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Starts 4 July 2025 17:03

Ends 4 July 2025

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Searching through Materials: Generate, Emulate, Simulate

Join a captivating session with Max Welling as he delves into the cutting-edge techniques of generation, emulation, and simulation for materials research. This MIT-Harvard colloquium presents a fusion of machine learning applications poised to revolutionize biomedical research. Engage with pioneering thoughts and understand the intersecti.
Broad Institute via YouTube

Broad Institute

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Overview

Join a captivating session with Max Welling as he delves into the cutting-edge techniques of generation, emulation, and simulation for materials research. This MIT-Harvard colloquium presents a fusion of machine learning applications poised to revolutionize biomedical research.

Engage with pioneering thoughts and understand the intersection of artificial intelligence and computer science, tailored for professionals and enthusiasts seeking to expand their horizons in the field.

Whether you're a seasoned researcher or new to the terrain of AI, this colloquium promises valuable insights that inspire and inform. Hosted on YouTube, the session is part of a series dedicated to advancing knowledge in Artificial Intelligence and Computer Science courses, aimed at fueling innovation and fostering collaboration within the scientific community.

Syllabus

  • Introduction to Materials Research
  • Overview of materials science
    Importance of machine learning in materials research
    Introduction to Max Welling’s contributions
  • Techniques in Generation
  • Generative models in materials science
    Applications of GANs and VAEs in materials prediction
    Case studies: Success stories and challenges
  • Emulation Methods
  • Definition and importance in research
    Techniques for data-driven emulation
    Examples from biomedical research
  • Simulation Techniques
  • Differentiating simulation from emulation
    ML-driven simulations in materials analysis
    Computational frameworks and tools
  • Integration of Methods
  • Combining generation, emulation, and simulation
    Multi-modal approaches in research
    Tools for effective integration
  • Applications in Biomedical Research
  • Case studies highlighting practical applications
    Challenges and future directions
  • Ethical and Societal Implications
  • Impact of AI and ML on materials research
    Addressing ethical concerns
  • Conclusion and Future Outlook
  • Recap of key insights
    Future trends in AI-driven materials research
  • Course Evaluation and Feedback
  • Assessment criteria
    Continuous improvement through feedback

Subjects

Computer Science