Biomedical engineering

Supervisors: Dr Jinglei Lv, A/Prof. Mayuresh Korgaonkar, Prof. Fernando Calamante

Eligibility: Knowledge about signal processing, image processing and experience on programming

Project Description:

We are so close to reading the mind with the modern neuroimaging technology. The electroencephalogram (EEG) records the electrical activity of billions of neurons while the functional magnetic resonance imaging (MRI) reflects the blood oxygen consumption because of neuronal firing.

Now at our lab, we have the hardware setup to record both signal modalities simultaneously. We can record the brain activity during resting state as well as with cognitive tasks, even movie watching. The concurrent activity recording from both EEG and fMRI helps us not only understand how the brain works and how the mind is generated, but also suggests potential biomarkers for psychiatric disorders, such as Depression, Bipolar and Schizophrenia. It demands smart engineering to decode faithful signals among massive noise in this advanced setting.

In this project, you will work with both biomedical scientists and neuroscientists to develop a pipeline of experiment design, data collection and data processing with simultaneous EEG and fMRI.

Requirement to be on campus: No

Supervisors: Prof. Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility:

• Basic knowledge in electronics.
• Experience in PCB design.
• Willingness to dedicate time and effort to the project.

Project Description:

Electroencephalogram or EEG is an electrical signal recorded from the head to indicate brain’s electrical activity. It is a vital physiological measurement which provides biomarkers of several neurological disorders, cognitive workload, stress, vestibular disorders and sleep.

Across the past few decades, EEG has been the most important indicator for sleep assessment, where it is used in conjunction with several other measured bio signals in a procedure called Polysomnography (PSG).

This project will aim at developing a sensor which can be worn on the forehead providing convenience and comfort with good quality signal. As such, this work will focus on developing a non-contact or capacitive EEG sensor to obtain biomarkers for different sleep stages.

The work will include developing state-of-the-art electronics with optimal sensor design through a working prototype. Performance evaluation will be carried out against a reference EEG device in laboratory settings.

In this project you will learn the fundamentals of designing, developing and testing biomedical electronics, along with the knowledge of acquiring bio signals from human body and consideration of environmental interferences. This will not only enrich your electronics skills, but also inform you about the process of system design for a medical wearable device.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility:

• Proficiency in hardware description languages (HDLs) such as VHDL or Verilog.
• Basic knowledge of PCB design.
• Willingness to dedicate time and effort to the project.

Project Description:

This project aims to design a hardware platform for capturing electroencephalography (EEG) signals using a Field Programmable Gate Array (FPGA) and a Bluetooth Low Energy (BLE) module.

The platform will focus on leveraging the computational efficiency and flexibility of FPGAs to process EEG signals in real-time, while utilizing the low-power communication capabilities of BLE to transmit the processed data to a PC or mobile device for further analysis.

Through this project, students will gain a holistic understanding of FPGA-based design and its application in physiological signal collection, specifically EEG, laying a strong foundation for future research and innovation in biomedical engineering and healthcare technology.

Requirement to be on campus: *Yes, dependent on government’s health advice

Supervisors: Prof. Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility:

• Basic skills in programming languages such as MATLAB or Python
• Familiarity with data compression algorithms or signal processing techniques
• Willingness to dedicate time and effort to the project
• Skills in hardware description languages (HDLs) such as VHDL or Verilog is bonus

Project Description:

This project aims to design a hardware-efficient data compression method for compressing physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG). By implementing high-efficiency data compression algorithms on hardware, the algorithm will significantly reduce storage and transmission bandwidth requirements while preserving the key features and accuracy of the signals/

The project will leverage advanced digital signal processing (DSP) techniques and hardware accelerators (such as FPGAs) to achieve real-time data compression, enhancing overall system performance and energy efficiency.

Through this project, students will gain a holistic understanding of hardware-based data compression design and its application in physiological signal processing. They will develop critical skills in hardware design, algorithm development, and data compression.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility:

• Proficiency in programming languages such as C/C++.
• Experience in PCB design.
• Willingness to dedicate time and effort to the project.
• Basic knowledge in RF is bonus.

Project Description:

Low-power physiological signal collection has become an essential aspect of modern biomedical research and healthcare applications. By utilizing advanced microcontroller units (MCUs) with integrated Bluetooth capabilities, it is possible to create efficient and portable devices for collecting signals such as electroencephalography (EEG). This project focuses on the design and implementation of a low-power hardware platform for physiological signal collection.

