Leading the future of smart sensing technology through strategic partnerships and responsible AI innovation.
I'm a Senior Machine Learning Engineer and Digital & AI Theme Lead, combining hands-on AI/ML engineering with strategic leadership in smart sensing technology. With over 8 years of experience, I develop and deploy cutting-edge AI solutions while fostering collaboration between industry, research, and government sectors.
At Jericho Smart Sensing Lab, I design and implement AI systems for optical, acoustic, and RF sensors, working with multidisciplinary teams to deliver deployable prototypes. Simultaneously, as Theme Lead at NSW Smart Sensing Network, I discover AI opportunities across diverse sectors and build strategic partnerships that advance the field.
My journey began with deep technical research in neural networks and intrusion detection systems during my PhD. Today, I bridge the gap between advanced AI/ML engineering and strategic technology leadership, ensuring innovations are both technically robust and responsibly implemented.
When I'm not working on smart sensing innovations, you'll find me in the kitchen experimenting with recipes—I still believe cooking is remarkably similar to coding, with ingredients as variables and instructions as procedures. I even created a flowchart for making stir fry that convinced my husband to cook for me!
Machine Learning & Deep Learning
Neural Networks & AI Systems
Internet of Things (IoT)
Unstructured Data Analysis - Acoustics, Image, and RF
TinyML and Edge AI
Computer Vision & Object Recognition
Data Visualization & Analytics
Python
TensorFlow, scikit-learn
AWS/Azure Cloud Platform
GNU/Linux Development
Agile Project Management
Description tStrategic Thinking & Planning
Stakeholder Management & Collaboration
Cross-sector Partnership Development
Diversity & Inclusion Leadership
Responsible Technology Advocacyext goes here
Proficient in artificial neural networks implementation
Proficient in *NIX development environment
Familiar with multiple machine learning and data analytics algorithms
Gnuplot scripting for visualization purposes
Experienced in machine learning APIs and Framework such as scikit-learn and TensorFlow
Programming knowledge in C/C++, Java, and Python
Identify the relevant data to evaluate the predictive power of the proposed models
Application of cutting edge deep learning ensemble for more accurate predictive results
Interpret results from machine learning outputs, including the reasoning and the data properties behind them
Leading author of journal and conference publications
Writing weekly finding and analysis report
Regular reviewer for AI and machine learning conferences, which involves analyzing the suitability of submitted papers for publication-most recently ICPRAI 2018 and AusDM 2018
Leading a diverse research and technical team
Leading author of journal and conference publications
Good project management skills resulting in multiple publications (full citations below)
Frequent collaboration with stakeholders and other researchers
Organizational skills developed from PhD candidacy, such as taking in feedback, coordinating co-authorships, and causal teaching position, such as attending meetings with the subject coordinators and other tutors, as well as reporting the student progress and contributing to the development of the subject
Presented my research works in international conferences in Australia and abroad (ER 2015, AI 2016)
Communicating complex ideas and information in my causal teaching position to a range of students
Keeping the classroom organized and delivering the material has developed my interpersonal skills
I responsible for end-to-end machine learning implementation for the smart sensors develop within the lab such as optical, acoustic, and RF sensors. I also lead research and technical team to deliver deployable prototypes for sensor fusion, data analytic, and visualization platform.
I provide consultation to disover AI and machine learning opportunities for our industry partners and find relevant research partners within the network member universities to develop smart sensing technology for industry partners. Projects involved so far: sorting recycling, marine mamal observations, and mining tailings dam.
I worked in collaboration with School of Physics to process Microbeam Radiation Theraphy (MRT) high dimensional energy deposition data for treatment planning. We were training Generative Adversarial Networks (GAN) to estimate the energy disposition for treatment planning. I was involved in pre-processing the data, exploring dimensional reduction methods.
I started the role during my PhD studies, for the opportunities to give back the knowledge I have to the students. I worked with the subject coordinators and other casual teachers to deliver the weekly workshops, tutorials, or lab. I also marked assignments and give feedback on how the semester is going. The role really hone in my interpersonal and presentation skills, where I worked with the academic teams and having to explain concepts to an audience who may not come across them before. Most recently, I ran the workshop and laboratory for these subjects : (1) "Information Technology Security and
Risk Management" (2) "Data Mining and Knowledge Discovery"
University of Wollongong, Wollongong, NSW, Australia
Graduated July 2019
University of Wollongong, Wollongong, NSW, Australia
Graduated July 2013
University of Wollongong, Singapore
Graduated April 2011
A. Saraswati, M. Hagenbuchner, & A. C. Tsoi. “Investigation in High Resolution Self-organising Map for Intrusion Detection Application”, 2021 (draft)
A. Saraswati, V. T. Nguyen, M. Hagenbuchner, & A. C. Tsoi. “High-resolution Self-Organizing Maps for advanced visualization and dimension reduction”, Neural Networks (105), pp. 166-184. Elsevier 2018.
A. Saraswati, M. Hagenbuchner, and Z. Q. Zhou, “High resolution som approach to improving anomaly detection in intrusion detection systems”, Australasian Joint Conference on Artificial Intelligence, pp. 191–199, Springer, 2016.
A. Saraswati, M. Hagenbuchner, and Z. Q. Zhou. “High Resolution Self-Organising Map Approach to Improving Anomaly Detection in Intrusion Detection Systems”. Poster presented at: School of Computing and Information Technology (SCIT) Trade Show, 2016 Oct 26, Wollongong, NSW
A. Saraswati, C.-F. Chang, A. Ghose, and H. K. Dam, “Learning relationships between the business layer and the application layer in Archimate models”, in Conceptual Modeling, pp. 499–513, Springer, 2015.
Location: Sydney, NSW