Controllable Sequence Editing for Counterfactual Generation

Overview

Discover controllable sequence editing techniques for counterfactual generation in biomedical AI, presented by Michelle Li from Harvard Medical School's Zitnik Lab.

Syllabus

• Course Introduction
Overview of Course Objectives
Importance of Counterfactual Generation in Biomedical AI
Introduction to Harvard Medical School's Zitnik Lab
• Fundamentals of Sequence Editing
Definition and Basic Concepts
Key Techniques in Sequence Editing
Challenges in Sequence Editing
• Counterfactual Generation in AI
What are Counterfactuals?
Applications in Biomedical AI
Ethical Considerations
• Controllable Sequence Editing
Concept and Importance
Techniques and Strategies
Evaluating Control Mechanisms
• Tools and Technologies
Overview of Software and Frameworks
Introduction to Popular Programming Libraries
Data Requirements and Management
• Case Studies and Applications
Real-World Examples in Biomedical Research
Exploring Case Studies from Zitnik Lab
Outcomes and Impact Analysis
• Hands-On Workshop
Practical Exercises in Sequence Editing
Developing Counterfactual Scenarios
Group Projects and Presentations
• Evaluation and Best Practices
Assessing Model Performance
Strategies for Improving Controllability
Lessons Learned from Biomedical Applications
• Future Directions in Sequence Editing
Emerging Trends and Innovations
Potential Advances in Biomedical AI
Research Opportunities in the Field
• Course Conclusion
Recap of Key Concepts
Final Q&A Session
Resources for Continued Learning

Subjects

Data Science