Researchers, students, and professionals at all levels of expertise are invited to showcase their work at the 6th annual Data Science Research Bazaar, March 19-20 at the Discovery Building. This interdisciplinary conference welcomes submissions for data-science-focused lightning talks, posters, workshops, and interactive discussions.
This year’s theme is AI and ML in Research: Navigating Opportunities and Boundaries, and we welcome presentations that explore both the potential and limitations of artificial intelligence (AI) and machine learning (ML) in research. The Research Bazaar will provide opportunities to dig into questions such as: How are researchers using AI/ML to approach and address long-standing problems in science? How are AI and ML transforming research workflows and accelerating discovery? What strategies can we adopt to minimize bias, promote fairness, and enforce accountability in AI/ML? A more detailed list of questions related to the theme is below.
While AI/ML will be a key focus, we encourage submissions from all areas of fundamental and applied data science and computational work. We are interested in presentations highlighting diverse perspectives spanning across research domains and industries (e.g., health, biology, agriculture, environment, psychology, humanities, sociology, business, etc.). Possible topics include but are not limited to:
- AI and ML in research: Anything surrounding the theme (see above)
- Case studies in real-world applications: Showcase data science solutions in areas such as: health, wellness, and healthcare; environmental science, agriculture, and sustainability; instructional practices, pedagogy, and educational policy; open science and open source; digital humanities; cities, communities, policy, and government; and other areas
- Interdisciplinary collaborations: Highlight projects that bridge research domains to address challenges and opportunities
- Ethical and responsible data science: Address issues in fairness, bias, transparency, and ethics in data-driven research and decision-making processes
- Innovations in data collection and analysis: Present novel data collection methods, data curation challenges, or advancements in analysis techniques
- Advances in visualization and communication of data: Explore methods for visualizing complex datasets and telling stories with data
- Methodological advances and new tools: Inform and instruct on the use of new software, statistical methods, or algorithms
- Data science for social impact: Focus on projects that address social issues, promote inclusivity, and support underrepresented communities or regions
- Education, training, and community building: Share strategies for teaching data science, building data literacy, developing educational tools, or creating inclusive data science communities
- Data privacy and security: Examine techniques for data anonymization, secure sharing, and privacy-preserving analytics
Whether your focus is on developing computational methods, applied data science, or broader interdisciplinary insights, the Research Bazaar is an opportunity to connect with, learn from, and contribute to UW–Madison’s ever-expanding data science research community.
Submit proposals by January 15, 2025, 11:59pm CT
Click the button below, and fill out the application form to have your work considered for the 2025 Data Science Research Bazaar! We particularly welcome submissions from historically marginalized or excluded groups and those tackling societal inequities in research. If you have any questions, please contact contact@datascience.wisc.edu.
Apply to present a lightning talk, poster, workshop, or interactive discussion.
Deadline: January 15, 11:59 pm.
Suggested questions related to the conference theme
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AI and ML in Research: Opportunities
- How is AI/ML being used to uncover new patterns or generate insights?
- In what ways are researchers using AI/ML to approach long-standing problems from fresh perspectives, leading to breakthroughs across disciplines?
- How is AI/ML transforming research workflows and accelerating discovery?
- How can agentic AI systems—those capable of autonomous actions and decision-making—be leveraged to accelerate research, and what are the opportunities for using these systems in complex, dynamic environments?
- How can AI/ML be integrated with various research disciplines to drive innovation, whether in scientific experiments, social research, or the humanities?
- What novel applications of AI/ML are emerging at the intersection of traditionally separate disciplines, sparking new research areas or transforming existing ones?
- In what ways can AI/ML be used to address pressing societal challenges such as climate change, public health, or economic disparity?
- How can we build inclusive AI/ML research communities, and what support do researchers need?
AI and ML in Research: Boundaries
- How foundational is “foundational AI”? When do foundation models outperform “predictive AI”, and vice versa?
- How can we balance model performance with energy requirements and resource limitations, ensuring sustainable AI research and development?
- How can we ensure AI/ML models are used appropriately and ethically across different fields of research?
- What strategies can we adopt to minimize bias and promote fairness in AI/ML models, particularly when applied to diverse populations?
- How do we improve model explainability while maintaining accuracy and complexity for real-world applications?
What privacy and copyright considerations must researchers keep in mind when working with AI/ML in sensitive or proprietary datasets? - How can accountability be enforced in AI/ML systems, especially when deployed in high-impact or high-risk scenarios?