Bridging AI, Software & Hardware to Build Future-Ready Solutions

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Welcome to my personal website!

This is my platform to share my journey as I continue to explore and build innovative solutions across AI-driven technologies, software, and hardware. My expertise spans agentic AI, ML, embedded systems, edge computing, IoT, full-stack software development, and cloud infrastructure, with a focus on delivering scalable, secure, and future-ready products.

I hold a PhD in Engineering from Deakin University and have served in roles including Solutions Lead, Tech Lead, Test Lead, and Research Engineer, contributing to projects ranging from medical devices for neurological diagnostics to sports performance and health technologies.

Passionate about innovation and impact, I collaborate with businesses and teams to integrate software, hardware, and AI into cutting-edge solutions.

Education

Doctor of Philosophy - Deakin University, Australia (2019 - 2024)
Bachelor of Electrical and Electronics Engineering (Honours) - Deakin University, Australia (2015 - 2019)
Diploma in Engineering - Deakin College (Melbourne Institute of Business & Technology), Australia (2014 - 2015)
Certificate of Management Accounting - The Chartered Institute of Management Accountants (CIMA), the UK, (2012 - 2013)

Work Experience

Firmware Test Engineer (Lead QA Engineer of Firmware Applications) at Catapult Sports (August 2022 - August 2025)
Research Engineer (Electronics and Solutions Lead) at Deakin University (August 2020 - July 2022)
- Developed three medical devices for guiding therapy in Ataxia. The project was funded by the Biomedical Translational Bridge program of Medical Research Future Fund
- Led the Electronic Module Design team with critical design and manufacturing decisions and reviews to manufacture devices timely for clinical trials. The devices are currently in process for FDA approval
- Maintained hardware and software designs compliant to FDA, TGA, CA, ISO, and UL standards while assuring product quality
- Managed Suppliers: Negotiated with EMC testers and manufacturers, maintained deadlines, mitigated risks from Covid-19 and shortage of electronic components. Consulted AWS in building and deploying cloud based IoT system architecture
- Administered the Jira based QMS and Source Control (Bitbucket and GitLab)
- Designed PCBs, CAD files and firmware for three wireless devices: one wearable on wrist or chest, and two instrumented (a cup and a spoon) to collect motion data to evaluate Ataxia with Machine Learning models. Devices were built to communicate directly to the AWS cloud using Wi-Fi or via mobile app using BLE.
Academic at Deakin University (August 2020 - July 2022)

Marked over 200 assignments and examinations of academic unit Data Communication (SEE312)
Supervised over 20 undergraduate and postgraduate projects & Internship programs offered by Network Sensing and Control (NSC) Research Lab students

Research Assistant at Deakin University (January 2019 - December 2019)
- Programmed ESP8266 microcontroller to establish TCP/IP communication between Deakin Smart Bag and Server. The bag is an IoT solution integrated with RFID technology for convenient baggage management
- Developed a Cerebellar Ataxia Evaluation Platform using Python for Windows and Mac OS: The platform collects haptic data from screen and motion data from wireless sensors while participants use integrated neurological tests. The system uses a remote server to host Machine Learning models for evaluation. The outcomes were published in IEEE Xplore (Multi-domain Data Capture and Cloud Buffered Multimodal Evaluation Platform for Clinical Assessment of Cerebellar Ataxia

Honours & Awards

Innovation Award - Deakin University

Issued by School of Engineering in 2019 for the final year undergraduate project

Ph.D. Scholarship

Issued by Networked Sensing & Control Research Lab at Deakin University in 2019. Funded by NHMRC grants GNT1101304, APP1129595 and Friedreich’s Ataxia Research Alliance (FARA) ride Ataxia Europe Research Award

Deakin IoT Smart Bag - Media Appearance

9News Australia, Australian and global newspapers and E-news providers

Top-Up Scholarship

Issued by Networked Sensing & Biomedical Engineering Research Lab at Deakin University in 2021 for the Biomedical Translational Project in Developing Assistive Devices to Diagnose Ataxia.

Research Publications

A Novel Feature from Instrumented Utensils for Clinical Assessment of Friedreich Ataxia (IEEE Engineering in Medicine and Biology Conference · Jul 20, 2023)

Friedreich Ataxia (FRDA) is an inherited disorder that affects the cerebellum and other regions of the human nervous system. It causes impaired movement that affects quality and reduces lifespan. Clinical assessment of movement is a key part of diagnosis and assessment of severity. Recent studies have examined instrumented measurement of movement to support clinical assessments. This paper presents a frequency domain approach based on Average Band Power (ABP) estimation for clinical assessment using Inertial Measurement Unit (IMU) signals. The IMUs were attached to a 3D printed spoon and a cup. Participants used them to mimic eating and drinking activities during data collection. For both activities, the ABP of frequency components from individuals with FRDA clustered in 0 to 0.2Hz band. This suggests that the ABP of this frequency is affected by FRDA irrespective of the device or activity. The ABP in this frequency band was used to distinguish between FRDA and non-ataxic participants using the Area Under the Receiver-Operating-Characteristic Curve (AUC) which produced peak values greater than 0.8. The machine learning models (logistic regression and neural networks) produced accuracy greater than 80% with these features common to both devices.