Understanding the importance of low-power design in wearable and portable devices is crucial for extending battery life, ensuring user comfort, and maintaining signal integrity. This project aims to delve into the principles and practices of low-power design, exploring the trade-offs and optimization techniques necessary for developing a reliable physiological signal collection platform. The project includes both hardware design and embedded programming.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisor: Dr Daria Anderson

Eligibility: Must have completed ITAR animal training modules

Project Description:

Invasive neuromodulation is used as a last-line therapy for drug-resistant epilepsy, and recent reports found that neuromodulation induces changes in functional brain networks in patients with successful reduction in seizures.

My lab will test new neurostimulation approaches in a mouse model of temporal lobe epilepsy to determine parameters that drive seizure reduction, monitored through long-term 24/7 Video/EEG monitoring. Although there has been some research on how brain networks physically reorganize post-therapy, the quantification of these changes through structural MRI has not yet been fully explored.

Diffusion and anatomical MRI scans will be acquired in a 7T MRI scanner in the Charles Perkins Centre. This project will involve brain extraction procedures, fixing brain tissue for long-term storage, and optimising imaging sequences for ex vivo diffusion MRI with support from the preclinical Sydney Imaging Core. Structural connections will be quantified across regions defined by the Allen Brain Atlas (ABA) mouse brain atlas.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisor: Dr Ann Na Cho

Eligibility:

• Has Tissue culture experience
• Interested in Neuroscience
• Willing to contribute to animal replacement model (humanised and miniaturised organ model, organoid)
• Availability to conduct all 6 weeks on campus in the lab

Project Description:

The recent breakthrough in developing the Brain organoid which involves cutting-edge technology of human stem cells and knowledge of nervous system development has been highlighted as a next-generation in vitro model for personalised disease modeling and medicine.

The 3 dimensional (3D) self-organising Brain organoid created from iPSCs enables the recapitulation of the endogenous cellular composition, organ-specific structure, phenotype, and functionality of human physiology and development including aging. Furthermore, to reflect the complexity of the central nervous system, organoid methodologies have ever advanced to physically assemble two or three different brain regions of organoid to be integrated into one organoid called ‘assembloid’.

This novel platform will not only enable an investigation of neuronal crosstalk between diverse brain organoids in psychiatric disorders but also screening for potential drug candidates and personalised therapeutics. This project will suit students interested in stem cell engineering and laboratory-grown human brain tissue.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisor: Dr Ann-Na Cho

Eligibility:

• Has Tissue culture experience
• Interested in Neuroscience
• Willing to contribute to animal replacement model (humanised and miniaturised organ model, organoid)
• Availability to conduct all 6 weeks on campus in the lab

Project Description:

Biomedical science is rapidly advancing in the development of lab-grown human cortices, known as cerebral organoids, by utilising cutting-edge human stem cell technology and insights into nervous system development. However, a major limitation of previous cerebral organoid models is their focus on cellular components alone, which significantly differs from the native brain environment, limiting maturation and functional development.

In this project, we aim to develop advanced tissue engineering tools, including novel biomaterials and organ-on-chip systems, to enable self-organizing cerebral organoids that accurately replicate the endogenous cellular composition, structural organization, phenotype, and functionality of human brain physiology. Additionally, we will model the blood-brain barrier to investigate virus infection routes and their mechanisms for hijacking the human brain.

This innovative platform will provide a comprehensive understanding of virus infections and facilitate drug and therapeutic screening as part of personalised medicine. This project is ideal for students interested in stem cell engineering and the study of lab-grown human brain tissue.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: A/Prof.. Arnold Lining Ju, Dr Helen Haimei Zhao

Eligibility:

• Academic Background: Applicants should have an educational background or be currently pursuing a degree in biomedical engineering, digital health, bioinformatics, computer science, or a related field.
• Relevant Coursework: Preferred candidates will have completed coursework or gained practical experience in machine learning or related disciplines.
• Technical Skills: Familiarity with coding languages and a basic understanding of machine learning concepts are advantageous.
• Research Interest: A strong interest in AI applications within biomedical, digital health, or cardiovascular research is essential.

Project Description:

Our group is dedicated to exploring the mechanobiology of blood vessels, with a specific focus on understanding how the geometry of vessels and fluid dynamics contribute to the development of thrombosis, particularly in cases involving malformed vessels. To advance our ability to assess and manage patients at risk of thrombosis, we aim to build a sophisticated system designed to detect and segment targeted arteries within the brain using Magnetic Resonance Angiography (MRA) images.

