The program consists of the following core activities

  • the requirements for the Ph.D. in Machine Learning;
  • the four core course requirements for the CNBC certificate;
  • exposure to experimental techniques in the form of a lab rotation;
  • a roughly semester-long project to satisfy the PNC first-year research requirement and the first of the MLD speaking skills requirements;
  • a year-long project that would satisfy both the PNC second-year research requirement and the MLD Data Analysis Project requirements;
  • participation in CNBC activities as a CNBC student; and
  • a Ph.D. thesis on a neuroscientific topic; if there is a single advisor, that person should be both a CNBC faculty member and affiliated with MLD; otherwise, the student may two co-advisors who, between them, have CNBC and MLD affiliations.

Additional satellite activities through the CNBC will also foster students’ professional and scientific development.

Course requirements

Students must complete the four-course requirement of the CNBC certificate program in the following areas: (i) cell and molecular neuroscience/neurophysiology, (ii) systems neuroscience, (iii) cognitive neuroscience, and (iv) computational neuroscience. Recommended courses fulfilling this requirement include

  • (i) 03-762 Advanced Cellular Neuroscience (CMU) or NROSCI 2100/2101 Cellular and Molecular Neurobiology (Pitt)
  • (ii) 03-763 Systems Neuroscience (CMU) or  NROSCI 2102 Systems Neuroscience (Pitt),
  • (iii) 85-765 Cognitive Neuroscience, and
  • (iv) to complete the computational requirement, students must take the combined Pitt/CMU course, 36-759 Statistical Models of the Brain (CMU) / Math 3375 Computational Neuroscience (Pitt).

Note that this is not exactly the same as the standard CNBC computational requirement.

To meet the course requirements in Machine Learning they must take

  • 10-715 Advanced Introduction to Machine Learning,
  • 10-702 Statistical Machine Learning,
  • 10-705 Intermediate Statistics,

They must also take any two of the following:

  • 10-708 Probabilistic Graphical Models,
  • 10-725 Convex Optimization,
  • 15-826 Multimedia Databases and Data Mining,
  • 15-750 Algorithms, or 15-853 Algorithms in the Real World.

Any substitutions or exemptions from coursework must be recommended by the student’s advisor and approved by the program co-directors and the co-directors of graduate studies in Machine Learning.

Program Milestones

First year research requirement: By the end of the first calendar year in the program, all students will be required to complete a data-analytic project. The purpose of the project is to have the student identify a biological problem, understand the data collection process, articulate the goals of building a model or performing a particular kind of analysis and implement this computational approach. In some cases the project may be a precursor to the student’s eventual thesis project. Both written and oral summaries of the project must be presented. The project will be evaluated by a committee consisting of at least three faculty, of whom at least two are PNC training faculty. Students who wish to enter the joint program from MLD after their first year may be able to waive this requirement with the permission of the PNC training faculty.

Second year research requirement: All students will be required to complete a deeper computational project. The student’s work on the project should demonstrate that the student has 1) the ability to analyze and interpret experimental data in a particular area 2) the ability to develop and implement a computational approach incorporating the relevant level of biological detail and 3) the ability to organize, interpret and present the results of the computational work. This project should be a body of work suitable for publication. It is expected that the research will be written up as a paper to be submitted to a journal in the relevant field. In the second year, students are expected to work on research about 1/3 of their time during the academic year and full time during the summer. In most cases this project will be on an area related to the student’s eventual thesis project, and in most cases it should be completed by the end of the student’s second calendar year in the program. In addition, the results of the project will be presented publicly in the form of a seminar. This project, which counts as the Data Analysis Project in MLD, will be evaluated by a committee consisting of at least three faculty, two of whom are PNC training faculty and one of whom is ML faculty appropriate to the topic.

Note the MLD speaking skills requirements will be met via the first-year research project and second-year research project presentations, specified above. In addition, students in this joint program must participate in the MLD journal club, but participation in a CNBC-related journal club may be used as a substitute if approved by the MLD graduate co-advisors.

Ph.D. Thesis proposal: Required coursework should be completed by the end of the third year. During the fourth year a Ph.D. candidate should present a thesis proposal first to his or her thesis committee and then to the CNBC and MLD community.

The student will have two joint advisors, one from MLD and the other a CNBC faculty member from outside of MLD. A thesis committee will be formed and should be composed of at least four members, one of whom is an external member (typically from outside CMU and Pitt); two must be PNC training faculty; two must be MLD faculty; and at least one CMU or Pitt member must be from a discipline outside of statistics and computer science. The thesis committee is subject to approval by the PNC training faculty and the MLD faculty.

The thesis proposal should include: a succinct summary of the proposed research problem; the significance of the proposed research; a review of relevant literature relating to the problem; a review of the candidate’s work leading up to the thesis, including preliminary results; a clear statement of remaining research; and a tentative schedule for completing the work. It should also conform to the stylistic requirements for thesis proposals in MLD. The thesis committee must offer its preliminary approval of the proposal. The student then arranges to present the proposal publicly, so that CNBC and MLD faculty and other community members can attend. Formal approval is conferred by the MLD faculty and the PNC training faculty.

Ph.D. Thesis Defense: Normally, the dissertation is completed during the student’s fifth year. The final defense is a public presentation, in accord with the College and University requirements for the Ph.D. It is the candidate’s responsibility to ensure that the Departmental, College and University guidelines are followed for publicity of the defense and availability of the thesis document at least two weeks prior to the defense. Note that the defense must be held at least 21 days before the date the degree is awarded.