Offerings for high achieving students

The School of Information Technologies (SIT) has two special programs that give high achieving students the opportunity to enroll in research-related IT project units as part of their undergraduate studies:

  • Talented Student Program (TSP)
  • Special Studies Program (SSP)

These special project units are meant replace elective units in first, second and third year. Below you will find more information about these programs and a list of projects available to students.

Talented Student Program (TSP)

The TSP is run by the Faculty of Science. The program provides challenging material to exceptional science students to enable them to maximise their intellectual potential and growth. Students enrolled in science degrees can take part in the TSP through the School of Information Technologies by taking research-related project units in first, second and third year. Entry to the TSP is by invitation from the Dean of the Faculty of Science.

For more information about the TSP please visit the Faculty of Science TSP website.

Special Studies Program (SSP)

The SSP is run directly by the School of Information Technologies and is open to all students with a strong interest in computing. It gives gives high achieving students the opportunity to carry out a research-related project under the supervision of an SIT academic.

Entry criteria for the SSP

In order to be allowed to enrol in an SSP unit, a student must have:

  • for first-year students, a minimum ATAR (or equivalent) of 99, and
  • for second and third-year students, an HD average in IT units of study (INFO, COMP and ISYS) and a Distinction average in non-IT units of study.

Invitations to the program are sent by email at the beginning of each academic year (usually during O-week). However, students that meet these criteria and have not yet received an invitation can also apply for entry by contacting the TSP/SSP coordinator directly.

Finding a supervisor

In order to be able to enrol, you must first find an SIT academic who agrees to supervise your project.

Research activities at the School of Information Technologies span a wide range of topics such as biomedical multimedia technologies, human computer interaction, visualization of large complex data sets, high performance computing, language technology, knowledge discovery and the economy of knowledge, machine learning, data mining methods for high dimensional data, foundations of programming languages, theoretical computer science, and algorithm design.

For information on SSP research projects available, see the list of current projects, contact the TSP/SSP coordinator, or browse the School's research areas webpage and get in touch with a group whose interests align with yours.

SSP units

The SSP units of study codes are:

  • INFO1911 Special Project 1A (Semester 1)
  • INFO1912 Special Project 1B (Semester 2)
  • INFO2911 Special Project 2A (Semester 1)
  • INFO2912 Special Project 2B (Semester 2)
  • INFO3911 Special Project 3A (Semester 1)
  • INFO3912 Special Project 3B (Semester 2)

Please not that these are 6cp units that require the same time commitment as any other regular unit of study.

If you are in year X and you want to enrol in semester Y, then you need to enrol in INFOX91Y.

How to enrol

Before you enrol, please make sure that you fulfill the entry criteria and that you have found an SIT academic willing to supervise your project.

To enrol, add the appropriate unit of study code to your enrolment through Sydney Student. At the end, you will be asked to apply for special permission. Please specify in your request the name of your supervisor; otherwise, the request will be automatically turned down.

Frequently asked questions

  1. I am not enrolled in an IT degree, can I still do the SSP projects in the School of Information Technologies?
    Yes, as long as you meet the SSP entry criteria. You can enrol in these units the same way as you would with any elective unit.
  2. Can I replace a subject that is core for my degree with an SSP project?
    No. Project units are not meant to replace core units. The only way to replace a core unit is by special permission by the Undergraduate Director.
  3. Do I need to drop a unit in order to do these projects?
    This is what we recommend. You should consider these projects as any other 6cp unit of study. Most students will replace an elective unit with a project, but in rare cases a student may choose to take a project unit in addition to their normal load.
  4. When is the deadline to enrol in these projects?
    When it comes to enrolling, you should consider these projects as any other 6cp unit of study, the only difference being that it requires special permission.
  5. How many SSP students are there in the School of Information Technologies?
    The program is very selective and therefore very small. Typically, less than 10 students participate in the SSP each year.
  6. Can I do two of these projects in the same semester?
    No, which is why there is only one unit of study code for each semester.
  7. I am in the TSP program. Are the SSP projects different than what I can do through TSP?
    No. The SSP program is very similar in spirit to the Faculty of Science TSP. If you are in the TSP you can choose to do these a project in the School of Information Technologies as a TSP project or as an SSP project. There is no difference. In most cases, TSP students will choose to do these projects using TSP unit of study codes.
  8. Can I do a project outside the list of available projects?
    Yes. Students can discuss other topics with supervisors. You are encouraged to look at the research areas in the School and to get in touch with potential supervisors.
  9. Can I do a project with a supervisor from a different School or from NICTA?
    No. We can only organize the enrolment if the work is supervised by an SIT academic. Other schools may have similar programs; for example the Faculty of Science has the TSP, as well as the SSP in each school.
  10. I would like to do a project but I am not sure what area to choose.
    Excellent! That is a very good start - we expect most students to be unsure at first. Arrange a meeting with the TSP/SSP coordinator to narrow down your interest areas.
  11. What kind of work is involved in these projects?
    The type of work can vary quite a lot from project to project. A typical project will start with a lot of reading of background material, followed by a number of tasks which can be implementations, or solving theoretical problems. At the end of the semester, students are required to submit a written report, describing the project, background material, the work done, and the results.
  12. How are these projects marked?
    The project aims (expected outcomes, deliverables, etc) are set between the student and supervisor at the beginning of the semester. When the work is complete, and the report has been submitted, the supervisor and SSP coordinator mark the project together.