This project specifically aims to identify and segment lesions in 3D Time-of-Flight MRA (TOF-MRA) images collected from both healthy individuals and patients suffering from intracranial artery stenosis or intracranial aneurysms. By accurately detecting and analyzing these vascular anomalies, the project seeks to enhance early diagnosis and pave the way for personalized treatment strategies that could significantly improve patient outcomes in vascular health.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: A/Prof.. Lining Arnold Ju, Dr Tao Huang

Eligibility:

• Academic Background: Have an educational background or be currently pursuing a degree in biomedical engineering, chemical engineering, materials science, biology, or a related field.
• Relevant Coursework: Completion of coursework or practical experience in cell biology, tissue engineering, or related disciplines is preferred.
• Technical Skills: Familiarity with basic laboratory techniques and principles is advantageous. Experience with 3D printing, microfluidics, or similar technologies is a plus but not required.
• Research Interest: A strong interest in biomaterials, tissue engineering, or cardiovascular research is essential.

Project Description:

Thrombosis is a major contributor to cardiovascular diseases and presents a significant global health challenge. Existing animal models and in vitro systems fall short in replicating the complexities of human blood vessels and hemodynamics.

This project aims to leverage bioprinting technology to develop advanced in vitro blood vessel models that closely mimic the physiological conditions of native vasculature. By integrating recent advancements in bioprinting with microfluidic systems, the project seeks to explore how these technologies can synergistically enhance the study of thrombosis mechanisms. This approach has the potential to provide deeper insights into thrombosis and improve the accuracy of experimental models used in research.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Lining Arnold Ju; Nicole Alexis Yap

Eligibility: Recommended but not compulsory: Familiar with MATLAB, Python, ANSYS, or any related algorithm programming software. Knowledgeable on the techniques used in clinical image processing and machine learning identification. Knows how to work around 3D printing technologies, along with associated computer software to printer input interfaces.

Project Description:

The underlying cause to cardiovascular diseases such as stroke and heart attacks is the development of clots within the circulatory system, which progresses into the vessels of the human body namely carotid and corohary vessels. Thrombosis is a multivariate condition, where factors such as age, sex, activity, diet, and genetics may all play a role in the susceptibility to pathology. More notably, it has been shown that vessel geometry, vascular endothelium health, and endogenous blood coagulability all play a significant role in thrombogenesis, of which the presentations of each are highly patient-specific.

In response to this, the lab has developed microvasculature-on-a-chip devices to study the effect of patient-specific hemodynamic environment on thrombosis. The microchannels are designed to recapitulate the full 3D architecture of the patient vessels, and be coated with human carotid artery cells before being perfused with whole blood to fully replicate blood flow in the patient body.

Using cutting-edge AI technology and in light of the avant-garde of machine learning and automation within global society today, we aim to develop an AI algorithm to aid the automation of 3D microprinting these vessel-chips, through both MRV segmentation of patient clinical images for double-layered chip alignment for the full lumen chips and identifying key area of thrombus development and pattern in various patient vessel geometries.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Lining Arnold Ju; Dr Wittaya Suwakulsiri

Eligibility:

The ideal candidate should demonstrate:

• Proficiency in basic molecular biology techniques, which include but are not limited to, micro-volume pipetting and centrifugation.
• Complementing this, familiarity with Unix-based scripts and R programming language would be advantageous.
• Preference is given to applicants who have a strong interest in microfluidic system, organ-on-a-chip models, Genetics, and programming.

Project Description:

The use of human organ-on-a-chip systems in biomedical and clinical research has surged due to the increasing need for models that accurately represent human diseases. Traditional preclinical models often fail to predict human physiological responses, underscoring the significance of organ-on-a-chip technology.

In our laboratory, we leverage organ-on-a-chip models in tandem with 'vascular mechanobiology'. To investigate the mechanobiological changes associated with cardiovascular diseases. However, our understanding of how genetic materials influence these mechanobiological alterations in organ-on-a-chip models remains limited. To address this pressing need, the lab is establishing a comprehensive framework for RNA isolation, RNA sequencing and bioinformatic analysis from organ-on-a-chip models.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Alistair McEwan; Michael Wong

Eligibility: Interest in working with people with disability directly

Project Description:

Bio markers of stress and anxiety to help people with communication difficulties or dementia.  These may be molecular or physiological or a combination.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Dr Sabrina Schaly; Prof. Alistair McEwan

Eligibility: Interest in working with people with disability directly

Project Description:

Personalized Communication Boards: Create customizable communication boards, possibly digital, that cater to the specific needs of non-verbal individuals, allowing them to express themselves more effectively.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Dr Sabrina Schaly; Prof. Alistair McEwan

Eligibility: Interest in working with people with disability directly

Project Description:

Assistive Eating Devices for Individuals with Limited Dexterity: Develop a low-cost, easy-to-use device that assists individuals with limited hand function in eating independently, such as a utensil stabilizer or a motorized plate.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Dr Sabrina Schaly; Prof. Alistair McEwan

Eligibility: Interest in working with people with disability directly

Project Description:

Customizable Sensory Feedback Devices: Create wearable devices that provide customizable sensory feedback, such as vibrations or gentle pressure, to help individuals with sensory processing disorders regulate their responses to environmental stimuli.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Prof. Alistair McEwan; Dr Petra Karlsson

Eligibility: Interest in working with people with disability directly

Project Description:

LiterAACy project – enabling books for literacy to become digitally accessible. Through software, hardware, assistive technology, human interfaces, user centred design.