Read more in IEEE Xplore

A Study of Upper-Limb Motion using Kinematic Measures for Clinical Assessment of Cerebellar Ataxia (IEEE Engineering in Medicine and Biology Conference · Jul 20, 2023)

Cerebellar Ataxia (CA) is a group of diseases affecting the cerebellum, which is responsible for movement coordination. It causes uncoordinated movements and can also impact balance, speech, and eye movements. There are no approved disease-modifying medications for CA, so clinical studies to assess potential treatments are crucial. These studies require robust, objective measurements of CA severity to reflect changes in the progression of the disease due to medication. In recent years, studies have used kinematic measures to evaluate CA severity, but the current method relies on subjective clinical observations and is insufficient for telehealth. There is a need for a non-intrusive system that can monitor people with CA regularly to better understand the disease and develop an automated assessment system. In this study, we analyzed kinematic measures of upper-limb movements during a ballistic tracking test, which primarily involves movements at the shoulder joint. We aimed to understand the challenges of identifying CA and evaluating its severity when measuring such movements. Statistical features of the kinematic signals were used to develop machine learning models for classification and regression. The Gradient Boosting Classifier model had a maximum accuracy of 74%, but the models had low specificity and performed poorly in regression, suggesting that kinematic measures from shoulder-dominated movements during ballistic tracking are not as viable for CA assessment as other measures.

Read more in IEEE Xplore

Modified Recurrence Quantification Analysis for Objective Assessment of Cerebellar Ataxia (IEEE Engineering in Medicine and Biology Conference · Jul 20, 2023)

Cerebellar Ataxia (CA) is a neurological condition that affects coordination, balance and speech. Assessing its severity is important for developing effective treatment and rehabilitation plans. Traditional assessment methods involve a clinician instructing a person with ataxia to perform tests and assigning a severity score based on their performance. However, this approach is subjective as it relies on the clinician’s experience, and can vary between clinicians. To address this subjectivity, some researchers have developed automated assessment methods using signal processing and data-driven approaches, such as supervised machine learning. These methods still rely on subjective ground truth and can perform poorly in real-world scenarios. This research proposed an alternative approach that uses signal processing to modify recurrence plots and compare the severity of ataxia in a person with CA to a control cohort. The highest correlation score obtained was 0.782 on the back sensor with the feet-apart and eyes-open test. The contributions of the research include modifying the recurrence plot as a measurement tool for assessing CA severity, proposing a new approach to assess severity by comparing kinematic data between people with CA and a control reference group, and identifying the best subtest and sensor position for practical use in CA assessments.

Read more in IEEE Xplore

A Comparative Severity Assessment of Impaired Balance due to Cerebellar Ataxia using Regression Models (IEEE Engineering in Medicine and Biology Conference · Aug 27, 2020)

Cerebellar ataxia (CA) refers to the impaired balance and coordination resulting from injury or degeneration of the cerebellum. Testing balance is one of the simplest means of assessing CA. This study compares instrumented assessment and clinical assessment scales of the balance test called Romberg's test. Inertial Measurement Unit (IMU) data were collected from a sensor attached to their chest of 53 subjects while they performed the test. The corresponding clinical scores were also tabulated. Using this data, 99 features were extracted to quantify acceleration, tremor and displacement of body sway. These features were filtered to identify the subset that better characterize the distinctive behavior of CA subjects. Elastic Net Regression model resulted a greater agreement (0.70 Pearson coefficient) with the clinical SARA scores. The overall results indicated that data from a single IMU sensor is sufficient to accurately assess balance in CA. The significance of this study is that evaluation of balance using Recurrence Quantification Analysis produces a comprehensive framework for the assessment of CA.

Read more in IEEE Xplore

Multi-domain Data Capture and Cloud Buffered Multimodal Evaluation Platform for Clinical Assessment of Cerebellar Ataxia (IEEE Engineering in Medicine and Biology Conference · Aug 7, 2020 )

Cerebellar Ataxia is a neurological disorder without an approved treatment. Patients will have impaired and uncoordinated motor functionality making them unable to complete their day-to-day activities. Ataxia clinics are established around the world to facilitate research and rehabilitate patients. However, the patients are generally evaluated by human - observation. Therefore, machine learning based data analysis is popular on motion captured via sensors. There are many neurological tests designed to analyse the motor impairments in different domains (such as upper limb, lower limb, gait, balance and speech). Clinicians follow scoring protocols to record the severity of patients for each domain test. This paper delivers a clinical assessment platform combining 12 neurological tests in 5 domains. It captures motion (from BioKin sensors), haptic and audio data (from the tablet or laptop screen). A data analysis system is hosted in a remote server which evaluates data to produce a severity score via different models built for each neurological test. The assessment platform clients and server communicate via a cloud buffer system. The scores input by the clinicians and predicted by the machine learning models are logged in the cloud database. This enables clinicians and doctors to view and compare the history of patient diagnosis. The server system is structured for automated score model upgrades via prompted approval. Thus, the most viable scoring model could be accommodated for each test based on longitudinal studies.

