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About

Dr Moni holds a PhD in Artificial Intelligence & Data Science in 2014 from the University of Cambridge, UK followed by postdoctoral training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Dr Moni then joined UQ in 2021. He also worked as an assistant professor and lecturer in two universities (PUST and JKKNIU) from 2007 to 2011. He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to increase the performance and transparency of AI-based automated decision-making systems. His research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving humans explainable AI models through feature visualisation and attribution methods. He has applied these techniques to various multi-disciplinary applications such as medical imaging including stroke MRI/fMRI imaging, real-time cancer imaging. He led and managed significant research programs in developing machine-learning, deep-learning and translational data science models, and software tools to aid the diagnosis and prediction of disease outcomes, particularly for hard-to-manage complex and chronic diseases. His research interest also includes developing Data Science, machine learning and deep learning algorithms, models and software tools utilizing different types of data, especially medical images, neuroimaging (MRI, fMRI, Ultrasound, X-Ray), EEG, ECG, Bioinformatics, and secondary usage of routinely collected data.

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Skills

Experience

Honorary Senior Research Fellow

Charles Sturt University (CSU), Bathurst

Jan-2024 to Present
Associate Lecturer

University of New South Wales (UNSW Sydney)

Feb-2015 to Nov-2022
Postdoctoral Research Fellow

Garvan Institute of Medical Research (GIMR)

Feb-2015 to Nov-2022
USyd Fellow

University of Sydney (USYD)

Dec-2017 to Nov-2022
Postdoctoral Researcher

University of Cambridge

May-2011 to Feb-2015
Senior Lecturer

The University of Queensland (UQ)

Aug-2021 to Dec-2024

Education

University of Cambridge

Ph.D. in Computer Science & Technology

Passout Year: 2015
Islamic University, Kushtia (IU)

MSc in Computer Science & Engineering

Passout Year: 2006
Islamic University, Kushtia (IU)

B.Sc. in Computer Science & Engineering

Passout Year: 2004

Publication

Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden o...

Background The six global nutrition targets (GNTs) related to low birthweight, exclusive breastfeeding, child growth (ie, wasting, stunting, and overweight), and anaemia among females of re...

Burden of disease scenarios by state in the USA, 2022–50: a forecasting analysis for the Global Burden of Disease Study 2021

Background The capacity to anticipate future health issues is important for both policy makers and practitioners in the USA, as such insights can facilitate effective planning, investment, ...

Global, regional, and national burden of HIV/AIDS, 1990–2021, and forecasts to 2050, for 204 countries and territories: the Global Burden of Disease...

Background As set out in Sustainable Development Goal 3.3, the target date for ending the HIV epidemic as a public health threat is 2030. Therefore, there is a crucial need to evaluate curr...

The global, regional, and national burden of urolithiasis in 204 countries and territories, 2000–2021: a systematic analysis for the Global Burden o...

Background Urolithiasis is a common urological problem that is associated with high morbidity. A comprehensive assessment of the non-fatal and fatal health trends of urolithiasis by age, se...

DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model

Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The ide...

DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model

Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The ide...

Identification of putative causal relationship between stroke and 1504 complex traits using large-scale phenome-wide screening

Stroke is the second leading cause of death and the third leading cause of long-term disability in the world. This study aimed to explore the novel putative causal genetic relationship of st...

The burden of bacterial antimicrobial resistance in the WHO European region in 2019: a cross-country systematic analysis

Background Antimicrobial resistance (AMR) represents one of the most crucial threats to public health and modern health care. Previous studies have identified challenges with estimating the...

A Novel Mixed Convolution Transformer Model for the Fast and Accurate Diagnosis of Glioma Subtypes

Glioblastoma is the most common adult brain tumor, significantly impacts disability and mortality. Early and accurate diagnosis of glioma subtypes is essential, but manual categorization is ...

Global, regional, and national stillbirths at 20 weeks' gestation or longer in 204 countries and territories, 1990–2021: findings from the Global Bu...

Background Stillbirth is a devastating and often avoidable adverse pregnancy outcome. Monitoring stillbirth levels and trends—in a comprehensive manner that leaves no one uncounted—is i...

Projects

Dec-2017 to Nov-2011

Statistical Bioinformatics and Machine-Learning Methods for Diagnosis and Prognosis of ovarian cance...