Requirement to be on campus: Yes, dependent on government’s health advice.

Supervisors: Prof. Alistair McEwan; Dr Mark Tracy, Senior Clinical Lecturer Westmead NICU

Eligibility: Most work will be at Westmead Campus

Project Description:

Abdominal emergencies such as necrotizing enterocolitis (NEC) is a common, catastrophic illness of the gastrointestinal system that affects mostly preterm newborn babies. Early accurate detection is both difficult and essential to prevent serious and lasting complications.

The abdominal Xray (AXR), the cornerstone for assessment at the bedside, is an imprecise tool that can be misinterpreted or incorrectly assessed resulting in misdiagnoses or delays in life-saving treatment. AI can revolutionise image analysis and thus improve early diagnosis and management. NEC is also one of many abdominal emergencies in infancy that can be confused with benign conditions potentially delaying lifesaving surgery.

In Stream 2, we will develop an early warning system to enhance the early detection and diagnosis of this common acute abdominal surgical emergency through an innovative computer software system.

Requirement to be on campus: Yes, dependent on government’s health advice.

Supervisor: Prof. Wenlong Cheng

Eligibility: Undergraduate student with engineering and chemistry background

Project Description:

The School of Biomedical Engineering at the University of Sydney is committed to advancing healthcare technologies through innovative research, interdisciplinary collaboration, and education. At the Nanobionics Lab, our work centers on creating advanced nanomaterials and biotechnologies for applications in biosensing, tissue engineering, and drug delivery.

In this project, you will participate in developing an integrated system of multimodal sensors that includes pressure, strain, and temperature sensors by growing gold nanowires on sponges. Once the sensors are created, you will test their performance using state-of-the-art equipment in the Nanobionics Lab, optimising their functionality by fine-tuning the gold nanowire growth process.

The successful development of an optimised multimodal sensor could pave the way for further development of a soft wearable device capable of monitoring human motion, with real-time data transmitted wirelessly to a smartphone app developed in the Nanobionics Lab.

Requirement to be on campus: Yes, dependent on government’s health advice.

Supervisors: Prof. Wenlong Cheng and Dr Yan Lu

Eligibility: Students own the following experience/research background/interests are welcome for this project:

• Chemistry/chemical engineering
• Nanomaterials
• Electronics
• Biology

Project Description:

Wearable bioelectronics are revolutionizing various industries by enabling portable, real-time monitoring. In agriculture, current methods for assessing product quality often rely on complex laboratory tests that require skilled personnel and specialized equipment, making the process slow and expensive.

This project aims to address these challenges by developing a smart glove embedded with stretchable, wearable biosensors made from conductive plasmonic 2D nanoassemblies. These sensors offer excellent Surface-Enhanced Raman Scattering (SERS) capabilities for detecting pesticide residues when paired with a portable Raman spectrometer.

In addition, the glove's conductive strain sensors can evaluate the shape and softness of plants, fruits, and vegetables. By integrating SERS technology with machine learning algorithms, the glove can provide real-time insights into product health or spoilage, enabling continuous monitoring and analysis.

This innovation not only simplifies product quality assessment but also reduces reliance on lab facilities and serves as a valuable training tool for farmers.

Requirement to be on campus: Yes, dependent on government’s health advice.

Supervisors: Dr Jinglei Lv; Prof. Fernando Calamante.

Eligibility:

• Basic skills with programming.
• Basic knowledge about medical image.
• Self-motivation, curiosity about research and passion to succeed.

Project Description:

In almost every mental health study, inference is made by case control comparison. However, it is indeed reluctant to define the healthy brain as the average of the small samples from one single study. Especially in neuroimage studies, high imaging expense and long acquisition time curse the sample number.

In fact, normal is usually a statistical concept based on large population. The more sample number is included, the higher accuracy should be the model. Nowadays, more and more neuroimages, such as structural MRI and diffusion MRI, are becoming publicly available.

Therefore, we can ask whether the healthy controls from one study can serve as baseline in another study? Can we define the universal healthy brain by pooling all the control individuals to fit one reliable statistical model, against which every disordered brain can be tested to infer abnormality?

Requirement to be on campus: No