver-bullet in a large program of research. The workshop intends to spark new ideas and discussions, encouraging a rigorous approach to tackling challenging issues in information systems research. The sessions will be designed to cater to a diverse range of participants, ensuring that both experienced and novice researchers find value and learning opportunities. Tentative topics that will be covered in the workshop include: a brief history and overview of different types of causality, introduction to QCA and set theoretic methods, key steps and issues in QCA analysis, QCA topics and questions for IS research, quick introduction to R and the QCA package, and practical Demonstration and hands-on exercises.
Speaker: Dr. Salman Aljazzaf
Speaker Bio:
Dr. Salman Aljazzaf is an Assistant Professor of Information Systems at the College of Business Administration, Kuwait University. His current research interest includes business value of IT, IT governance, digital platforms, digital marketing, and e-government. He earned his PhD in Information Systems and MBA from the University of Maryland, College Park (2019 and 2014) and received his BSc. in Computer Engineering from Kuwait University (2006). Before pursuing his graduate degrees, Salman worked as a network and telecommunications engineer in the Kuwait National Petroleum Company between 2006 and 2012.
Date: Wednesday, November 20
Time: 08:30 AM - 09:30 AM
Location: B60 R101
Workshop Overview:
This workshop focuses on uncovering hidden biases and shortcuts often present in blackbox machine learning models. Participants will learn how explainable AI (XAI) techniques can make these models more transparent, revealing why and how decisions are made. Through practical examples and discussions, the workshop will cover strategies to identify and reduce bias, improve fairness, and ensure accountability in AI systems. Attendees will leave with a deeper understanding of how to apply XAI to build more ethical and reliable AI models in real-world applications.
Speaker: Dr. Raed Alharbi
Speaker Bio:
Dr. Raed Alharbi is an Assistant Professor and Head of the Information Technology Department at the College of Computer Science, Saudi Electronic University (Riyadh, Saudi Arabia). He earned his Ph.D. from the University of Florida (Gainesville, USA) and is the founder and president of the educational and research group "Akadima." Dr. Raed actively engages in various research grants and serves as a member and reviewer for several local and international conferences, including IEEE and BigData. His research interests focus on Explainable Artificial Intelligence (XAI), Arabic language studies, and natural language processing.
Workshop 3 Title: Virtual Personas: A Hands-on Journey into Large Language Models
Date: Wednesday, 20 November 2024
Time: 13:15 PM - 14:15 PM
Location: B60 R101
Workshop Overview:
This interactive workshop explores how Large Language Models (LLMs) create and maintain virtual personas. Participants will gain both theoretical understanding and practical experience through hands-on exercises. The workshop progresses through multiple levels: from basic prompting to complex population-level persona engineering, examining the underlying mechanisms of LLMs. Through a combination of theory and practice, participants will explore the architecture and behavior of these models while working with concrete examples.
Participation Requirements: Participants are encouraged to bring their own PCs with internet access and a Google account.
Speaker: Dr. Enric Junqué de Fortuny
Speaker bio:
Enric Junqué de Fortuny is an Assistant Professor of Managerial Decision Sciences at IESE. Prior to his appointment at IESE, he was an Assistant Professor in Information Systems and Business Analytics at New York University (Shanghai), an Assistant Professor in Marketing at the Rotterdam School of Management (Netherlands) and a Senior Research Fellow at INSEAD's eLab for Big Data (France/Singapore). He holds a Ph.D. in Applied Economics from the University of Antwerp, an M.Sc. in Computer Science Engineering, and a B.Sc. in Computer Science from the University of Ghent (Belgium). His research focuses on data science, information systems, and bridging insights from academia to society, particularly in areas involving fine-grained human behavior and natural language processing. His research has been recognized with the European Research Paper of the Year 2017 award, organized by CIO-NET and the Association for Information Systems. His work has been published in leading journals including Management Information Systems Quarterly (MISQ), Journal of Consumer Research (JCR), Machine Learning (ML), IEEE Transactions on Neural Networks and Learning Systems, and top conferences such as Knowledge Discovery and Data Mining (KDD).
