Autonomously Modelling Human Behaviour

This is a funded position for a master’s student in the School of Computing that will work closely with the Ingenuity Labs on a project to autonomously model human behaviour. The student will be co-supervised by Prof. Ali Etemad and Prof. Christian Muise.

Project Summary

This project aims to capture interpretable insight from human users of a system using modern AI techniques. The learned representation will capture the core elements of observed human behaviour in a form detailing how and when the user transitions from one mental state to another. The source of behavioural information will be data retrieved from biomedical devices such as heart rate or skin conductance sensors. The elements of the learned representation, and the mechanics it captures, will all be learned entirely in a data-driven fashion. The research will be conducted on a driving simulation testbed that will allow for mixed human-machine control of the (virtual) vehicle.

Research Areas

  • Deep learning & machine learning for representation learning
  • Time series analytics
  • Dynamical systems & automated planning
  • Human-computer interaction

Application Procedure

Expertise in this specific area is not essential, but a passion/interest for exploring and discovering new ideas in the field is a must. Unless explicitly stated above, the focus of the research must follow closely to what is proposed.

To apply to the above position, email Prof. Muise with the following details:

  1. Your name and a bit of background about yourself. Please include any exceptional circumstances in your career so far (e.g., time taken for health of family reasons, extended industry experience, etc). We’d like to know who you are and we recognize that many successful students have taken unconventional paths in getting here. Help us understand your path and achievements in context of your particular experiences.

  2. The position name and expression of your interest in the area.

  3. A CV/Resume that includes any academic/outreach* experience.

  4. Academic transcripts from your past university experience.

  5. (if available) A link to any software/projects you’ve worked on (e.g., GitHub profile).

Once we receive the above, we will send along details on the next steps of the process. Currently, this will include the following:

  1. Reviewing a paper (your choice from a set of 5 by the lab) to provide feedback on.

  2. A coding exercise (roughly) tailored to the position.

  3. One or more interviews with Prof. Muise and students of the Mu Lab.

The Mu Lab typically receives far more interest than we can possibly support. To help demonstrate your interest and ability, below are some suggestions. While none of these are mandatory, they go a long way to establishing your suitability with the lab and help you stand out as a candidate.

  • Being able to read, understand, and reproduce existing research is an important component to conducting your own research. Take on a reproducibility challenge from any of the modern AI conferences (AAAI, IJCAI, ICAPS, ICML, ICLR, NeurIPS, …) from the last 2 years. More information on the process can be found here. The expectation is that you choose something from a paper that would take a weekend or two to reproduce.

  • (for planning-related positions) Much of our research involves planning models (learning them, solving them, and ultimately helping machines to understand them). Concepts for planning are typically taught in most undergraduate AI courses. If you need a refresher, this is a decent tutorial. Model one of the final two domains here using the online editor (you can import a wide variety of existing domains for working examples). To share it, use Session > Save, and send the read-only link.

  • (for PhD positions) Submit a 1-2 page research statement that describes a potential research agenda that aligns with the subject area above.

It’s worth noting that while item (2) is fairly specific to labs such as ours, items (1)+(3) are useful for many other applications you may be considering.

*: The Mu Lab is dedicated to building and fostering a diverse team of researchers. We openly welcome applicants from all walks of life and ask that you emphasize in your application your commitment and any past experience to initiatives that aim to support underrepresented minorities. Such aspects are valued and promoted by members of the Mu Lab, and will be viewed favourably during our selection process.