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Starts 5 June 2025 18:18
Ends 5 June 2025
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Searching through Materials: Generate, Emulate, Simulate
Explore Max Welling's insights on materials research through generation, emulation, and simulation techniques in this MIT-Harvard colloquium on machine learning applications in biomedical research.
Broad Institute
via YouTube
Broad Institute
2463 Courses
1 hour 2 minutes
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Overview
Explore Max Welling's insights on materials research through generation, emulation, and simulation techniques in this MIT-Harvard colloquium on machine learning applications in biomedical research.
Syllabus
- Introduction to Materials Research
- Techniques in Generation
- Emulation Methods
- Simulation Techniques
- Integration of Methods
- Applications in Biomedical Research
- Ethical and Societal Implications
- Conclusion and Future Outlook
- Course Evaluation and Feedback
Overview of materials science
Importance of machine learning in materials research
Introduction to Max Welling’s contributions
Generative models in materials science
Applications of GANs and VAEs in materials prediction
Case studies: Success stories and challenges
Definition and importance in research
Techniques for data-driven emulation
Examples from biomedical research
Differentiating simulation from emulation
ML-driven simulations in materials analysis
Computational frameworks and tools
Combining generation, emulation, and simulation
Multi-modal approaches in research
Tools for effective integration
Case studies highlighting practical applications
Challenges and future directions
Impact of AI and ML on materials research
Addressing ethical concerns
Recap of key insights
Future trends in AI-driven materials research
Assessment criteria
Continuous improvement through feedback
Subjects
Computer Science