For advice or further information on the TSP and SSP in the School of Information Technologies, please contact:

Dr Julian Mestre (SSP and TSP Co-ordinator)

Ms Katie Yang (Undergraduate Administrative Officer)

Associate Professor Bing Bing Zhou (Undergraduate Director)

Project Offerings



Weidong (Tom) Cai

Intelligent 3D Single Neuron Reconstruction

Area: Computational neuroscience

The single neuron reconstruction is one of the major domains in computational neuroscience, a frontier research area intersected with signal processing, computer vision, artificial intelligence and learning theory, applied mathematics, fundamental neuroscience and quantum physics. The 3D morphology of a neuron determines its connectivity, integration of synaptic inputs and cellular firing properties, and also changes dynamically with its activity and the state of the organism. Analyzing the three-dimensional shape of neurons in an unbiased way is critical to understanding how neurons function and developing applications to model neural circuitry. Such analysis can be enabled by reconstructing tree models from microscopic image stacks by manual tracing. However such manual process is tedious and hard to scale. This project aims to develop novel computational approaches for automatic 3D reconstruction of neuron models from noisy microscopic image stacks. Such methods would enable faster and more accurate neuron models to further accumulate the knowledge of single neuron functionality and neural network connectome.

Neuroimaging Computing for Early Detection of Dementia

Area: Biomedical image computing

Dementia is one of the leading causes of disability in Australia, and the socioeconomic burden of dementia will be aggravated over the forthcoming decades as people live longer. So far, there is no cure for dementia, and current medical interventions may only halt or slow down the progression of the disease. Therefore, early detection of the dementia symptoms is the most important step in the management of the disease. Multi-modal neuroimaging has been increasingly used in the evaluation of patents with early dementia in the research setting, and shows great potential in mental health and clinical applications. The objective of this project is to design and develop novel neuroimaging computing models and methods to investigate pattern of dementia pathology with a focus on early detection of the disease.

Context Modeling for Medical Image Retrieval

Area: Bioimage informatics

Content-based medical image retrieval is a valuable mechanism to assist patient diagnosis. Different from text-based search engines, similarity of images is evaluated based on comparison between visual features. Consequently, how to best encode the complex visual features in a comparable mathematic form is crucial. Different from the image retrieval techniques proposed for general imaging, in the medical domain, disease-specific contexts need to be modeled as the retrieval target. This project aims to study the various techniques of visual feature extraction and context modeling in medical imaging, and to develop new methodologies for content-based image retrieval of various medical applications.

Structural Feature Representation for Image Pattern Classification

Area: Image pattern classification

Image pattern classification has a wide variety of applications, such as differentiation of disease patterns and detection of interest objects. The classification performance is largely dependent on the descriptiveness and discriminativeness of feature representation. Consequently, how to best model the complex visual features especially the complex structural interactions is crucial. Currently many different ways of image feature extraction have been proposed in the literature, yet their performance is still unsatisfactory and feature extraction remains a hot topic in computer vision. This project aims to study the various techniques of structural feature representation, and to develop new methodologies for various applications in the medical imaging domain.

Alan Fekete

Several projects on cloud-based and concurrent storage systems

Area: Databases

See descriptions

Seok-hee Hong


Scalable Visual Analytics of Big Data

Area: Visual analytics

Technological advances such as sensors have increased data volumes in the last few years, and now we are experiencing a “data deluge” in which data is produced much faster than it can be used by humans.

Further, these huge and complex data sets have grown in importance due to factors such as international terrorism, the success of genomics, increasingly complex software systems, and widespread fraud on stock markets.