Read more in IEEE Xplore

Neuromorphic Chip Embedded Electronic Systems to Expand Artificial Intelligence (IEEE Artificial Intelligence for Industry Conference · Oct 1, 2019 )

Neuromorphic chips are electronic hardware mimicking neurons in human brain in an electronic structure. These ASICs (Application Specific Integrated Circuits) provide artificial neural networks with computational power comparatively higher than most neural networks generated by software algorithms. 'CM1K' is an electronic chip in this family of products. It has a parallel neural network of 1024 neurons. These neurons provide K-Nearest Neighbor (KNN) data classification. The chip requires to be embedded in an electronic system to access all its capabilities. This paper deliver a novel hardware system embedding CM1K neuromorphic chip. The system was implemented in image and video frame analysis for evaluation. The results prove that the system could benefit various applications including security, asset management, home appliances, mail sorting and manufacturing. Since the embedded system provide opportunity to integrate AI in to simple electronics, it helps on extending AI applications.

Read more in IEEE Xplore

Projects

ASSOCIATED WITH CATAPULT SPORTS

Vector (Wearables, Edge devices and Infrastructure)

Project: Vector

Clearsky (Local Positioning System)

Project: Vector Clearsky LPS

ASSOCIATED WITH DEAKIN UNIVERSITY

Quantitative assessment of Cerebellar Ataxia using human motor functions

**Funded by National Health and Medical Research Council, Australia

Quantitative assessment of Friedreich Ataxia using human motor functions

**Funded by Friedreich’s Ataxia Research Alliance (FARA) rideATAXIA Europe research award

Cloud computing and IoT based health monitoring systems (Aug 2019 - Jul 2022)

Project: BioKin IoT and cloud computing system development

Data capture and analysis systems for human motion and behaviour (Aug 2019 - Jul 2022)

Project: BioKin IoT and cloud computing system development

Blockchain for IoT Networks (Feb 2021 - Nov 2021)
Ultra-Low Power IoT System Design for Data Logging and Streaming (Feb 2021 - Nov 2021)
Detection and Tracking Systems using Thermal Infrared Vision (Feb 2020 - Nov 2020)
Localization and Mapping Systems for Mobile Robots and Human Motion Capture (Feb 2020 - Nov 2020)
Developing a Device for Guiding Therapy in Ataxia and Imbalance (Jan 2020 - July 2022)

**Funded by Medical Research Future Fund

Deakin IoT Smart Bag Development (Apr 2019 - Jun 2019 )

A smart bag integrating IoT, RFID technology and cloud processing. The project was published and broadcasted in Australian and global media.

Lbrain electronic platform (Jul 2018 - May 2019)

Developed an electronic platform integrating CM1K Neuromorphic chip and ATMEGA 2560V microcontroller

Neuromorphic Chip Embedded Cognitive Computer Systems to Expand AI Applications (Jul 2018 - May 2019)

Lbrain (electronic AI hardware) platform development for universal applications. Cognitive Computer based on Lbrain. AI integrated multi - purpose software development (UWP - C#). IoT sensors for AI integration (Windows 10 IoT Core).

AI on a chip (Jul 2018 - Oct 2018)

Integrating AI ASICs for embedded system development.

Academic Project- Power Distribution Substation Design (Jul 2018 - Aug 2018)

Planning and designing a safe power distribution substation to suit an estimated daily load profile of customers.

Power Generation and Distribution system design (Mar 2017 - Jun 2017)

The system was designed to provide power to an estimated load and area.

Animatronic head (Jul 2016 - Oct 2016)

An animatronic head was designed and built to demonstrate human facial expressions.

Modern control strategies for induction motor drives (Jul 2016 - Sep 2016)

Research project presenting control strategies used in induction motor drives.

Water storage and purification system design (Jun 2014 - Sep 2014)

The system was designed in an academic group project. It was proposed for village Sandikola in Nepal as a solution for their water system. Supported by Engineers Without Borders (EWB) Australia.

ASSOCIATED WITH EX-PACK LANKA CORRUGATED CARTONS PLC

Manufacturing Plant Performance Display System (Nov 2018 - Dec 2018)

A microcontroller based High Voltage - 7 segment display system to display real time machine performances from machine pulse counts, contacts and timers. The solution was integrated in a corrugated carton manufacturing plant.

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