Field Course in Computational Population Biology

 

 

 

 

 

Instructors:   Tanya Berger-Wolf (UIC)

                       Daniel Rubenstein (Princeton)

                        Iain Couzin (Princeton)

 

 

 

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collage1A unique highly integrated pilot field course is offered in Kenya (at the Mpala Research Centre) where US biology (graduate from Princeton University) and engineering students (graduate from UIC) are working with faculty in both disciplines to learn how to ask questions, frame hypotheses and understand how and why the disciplines and cultures do this differently. The course began with a ‘boot camp’ where students learned the key concepts and approaches from biology, computer science and engineering. The initial interdisciplinary orientation was followed by a research project in the field. The on-location course was followed up throughout the semester at each university, culminating in a min-conference of student presentations.

 

 

 

·      Course projects

 

·        Course wiki

·        Kenya travel blog

·        Pictures and videos

·        Final project presentations.

 

·         UIC news article about the course

 

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Students are expected to gain from the course:

·        Ability to integrate a wide range of technical computer science knowledge and apply it in the context of solving problems in population biology

·        Ability to function as part of an interdisciplinary team to bring an idea to a finished computational product

 

Students are expected to cover half of the travel expenses. Estimated cost of participation: about $1,250

(thanks to a generous donation of Bill Unger, member of the CoE Advisory Board,  that covers half of the expenses)

 

Enrollment limit:

5-7 engineering students (PhD, from UIC)

10 biology students (PhD, from Princeton)

 

Prerequisite knowledge:

·        Basic biology

·        Calc I

·        Statistics

·        Computer Literacy

·        For biologists: basic ecology, population biology

·        For engineers: mathematical foundations, algorithms, computational thinking

 

Schedule:

·        Beginning of December: meet with students, give information about the course, and prepare for the course

·        Jan 4 – Jan 23:  Field portion of the course

·        January 25 – end of the semester: On-campus portion of the course

·        April 30 (tentatively): videoconference where all teams will present their final project results.

 

Computational syllabus:

·        Agent based simulations and modeling

·        Basics of control theory

·        Data mining

·        Social network analysis

·        Streaming algorithms

·        GIS

 

Project themes:

·        Identification of individuals (who?)

·        Location of individuals (where and when?)

·        Interactions (who is with whom?)

  • Activity and behavior (what are they doing and why?)