In 2026, many JMAI authors make outstanding contributions to our journal. Their articles published with us have received very well feedback in the field and stimulate a lot of discussions and new insights among the peers.
Hereby, we would like to highlight some of our outstanding authors who have been making immense efforts in their research fields, with a brief interview of their unique perspective and insightful view as authors.
Outstanding Authors (2026)
Sherry J. H. Feng, Auckland University of Technology, New Zealand
Maarten P. D. Schadd, Netherlands Organisation for Applied Scientific Research, The Netherlands
Cessie Valenie Anak Edwin, Universiti Malaysia Sarawak, Malaysia
Alexandros Sagkriotis, Real-World Evidence, UK
Outstanding Author
Sherry J. H. Feng
Dr. Sherry Feng’s journey in AI began with building a simple machine learning model for a user playing rock–paper–scissors against a “smart” computer - an experience that first revealed to her the transformative power of artificial intelligence. This early spark led her to pursue the field more deeply, eventually becoming one of the youngest AI consultants at the United Nations, where she applied AI to global challenges. She later pursued a PhD specialising in explainable large language models, with a focus on statistical and computational geometry. This work was driven by a strong belief that AI systems must be transparent and interpretable to be truly trustworthy. Since then, she has gained experience across academia, industry (including Amazon), startups, and government. Notably, she contributed to AI policy development by supporting the New Zealand Prime Minister’s Chief Science Advisor. Most recently, she founded CustodianAI, an AI startup commercialising her research in large language models.
In Dr. Feng’s view, a good academic paper is efficient and to the point - it respects the reader's time while delivering its contribution with clarity and precision. Beyond that, she believes that a strong paper provides sufficient resources and detail for others to replicate its findings, as reproducibility is fundamental to scientific integrity. It should be grounded in robust, supportive evidence, and perhaps most importantly, it should open doors rather than close them - inspiring discussion, inviting critique, and encouraging others to push the field further.
According to Dr. Feng, avoiding bias starts with a genuine effort to understand all perspectives surrounding a topic, even those that challenge your own position. She rigorously critiques ideas, including her own, and embraces intellectual humility in her writing. She reminds herself that her contributions are not the definitive answer, but rather part of an ongoing conversation.
Lastly, Dr. Feng would like to encourage other academic authors, “It is normal to feel like your research is stagnant; this feeling is more common than many admit. What I have come to appreciate is that sometimes discovering a way that does not work is just as valuable as finding one that does. Even shifting the needle slightly in a field is meaningful progress. Keep going! The work you are doing matters more than you realise.”
(by Sasa Zhu, Brad Li)
Maarten P. D. Schadd
Maarten Schadd is a scientist in the Intelligence & Decision Support department at TNO, specializing in Artificial Intelligence (AI) for decision support systems that enhance military planning processes. He holds both an MSc and a PhD in Artificial Intelligence and is an expert in Monte Carlo techniques and optimization algorithms. Building on his experience with complex interacting systems, he now leads research on advanced predictive tools for operational decision support, which incorporate the use of biophysical models alongside AI techniques. His recent work includes designing a hypoxia classifier and applying human metabolic models geospatially to support decision-making processes. He is passionate about bridging theoretical AI research with practical, real-world applications that improve operational effectiveness and safety.
Maarten regards a good academic paper as one that combines rigorous methodology with clear and compelling communication. In his field, a paper should present innovative solutions or approaches that advance understanding, whether it is a novel optimization algorithm, a machine learning approach, or the integration of AI with biophysical models. Data and evidence must be robust, reproducible, and transparent, allowing others to build upon the work. Equally important is relevance: the study should address a real-world problem or provide actionable insights for decision support. Clear structure, precise language, and well-justified conclusions are essential. In essence, a strong paper demonstrates originality, credibility, and practical significance while making complex ideas accessible to its audience.
In Maarten’s view, one of the biggest challenges is finding a clear structure and storyline for the article. It is important not to be afraid of restructuring the entire paper if it feels like it is not working. Repetition is often a clear signal that the flow needs adjustment. As authors write, they also start uncovering weaknesses in the methodology, which can sometimes require rerunning experiments or refining analyses. That is why thorough documentation of data, code, and procedures is essential. It ensures they can efficiently revisit experiments, reproduce results, and maintain the integrity of the research. Balancing clarity, structure, and methodological rigor is often the trickiest, yet most crucial, part of academic writing.
