Boston University Machine Intelligence Community is an organization focused on providing opportunities for undergraduate and graduate students to learn about machine intelligence in a community environment. As a brief history, MIC was founded originally as a research reading group at MIT in 2016 after being inspired by the AlphaGo versus Lee Sedol match, and was brought to BU late last semester.

We are a student-led research group sponsored by Boston University's Rafik B. Hariri Institute for Computing and Computational Science and Engineering, BU Software Application & Innovation Lab (SAIL), and BU Spark.



Tuesdays from 7:00 PM to 8:30 PM

  • Machine Intelligence (September 12)
  • Gradient-Based Learning (September 19)
  • Introduction to Neural Networks (October 3)
  • Regularization (October 10)
  • Compositional Data (October 17)
  • Transfer Learning (October 31)
  • Sequential Data (November 7)
  • Deep Reinforcement Learning (November 14)
  • Unsupervised Learning (November 28)
  • Neural Style Transfer (December 12)

Other meetings

  • BU MIC reading groups every Monday from 7:00 PM to 8:30 PM at Hariri Institute
  • MIT MIC reading groups every Wednesday from 5:00 PM.
  • "Learning to learn by discoursing discourse": A chill session where we read short and light papers together and trade thought processes for reading papers, develop intuition, and discourse research ideas. A meta exercise in learning to learn by discoursing discourse!





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Content Creation

Writings blogs, workshops, and other materials.

External Liaison

Manage BUMIC’s relationship with sponsors, companies and other entity outside of the Boston University community.

Internal Liaison

Manage BUMIC's relationship with Boston University community.

Technology/Web design

Setting up the technologies and maintain the website.


Andrei Lapets

Advances in machine learning will have wide-ranging effects on the computational sciences and society at large, and in particular with regard to privacy and cybersecurity. Building interest and awareness on this topic is essential for both productive and responsible engagement with this re-emerging area of research.

Kate Saenko

My research interests are in the broad area of Artificial Intelligence with a focus on Adaptive Machine Learning, Learning for Vision and Language Understanding, and Deep Learning. I am also part of the Artificial Intelligence Research initiative at BU, formed in the fall of 2017 with the goal of promoting AI research and education in the BU community.