Programs & Events

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Python for Genomic Science

Python for Genomic Science, July 6 - July 18

MW 10am-11:30am

YINS, 17 Hillhouse Ave, Room 335

This course offers an introduction to using Python for genomics. The course was originally developed at Johns Hopkins, as a part of a course sequence in genomic science. The course consists of brief online lectures, 8 short tests and a final exam. No prerequisite, open to all.

Class 1: Wed, July 6 

Overview of Python and first steps towards programming

Class 2: July 11

Data Structures, Ifs and Loops in Python

Class 3: July 13

YINS Summer Seminar

The YINS Summer Seminar Series is designed for students, faculty, and staff of the Yale Institute for Network Science, as well as for their collaborators and summer visitors, to exchange ideas with the rest of the YINS community in a supportive, encouraging environment that promotes learning, individual growth, and the highest standards of academic research. Bring your lunch, and actively join our community!

The YINS Summer Seminar Series will take place on Wednesdays at noon on the 3rd floor of 17 Hillhouse Ave.

YINS Summer Seminar: Mark McKnight

Mark McKnight, “Breadboard: First Slice”

Talk summary: Join Mark McKnight, the lead developer of breadboard, for a 90-minute hands-on workshop. After a brief demo, Mark will lead the participants through downloading and installing breadboard, launching their first game, and making modifications to the included public goods game. 

Breadboard is a software platform for developing and conducting experiments on networks using online participants. Learn more at breadboard.yale.edu

Inference, Information and Decision Systems Group

The Inference, Information and Decision (I.I.D.) Systems Group is led by Amin Karbasi. The research in I.I.D. is at the intersection of learning theory, large-scale networks, and optimum information processing. We devise new algorithms, build models, analyze the behavior of large and complex networks, and develop systems that can automatically acquire and reason about highly uncertain information. Our application domains include interactive recommender systems, social and neural networks, and optimal experimental design.

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