Date: Thursday, November 21
Time: 10:00 AM - 11:00 AM
Location: B60 R101
Workshop Overview:
Dr. Faisal Alotaibi will conduct a workshop on Literature Review and AI-based Tools, emphasizing the importance of literature reviews in research projects. A literature review helps establish context, identify gaps in knowledge, and support research arguments. It is crucial for understanding the existing knowledge base and guiding research direction. The workshop outlined steps for conducting a comprehensive literature search, organizing and structuring the review, evaluating sources, and analyzing findings. Using AI in literature reviews offers benefits such as saving time and effort, enhancing accuracy, uncovering hidden insights, and improving efficiency. Commonly used AI tools include literature search engines, text mining tools, and citation management software. It is important to critically evaluate the results generated by AI tools and ensure their accuracy and reliability through human oversight and data validation. The conclusion of a literature review should summarize findings, identify gaps, propose future research, and set goals for further investigation. Embracing innovation, critical evaluation of AI-generated insights, and addressing ethical considerations are essential for utilizing AI tools effectively in literature review processes. The workshop highlighted the importance of literature reviews in research projects and the potential benefits of incorporating AI technology to streamline the research process.
Speaker: Dr. Faisal Alotaibi
Speaker Bio:
Dr. Faisal Alotaibi is currently an assistant professor at Prince Sattam bin Abdulaziz University. He obtained his PhD in computer science from Liverpool University in United Kingdom and Undergraduate degree in computer system engineering in Edinburgh and Master degree in computer science from Liverpool John Moores Unversity. Dr. Alotaibi’s research interests include Artificial Intelligence in cybersecurity, Machine Learning, and Data Science.
Date: Thursday, November 21
Time: 13:15 PM - 14:15 PM
Location: B60 R101
Workshop Overview:
A technical workshop designed to enhance your data visualization skills for scientific research. Led by Dr. Jumanah Alshehri, this session introduces participants to the fundamentals of data visualization in Python, covering key principles and essential libraries such as Matplotlib, Seaborn, Plotly, and others. Participants will start with basic plotting techniques, advance to complex visualizations and interactive visuals. The workshop will also delve into visualizing both structured data, like tabular datasets, and unstructured data, such as text, equipping participants with strategies to handle diverse data types. Throughout the session, Dr. Alshehri will share best practices for building clear, insightful visuals and integrating them effectively into scientific papers. Attendees will gain a toolkit for transforming data into compelling graphics, enhancing the clarity and impact of their research. This workshop is ideal for researchers eager to elevate their data presentation and create visualizations that convey scientific insights with precision and clarity.
Participation Requirements: Participants should have a basic understanding of Python programming, a Google account, and bring a laptop, as all coding will be done using Google Colab Notebooks.
Speaker: Dr. Jumanah Alshehri
Speaker Bio:
Dr. Jumanah Alshehri is an Assistant Professor in the MIS Department at Imam Abdulrahman Bin Faisal University. Holding a Ph.D. in Machine Learning and Data Mining from Temple University, Philadelphia, PA. Her research focuses on modeling and extraction of knowledge from unstructured (textual) data where she investigates how user-generated content, such as comments, encapsulates rich information that enhances the semantic comprehension of emerging events in news. Dr. Alshehri has published extensively, focusing on topics like media bias, fake news detection, semantic analysis, and aligning user comments with news content. Dr. Alshehri aims to contribute to the broader understanding of how user engagement can be harnessed to improve the prediction, forecasting, analysis, and interpretation of emerging events. Her work has earned recognition from institutions such as the National Science Foundation, and she serves as an Associate Editor for the Big Data Journal.
In her upcoming workshop, “Visualizing Research with Python: A Practical Guide to Data Visualization in Science,” Dr. Alshehri will draw from her extensive experience in computational science to guide participants through effective data visualization techniques using Python.