Doctorate Mahmudul Hasan: Graduate Research Teaching Fellow, Deakin University, Melbourne, Victoria, Australia

Mahmudul Hasan is a passionate and dedicated Ph.D. candidate in Information Technology at Deakin University, Australia. With a robust academic foundation in Machine Learning, Artificial Intelligence (AI), Blockchain, and Cybersecurity, Mahmudul focuses his research on enhancing the privacy and efficiency of Federated Learning by integrating blockchain technology. His work aims to bridge the gap between emerging technologies and real-world applications, particularly in healthcare and business intelligence. Alongside his research, Mahmudul has a rich teaching history, having conducted courses on cybersecurity, data management, and secure coding at Deakin. He also mentors global researchers through online platforms, inspiring the next generation of tech innovators. Mahmudul’s personal interest in wildlife photography and creating educational content on YouTube further highlights his well-rounded personality, combining technical excellence with creative expression.

Online Profiles

Google Scholar Profile

Mahmudul Hasan is an active presence in the academic and tech communities with profiles on GitHub, LinkedIn, and other professional platforms. On GitHub, he shares his personal projects that focus on cutting-edge research areas such as Federated Learning, AI, and Cybersecurity. These repositories showcase his work on blockchain systems, machine learning models, and other data-driven solutions. His LinkedIn profile reflects his journey from lecturer to a research fellow, showcasing collaborations with renowned scholars and institutions. Mahmudul also actively participates in global online education through his YouTube channel, where he has uploaded over 400 videos covering a variety of Computer Science topics. His channel has become a valuable resource for students worldwide, particularly those pursuing data science, programming, and AI courses.

Mahmudul’s research contributions are widely recognized in the academic community, with his work accumulating 477 citations and an h-index of 13 since 2020. His research explores innovative solutions in Federated Learning, Blockchain, and Machine Learning, with particular emphasis on privacy-preserving AI and cybersecurity. His work has been cited in several high-impact journals, contributing to the growing body of knowledge on distributed machine learning models and their applications in healthcare, business intelligence, and climate science. Mahmudul’s publications are not only influential but also demonstrate his ability to bridge theoretical concepts with practical applications.

Education

Mahmudul Hasan is currently enrolled in a Ph.D. program in Information Technology at Deakin University, Australia (2023–Present), where his research focuses on integrating Blockchain and Federated Learning to create more efficient and secure machine learning models. His doctoral supervisors include prominent figures like Professor Dr. Yong Xiang, Professor Dr. John Yearwood, and Dr. Md Palash Uddin. He holds a M.Sc. in Computer Science and Engineering from Hajee Mohammad Danesh Science and Technology University (Bangladesh), where he worked on developing a data balancing technique to address performance discrepancies in black-box machine learning models. He also holds a B.Sc. in Computer Science and Engineering from the same institution, where he worked on projects related to agricultural crop recommendation systems and exchange rate prediction using deep learning methods.

Research Focus

Mahmudul’s research primarily revolves around Federated Learning, Blockchain, Machine Learning (ML), and AI. His doctoral research focuses on the intersection of Blockchain and Federated Learning, specifically exploring how these technologies can be combined to create more secure, private, and efficient machine learning models. He is particularly interested in distributed learning systems where data privacy is a concern, such as in healthcare, business intelligence, and cybersecurity applications. His work also includes investigating explainable AI and machine learning interpretability, which are critical for making AI models more transparent and understandable. Additionally, Mahmudul has contributed to advancing cybersecurity through the development of new models to predict cyber threats and malicious behavior in complex network systems.

Experience

Throughout his academic career, Mahmudul Hasan has built an impressive portfolio in both teaching and research. He is currently a Graduate Research Teaching Fellow at Deakin University, where he leads courses in Cybersecurity Analytics and Data Management. He has previously served as Casual Academic staff at Deakin, teaching courses such as Secure Coding and Data and Information Management. Beyond teaching, Mahmudul has extensive experience as a Research Assistant, working on projects in areas like computer vision, business intelligence, and data science. He has collaborated on several international research projects, including studies on AI-powered healthcare solutions and machine learning-based recommendation systems. His ability to work across different research domains has allowed him to bridge gaps between theory and practice, contributing significantly to the global research community.

Research Timeline

  • 2021–Present: Principal Investigator at CeMRD (Center for Multidisciplinary Research and Development), where Mahmudul leads a team of over 40 researchers on interdisciplinary projects related to AI, data science, and machine learning. Over 12 research papers have been published under his leadership.

  • 2022–2023: He served as a Research Fellow at the Ministry of Science & Technology, Bangladesh, focusing on carbon emission prediction using ensemble machine learning techniques. This project had a substantial impact on understanding the relationship between climate change and carbon emissions.

  • 2020–2021: Mahmudul worked as a Research Assistant in a project on masked face recognition systems, contributing to AI security and data privacy. Additionally, he was part of a multidisciplinary team at Swansea University that explored big data applications in financial innovation.

Awards & Honors

Mahmudul’s academic and professional achievements have earned him numerous awards and recognition. Among these, the Australian Research Council Grant Funded Scholarship (2023) stands out, acknowledging his potential in groundbreaking research. He was also awarded the NST Fellowship by the Ministry of Science & Technology, Bangladesh (2022-2023) to support his research in AI and machine learning. He received the IEEE Best Paper Award (2020) for his work on machine learning models in cybersecurity. Additionally, Mahmudul was awarded the Deakin University Top 10 Presentation Award (2024) for his outstanding contribution to the HDR Annual Conference. His consistent academic excellence has been further recognized with the CSE Dean’s Award (2019) and multiple Intra University Programming Contest wins.

