Nominations for PhD and Postdocs for the Columbia Program for Human-Guided Machine Adaptation

SEAS Faculty are invited to nominate a PhD student or Postdoctoral Fellow to be part of the Columbia program for human-guided machine adaptation in collaboration with the U.S. Army Research Laboratory. Students and Fellows will be fully supported by the Program (salary and tuition).  Appointments to the Program are for 1 year, with the potential for renewal given satisfactory progress and the availability of funds.  Students/Fellows are not obligated to work for the US Army or Gov’t, though there may be opportunities for internships and employment if there is mutual interest.

This program for Students/Fellows will include the following

  1. A suggested set of courses that include both AI/Machine learning courses as well as courses in human decision making, psychology of team dynamics, human cognition, human-centric design,  human computer interaction. These are not required, but serve as options for students to consider when choosing electives.
  2. Co-mentors for students/postdocs that include an AI mentor and a Human decision making/cognition mentor.
  3. Monthly student/postdoc organized seminar series on Human-guided AI — this will be remote/virtual.
  4. Interaction with ARL scientists and Gov’t Contractors through internships (see below), joint seminar participation, scientific advising and research.
  5. Potential for student/postdoc participation  in ARL internships (not required).
  6. Student/postdoc development and testing of research ideas on testbeds developed by and for ARL
  7. Yearly student/postdoc workshop and research presentations

Though the program is open to all students/postdocs, priority will be given to those that are US Citizens and permanent residents.

The nomination package will include

1) a letter from the primary academic supervisor describing the nominee.

2) why they are a fit for this training program and how their work would match the research goals of the program (i.e., “elucidating principles of effective, stable mutual adaptation between humans and intelligent systems and that improve performance in complex, dynamic environments“).

3) the mentoring team (proposed human decision sciences and AI/computer sciences mentors).

4) CV/resume of the nominee.