AUD 500,000

Funded by University of Sydney

Eleven outstanding early career researchers from around the world will join the University of Sydney in 2017 under the University of Sydney Fellowship scheme. Now in its 21st year, Sydney Fellows was the first scheme of its kind in Australia when launched in 1996. It aims to recruit promising young scholars in order to enhance the research strengths and culture of the University and enable them to contribute to its thriving intellectual life. This year, the scheme focused on recruiting talented recent doctoral graduates who can contribute to our whole-of-university multidisciplinary initiatives, including the Charles Perkins Centre, the Brain and Mind Centre and the China Studies Centre.
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Jun-2018 to Jun-2020

develop AI-based health-care related software products

Aud $40,000

Funded by Karte Ltd (Japan) and iHealthOmics Ltd (Hong Kong)

Seed funding from two companies Karte Ltd (Japan) and iHealthOmics Ltd (Hong Kong) to develop AI-based health-care related software products. Received seed funding ($40,000) from Karte Ltd. 2018-2020
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Jan-2016 to Jan-2017

The Garvan Research Foundation & Ridley Corporation award

AUD 50,000

Funded by Garvan Institute of Medical Research

Jan-1970 to Jan-1970

Deep learning models development and application to the Neuro Imaging (MRI and fMRI)

Magnetic resonance (MR) imaging has become an important non-invasive radiological modality for various clinical applications, such as stoke and cancer. Extracting meaningful clinical information without human interaction is a challenging task. Developing such automatic methods are important in order to reduce human errors and the time taken by clinicians. In this project, the student will develop novel deep learning algorithms to solve segmentation and detection problems from imaging that could possibly be deployed to MRI & fMRI scanners and may eventually used for diagnostic purposes. The project will involve applying computer vision and deep learning techniques to MR image processing and analysis.
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Jan-1970 to Jan-1970

Deep Leaning Model to identify Neuroimaging biomarkers

Jan-1970 to Jan-1970

Deep Learning models to solve inverse problems utiling MRI/fMRI image

Jan-1970 to Jan-1970

AI-based based model development for Magnetic Resonance Imaging

Conference/Seminar/STTP/FDP/Symposium/Workshop

Conference
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Real-time human activity recognition using non-intrusive sensing and continual learning

Hosted By:

RMIT University Australia ,

Melbourne, Melbourne, Australia
With a rapid increase in the ageing population across the globe, there is an urgent need for the development of affordable and sustainable solutions to provide aged care support services. Recent advancements in sensor technologies coupled with the use of artificial intelligence (AI) make it possible to monitor and classify the activities of daily living (ADL) of residents in aged care settings, making it easier to detect and predict any potential health problems. The development of such an architecture, however, presents two key challenges: (i) the determination of appropriate sensors and (ii) the selection of suitable AI approaches to recognise individual activities. While existing studies often only focus on addressing one challenge at a time, in this paper, we present the design and implementation of a real-time human activity recognition system called HARNIC, which uses not only non-intrusive sensors but also utilises continual learning to classify individual activities in a simulated environment. We conducted a thorough analysis of current non-intrusive sensors and subsequently selected appropriate sensors for real-time activity monitoring by considering several features such as adjustable sensitivity, detection range, trigger modes, processing power and accuracy. Using the sensors, we designed and simulated a smart aged care environment in a laboratory setting and collected ADL data. This data is categorised into three levels i.e., low, medium, and high, based on the type of activity. We then worked on generating a benchmark data set used to build machine learning models and performed testing of our models. To address the second challenge, we considered incremental and non-incremental methods and evaluated their effectiveness in recognising individual activities in real-time. Our initial experiment results indicate a clear superiority of our HARNIC over the existing state-of-the-art methods used in this study.
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Honours & Awards

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Charles Sturt Excellence Awards
Awarded by:

Charles Sturt University, Australia

Year: 2023
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Best Impact Award
Awarded by:

International Conference on Applied Intelligence and Informatics, UK

Year: 2021
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Early Career Fellowship
Awarded by:

University of Wollongong Engineering & information science

Year: 2020
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Certara-Monash Fellowship Awarded
Awarded by:

Certara Australia Pty. Ltd

Year: 2019
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The Ridley Ken Davies Award ($50,000)
Awarded by:

Ridley Corporation, Australia

Year: 2016
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Travel award
Awarded by:

ANZBMS Conference, Australia

Year: 2016
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Best student paper award
Awarded by:

international conference- IDBSS2014, UK

Year: 2014
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Travel award
Awarded by:

University of Tennessee, USA

Year: 2013
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The Cambridge Commonwealth, European & International Trust award
Awarded by:

The Commonwealth Trust, UK

Year: 2011