Top-Noted Publications

Mahmudul has authored several influential publications in the fields of machine learning, cybersecurity, and AI. Some of his most cited works include:

  • “A Systematic Literature Review of Robust Federated Learning” in ACM Computing Surveys (2025), which offers an in-depth analysis of current Federated Learning research.

  • “Interpretable AI for Cervical Cancer Risk Analysis” in Digital Health (2025), a significant contribution to health informatics.

  • “Exploring Happiness Factors with Explainable Ensemble Learning” in PLOS ONE (2025), which applies ensemble learning techniques to analyze mental health data.
    His work is highly regarded in the academic community for its contribution to solving real-world problems using advanced machine learning models and explainable AI techniques.

  • Top-Noted Publications

    1. Deep Learning-Based Exchange Rate Prediction During the COVID-19 Pandemic
      MZ Abedin, MH Moon, MK Hassan, P Hajek
      Annals of Operations Research, 345 (2), 1335-1386
      Cited by 116 (2025)
      This study explores how deep learning models were employed to predict exchange rates during the COVID-19 pandemic, revealing key financial insights.

    2. Ensemble Machine Learning-Based Recommendation System for Effective Prediction of Suitable Agricultural Crop Cultivation
      M Hasan, MA Marjan, MP Uddin, M Ibn Afjal, S Kadry, S Ma, Y Nam
      Frontiers in Plant Science, 14, 1234555
      Cited by 67 (2023)
      This paper highlights an ensemble machine learning model aimed at predicting the most suitable crops for cultivation, with significant implications for sustainable agricultural practices.

    3. Effect of Imbalance Data Handling Techniques to Improve the Accuracy of Heart Disease Prediction Using Machine Learning and Deep Learning
      MA Sahid, M Hasan, N Akter, MMR Tareq
      2022 IEEE Region 10 Symposium (TENSYMP), 1-6
      Cited by 29 (2022)
      This paper addresses the issue of data imbalance in heart disease prediction, proposing techniques to enhance model accuracy.

    4. Advancing Reservoirs Water Quality Parameters Estimation Using Sentinel-2 and Landsat-8 Satellite Data with Machine Learning Approaches
      M Mamun, M Hasan, KG An
      Ecological Informatics, 81, 102608
      Cited by 23 (2024)
      This study leverages machine learning models with satellite data to assess and improve the estimation of water quality parameters in reservoirs.

    5. A Blending Ensemble Learning Model for Crude Oil Price Forecasting
      M Hasan, MZ Abedin, P Hajek, K Coussement, MN Sultan, B Lucey
      Annals of Operations Research, 1-31
      Cited by 23 (2024)
      This research proposes an ensemble learning approach for forecasting crude oil prices, providing key insights into the global energy market.

Top Strengths for the Best Innovator Award:

Timely & Impactful Applications

  • Relevance to Current Global Issues: Each of the studies addresses some of the most pressing global challenges. From the economic instability caused by COVID-19 and fluctuations in oil prices to sustainable agriculture and healthcare, these papers tackle issues that affect millions of people worldwide.

  • Practical Solutions: These applications aren’t just theoretical—they have real-world implications. The ability to provide innovative solutions in financial forecasting, agriculture, public health, and environmental management shows a unique ability to respond to immediate needs.

2. Sophisticated and Cutting-Edge Techniques

  • State-of-the-Art AI/ML Methods: The use of deep learning and ensemble methods demonstrates an advanced level of technical expertise. These techniques allow for highly accurate, efficient, and scalable solutions to complex problems, setting these papers apart from more traditional approaches.

  • Innovation in Application: By employing these sophisticated techniques in diverse fields like finance, agriculture, healthcare, and environmental science, the research shows creativity in applying modern AI methodologies to a wide array of challenges, often in areas where AI hasn’t been widely explored.

3. Interdisciplinary Expertise

  • Cross-Disciplinary Knowledge: The studies span several diverse fields—finance, agriculture, healthcare, and environmental science—demonstrating the ability to bridge gaps between technology and domain-specific knowledge.

  • Broad Applicability of Research: This interdisciplinary approach speaks volumes about the researcher’s versatility and adaptability. The ability to apply machine learning and AI in such varied contexts suggests a deep understanding of both the technical aspects and the real-world challenges in these fields.

4. Global and Societal Impact

  • Significant Contribution to Society: Each of these research papers isn’t just advancing knowledge; it’s creating tangible benefits for society at large. Whether it’s improving healthcare outcomes, driving economic stability, enhancing food security, or preserving the environment, the impact is vast and measurable.

  • Sustainability and Well-Being: Many of these studies also focus on sustainability (agriculture, water quality, etc.), highlighting a commitment to solutions that support long-term societal well-being and environmental health. This makes the research not just innovative, but ethically aligned with pressing global needs.

5. High Citation & Recognition

Academic Influence: The high citation count reflects the importance of the research in the academic community. It shows that the work is not just innovative but also widely recognized and used by others in the field, amplifying its influence and encouraging further advancements.

Impact Beyond Academia: The recognition of these papers also indicates that the research has practical, real-world applications that have resonated with both scholars and industry professionals, reinforcing the relevance and scalability of the innovations.

Mahmudul Hasan, Computer Science, Best Innovator Award