Dylan Campbell

The Australian National University

School of Computing

About Me

I am a lecturer (US/Europe equivalent: Assistant Professor) in the School of Computing at the Australian National University. Previously, I was a Research Fellow of the Visual Geometry Group at the University of Oxford, where I was supervised by Andrea Vedaldi and João Henriques. Before that, I was a Research Fellow of the Australian Centre for Robotic Vision and ANU, where I was supervised by Stephen Gould. I received my PhD from ANU, supported by Data61/CSIRO, working on geometric vision problems under the supervision of Lars Petersson, Laurent Kneip and Hongdong Li. I hold a BE in Mechatronic Engineering (Hons) from the University of New South Wales, with an honours thesis in robotics completed under the supervision of Mark Whitty.


I have broad research interests within computer vision, optimisation, machine learning, and robotics, with particular expertise in 3D vision and optimisation for deep learning. I have investigated problems of geometric sensor data alignment (including camera localisation, simultaneous localisation and mapping, structure from motion, and optical flow), 3D representations (including neural radiance fields), and differentiable optimisation layers (inserting constrained optimisation problems into deep learning systems). Current topics of interest include discovering and exploiting symmetries in data to share information across long-range physically-motivated connections, and optimisation in deep learning for training neural networks efficiently with respect to time and the quantity of data.

Information for prospective research students

I am always looking for highly motivated students who are interested in conducting research with me. To see what type of work I do, you can read some of my selected papers. I encourage students to contact me but please read the following before doing so:

Existing ANU undergraduate and masters students: From all potential applicants, I require an academic transcript, a CV, a sample of research writing, and a proposed research topic. I have numerous projects ideas in computer vision and machine learning, often advertised here, but I am equally happy for you to propose your own project so long as it aligns with my research interests. If I supervise your project, I will expect you to work consistently throughout, not just before meetings or milestones, and to attend regular meetings. Note that there is no expectation that an Honours project will result in a conference or journal publication. Further resources are available here.

Existing ANU PhD students: Email me to set up a meeting and include information about you and your current project.

Potential PhD students: From all potential applicants, I require an academic transcript, a CV, a sample of research writing, and a proposed research topic. You must contact me at least three months before the admissions deadline, and have read the College's PhD admission website that includes the scholarship deadlines. All applications should come through the ANU applications system. You must have good mathematical and computational skills, preferably with prior experience in machine learning and computer vision. You should have a high standard of oral and written English proficiency, strong programming skills in Python, and familiarity with revision control.

Summer and visiting scholars: Generally, I do not supervise visiting students during the northern hemisphere summer (June to August), but I do participate in the Summer Research Scholar program (December to January). Potential visiting PhD students should send me a description of your work, a list of publications or Google Scholar link, and how your trip will be funded.

Research project proposals: As a minimum, a research proposal consists of (a) the research question, (b) how it relates to existing work, (c) a list of datasets for training/testing and any specialist software required, and (d) a timeline with milestones attached to specific dates. The usual approach is to extend some recent work in a small way (~3 months) and then move onto something more ambitious. Proposal writing resources are available here.

Diversity: I welcome and encourage applications from individuals with diverse backgrounds that may be poorly-represented in computing, including those who identify as women, Aboriginal or Torres Strait Islander, or LGBTQIA+; and individuals from regional or rural areas, culturally diverse backgrounds, low socioeconomic backgrounds, and those living with a disability. I'd particularly like to highlight the Elevate scholarship, which aims to foster the inclusion of women in STEM.






Deep Declarative Networks Workshop (CVPR2020)

The Deep Declarative Networks workshop was held at CVPR2020 and consisted of 6 invited talks and 5 accepted papers with oral presentations. Watch the talks here. Read the papers here.