Mr. Iko-Ojo Simon, BSc, MSc, is a PhD candidate researching Algorithm Debt in Machine Learning and Deep Learning Systems. He earned his Bachelor’s (Hons.) and Master of Science (MSc) degrees in Computer Science from the University of Jos, Nigeria.
Prior to starting his PhD, Iko-Ojo served as an Assistant Lecturer in the Department of Computer Science at the University of Jos, where he was involved in both research and teaching in the areas of Computer Science and Software Engineering.
He is currently a sessional academic at the Australian National University, where he tutors students in various Computer Science courses.
Master of Public Health - MPH, Reproductive and Child
BSc, Public Health
Experience:
Eskedar Mekonnen is a Digital Research Trainer at Intersect Australia. She is also a PhD candidate at the Robinson Research Institute, University of Adelaide. With a background in Epidemiology, Eskedar possesses extensive skills in research design and analysis. Eskedar has years of experience working with diverse data types and analysis methods, including Machine Learning and longitudinal data collection and analysis. She is skilled in various research software programs, including R, Qualtrics, and REDCap.
Leta holds a Bachelor’s degree in public health from Dilla University (2017) and a Master’s of Public Health in Epidemiology from Jimma University (2020), Ethiopia. He has served as a lecturer and researcher at Salale University, where he taught courses such as Epidemiology, Research Methods, and Data Management while also taking on various academic positions. Currently, Leta is pursuing a PhD in Genetic Epidemiology, focusing on the intriguing roles of human endogenous retroviruses in neurodegenerative diseases.
Philosophy and History of Science, Data science, Programming, Statistics, Data Visualization, Scientific Conversations, Knowledge Sharing, and research.
Experience:
Ali is currently a second year PhD student in Biomedical Engineering at UNSW with over 9 years of research experience through his PhD studies, and as a research assistant in various labs from 2013 till present. He has been programming using Excel, R and bash (Linux) for over 5 years and has a strong background in the analysis of health and biological data resulting in the publication of several bioinformatics papers. During the past 5 years he has been able to develop required specialised skills and gain lots of experience in both programming and data analytics (statistical and Artificial Intelligence-based approaches) which gave him a strong capability in teaching students how to start programming and how to design an efficient analysis pipeline in the Excel, R and bash environments. He has over 4 years of teaching data analytics in both R (statistical analysis, scripting and visualization) and Bash (package development) programing languages. He also has several hours teaching bioinformatics experience at University of Sydney and UNSW as casual tutor.
PhD, Applied and Plasma Physics (ongoing) BSc (Hons I ), Physics
Personal Interests:
Applied plasma physics for nanoparticle fabrication and implementation in biomedical applications. Passionate about conveying my knowledge to the scientific and public community.
Experience:
2+ years of research experience in plasma polymerised nanoparticle and their biomedical applications; 5+ years as a tutor for students at various academic levels.
PhD (in progress), Distributed Machine Learning BEng (Hons.), Software Engineering
Experience:
Murtaza is a PhD candidate at the University of Melbourne researching privacy-preserving, secure and distributed machine learning paradigms. He completed his Bachelor of Engineering with First Class Honours from Monash University in 2022.
Prior to his PhD, Murtaza worked as a Software Engineering Analyst at Goldman Sachs, where he designed distributed systems and AI-driven automation for financial workflows.
He is currently a Sessional Academic at the University of Melbourne, where he tutors students in Distributed Systems and Cluster and Cloud Computing. He is also a Digital Research Trainer at Intersect, delivering training in digital and computational research tools to researchers across Australian universities.
Vu is a PhD candidate at the University of New South Wales (UNSW), where his research focuses on multimodal self-supervised learning for Earth observation using SAR and optical imagery. He completed a Master of Information Technology (AI, Data Science and Engineering) with Excellence at UNSW in 2023.
Alongside his research, Vu has experience in developing computational tools and delivering training in programming and digital workflows. Prior to pursuing his PhD, he worked as a Computational/Structural Engineer at Stantec, where he developed automation tools and delivered tutorials to support the adoption of digital workflows. He also mentored students at UNSW and led peer-learning Python sessions in industry.
Vu is currently a Digital Research Trainer at Intersect Australia and an intern in the 3AI team, where he delivers training in digital research tools and contributes to interdisciplinary applied AI research.
Beminate Lemma Seifu is a PhD candidate at Monash University, based at the Monash Centre for Health Research and Implementation. Her research focuses on developing and validating risk prediction models for gestational diabetes mellitus (GDM), with an emphasis on integrating Artificial intelligence to improve predictive accuracy and clinical applicability.
She holds a Master of Public Health in Biostatistics and has extensive experience in epidemiological research, advanced statistical modelling, and data science using R, Python, and STATA. Beminate has authored over 70 peer-reviewed publications in maternal and child health, reproductive health, and nutrition, contributing significantly to global health evidence, particularly in Sub-Saharan Africa.
Experience
Beminate also has strong academic teaching experience, having served as a Lecturer in Biostatistics and Assistant Lecturer in Public Health, where she taught undergraduate and postgraduate courses, supervised research students, and mentored future public health professionals.
Her work is driven by a commitment to methodological rigor, transparency, and the translation of data science into clinically meaningful insights for improving maternal health outcomes.
Yuhao is a PhD researcher at unimelb specializing in multimodal machine learning and robust learning under noisy labels, with a particular focus on healthcare data such as electronic health records. His research explores multimodal fusion, label noise modelling, and explainable AI for medical applications. Alongside his research, he teaches as a casual tutor in statistics and linear statistical models, and mentors students in data science and machine learning.
Academic Background: Prabhjot is currently pursuing a PhD in Quantum Machine Learning at the University of Melbourne. He holds a Master of Mathematics in Computer Science from the University of Waterloo (Canada), where his thesis focused on website fingerprinting on LEO satellite internet, and a Bachelor of Computer Engineering from Thapar Institute of Engineering and Technology (India).
Experience: Prabhjot is an interdisciplinary researcher with expertise spanning quantum machine learning, cybersecurity, and AI. He currently serves as a Sessional Academic at the University of Melbourne, where he tutors students in Distributed Systems and mentors data science projects. Previously, he worked as a Graduate Research Assistant at the University of Waterloo, developing machine learning frameworks for network security, and as a Consulting Engineer at Cisco Systems, where he designed and deployed network security solutions for enterprise clients. His research has been published at venues including NDSS, ACM WPES, and the IEEE Internet of Things Journal.