Visual analytics is the science of analytical reasoning facilitated by interactive visual interface.

This project aims develop new visual representation, visualization and interaction methods for humans to find patterns in huge abstract data sets, especially social networks, biological networks, and finance/business networks.

These new visualization and interaction methods are in high demand by industry.

David Lowe






Augmented reality remotely-accessed labs
Existing remote labs largely duplicate conventional experimental labs, but the computer interface provides an opportunity to enrich the experience of interacting with the equipment by using augmented reality approaches (imagine a magnetics experiment where the video image is overlayed to show the magnetic field lines). This project involves developing the software interfaces for an existing remote laboratory in order to provide an illustrative prototype. The prototype will demonstrate the benefits that can be achieved through the use of augmented reality technologies.

Architecture for collaborative remotely-accessed labs
The leading remote labs software management system – Sahara – has been designed to be consistent with multi-student distributed collaboration, but this functionality has not yet been fully explored or implemented. This project will investigate extending Sahara to incorporate distributed student collaboration within an experiment session.

Remote control of physical systems: the role of context
It is becoming increasingly common to use remote access to control physical systems. For example, researchers within the Faculty have been exploring remote and automomous control of Mars Rovers, mining equipment, teaching laboratory apparatus and fruit picking robots. This project will focus on the role of contextual information in supporting engagement and learning in these systems.

Simulation of chronic pain through control of pressure gloves
(In conjunction with Prof Phil Poronnik, Biomedical Sciences)
Work is underway with the artist Eugenie Lee to develop an installation to inform the public about chronic pain. This project requires a glove that can be pumped up with viscous fluid under computer control (probably a good job for an Arduino). Essentially this is trying to emulate pressure etc on the hands to go along with the Oculus illusions…

Bernhard Scholz


Area: Programming Languages

The aim of the Soufflé project is to design and implement novel compilation techniques for the efficient translation of Datalog-like programs to C++. Datalog-like languages are actively used as a domain-specific language for rapid prototyping including networking, data retrieval, semantic web, and program analysis for large-scale code bases with million lines of code to find bugs and security anomalies.  This research effort is in collaboration with Oracle Labs/Brisbane.

Query scheduling is an important combinatorial problem in producing efficient nested loop structures for relational algebra operations. The order of the loops is arbitrary for guaranteeing the correctness. However, the order has impact on its efficiency.  Finding a cost-optimal order is a challenging research question. In this project, we extend scheduling techniques that have been used in relational databases in the past (e.g. Selinger's algorithm), with automated feed-back directed compilation techniques to identify hot-spots in the loop-nest.

Masa Takatsuka



Kinect-based 3D interaction

Area: Visualization, human-computer interaction

This project aims to develop various techniques to use 3D capture device such as Microsoft's Kinect to achieve natural user interface in order to interact with environment/information.

Data Visualization

Area: Visualization, human-computer interaction

The object of this project is to develop method to generate images from complex real-world data (financial, environmental, political/sociodemographic, educational, etc) so that a user can actually see the complex data.

Judy Kay

Kinect-based large display interaction

Area: Computer Human Adapted Interaction

This project involves creating software tools and doing studies to evaluate their effectiveness for creating interactive large public displays. These enable a passerby to stop and use the display for various tasks, such as exploring an information space. We have installed such a display at the SIT Building. We also have a set of tools that make use of the Kinect to interpret body "gestures" of a person near the wall. We are currently creating additional tools and conducting usability evaluations of the systems built with them.

Irena Koprinska

Predicting weight-loss from gut microbial data using machine learning
Co-supervised by Irena Koprinska (SIT), Eline Klaassens and Mark Read (CPC)

Area: Machine Learning

A myriad of diet-interventions have emerged in response to obesity. However, there is considerable variability in a patents’ response. It is becoming clear that diet and genetics alone do not explain this, and that the gut microbial community (the microbiome) is key in determining patients’ weight-gain and potential weight-loss in response to different diet interventions.

Understanding exactly how the microbiome responds to diet is non-trivial. There is considerable overlap in the functions that particular bacteria can perform, and bacteria can also adapt to changing environments, substitute and compensate for one another’s function. As a result, it is extremely difficult to attribute weight-loss and weight-gain to individual bacteria and it is necessary to analyse bacterial co-occurrence.

This project will apply machine learning techniques to extract patterns of bacterial co-occurrence and investigate how they correlate with weight gain and weight loss. Several data sets are available. The ultimate goal is to predict in advance how a patient will respond to a diet, and this has an enormous potential for improving healthcare and well being.