Maarten believes that academic writing offers the unique opportunity to share ideas and results with the broader scientific community and contribute to the advancement of knowledge. It allows authors to connect with other researchers who read and build upon their work, fostering collaboration and intellectual exchange. Equally important, it provides a structured way to document scientific foundation, which not only strengthens the credibility of current research but also supports future endeavors, such as project proposals or funding applications. In essence, academic writing is both a means of communication and a tool for establishing lasting scientific impact.
(by Sasa Zhu, Brad Li)
Cessie Valenie Anak Edwin
Cessie Valenie Anak Edwin is a postgraduate student at the Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak. She holds a degree in Software Engineering and is currently pursuing a master’s degree with a focus on artificial intelligence. Her research centers on the application of AI techniques in the context of bronchial asthma, with particular interest in predictive modeling and data-driven healthcare solutions. Recently, her work has involved developing machine learning models to assess asthma risk, improve early detection, and support asthma management. Through her studies and research, she aims to contribute to the advancement of intelligent healthcare systems that enhance patient outcomes and support clinical decision-making.
Cessie believes that a good academic paper effectively communicates knowledge with clarity, precision, and intellectual rigor. It should be structured logically and coherently, allowing readers to easily extract key information and understand the development of ideas. Beyond readability, a strong paper demonstrates methodological soundness, critical analysis, and a clear linkage between research questions, methods, results, and conclusions. Importantly, it should be accessible to its intended audience without oversimplifying complex concepts. Ultimately, a high-quality academic paper contributes meaningfully to the advancement of its field by offering new insights, validating existing knowledge, or opening pathways for further research.
Cessie highlights that a researcher must first recognize common biases to effectively mitigate them in writing. Examples of biases include confirmation bias, sampling bias, language bias, or analytical bias. Avoiding bias in academic writing requires conscious awareness of its sources and the disciplined application of objective, evidence-based practices. Bias often arises from personal beliefs, cultural assumptions, selective use of data, or methodological limitations. Peer review and feedback also play a critical role, as external perspectives can identify overlooked assumptions or interpretive distortions. Ultimately, avoiding bias involves maintaining intellectual honesty, grounding arguments in verifiable evidence, and presenting findings in a balanced and transparent manner.
“Research serves as the foundation for addressing complex and evolving challenges within society as they form the essential building blocks within the broader body of knowledge. Scientific progress is inherently cumulative; small, well-executed studies, when combined over time, generate significant impact and drive meaningful advancements in the field. To fellow academic writers, continued dedication, intellectual curiosity, and perseverance are vital. The process of research can be demanding, but each rigorous inquiry, validated finding, and thoughtfully articulated paper contributes to a larger, collective effort to expand understanding and improve real-world outcomes. In this sense, every contribution—no matter how small—plays a meaningful role in shaping the future of scientific discovery,” says Cessie.
(by Sasa Zhu, Brad Li)
Alexandros Sagkriotis
Alexandros Sagkriotis is a leader in Real-World Evidence (RWE) with more than 32 years of experience in the pharmaceutical industry. He is the founder of Helios Academy Ltd, an independent initiative focused on evidence strategy, methodological innovation, and leadership development—guided by the principle “Where Science Meets Compassion.” His work spans oncology, ophthalmology, dermatology, and data science, with a particular emphasis on integrating real-world data, artificial intelligence, and regulatory frameworks into coherent evidence architectures. He has contributed to advancing RWE through publications, policy engagement, and strategic advisory roles aligned with EMA, FDA, and HTA expectations. His recent work explores the transformation of registries, the role of AI in evidence generation, and the ethical dimensions of data use. Alongside his scientific work, he is an accredited coach, supporting leaders in navigating complexity with integrity and clarity.
In Alexandros’s view, academic writing is essential because it creates a structured, transparent, and reproducible foundation for knowledge generation. In healthcare, where decisions affect patient outcomes and societal trust, evidence must be communicated with rigour and accountability. However, academic writing should do more than document—it should challenge. Too often, progress is slowed not by lack of data, but by entrenched thinking. There are clear examples where fixed mindsets can dismiss emerging fields such as artificial intelligence in healthcare, not because evidence is lacking, but because it does not fit established narratives. This resistance can suppress curiosity, delay innovation, and ultimately limit the development of solutions that enable personalised medicine. Academic writing gives researchers the responsibility—and the platform—to question these limitations. It allows them to be explicit about what is not working, to push boundaries, and to ensure that scientific progress serves all patients, not just established systems.
According to Alexandros, an author in scientific research must combine methodological rigour with ethical responsibility. This includes clear research questions, an appropriate study design, and transparent reporting of limitations. Integrity is critical—authors must avoid overstating findings, resist publication bias, and ensure that conclusions are driven by evidence rather than expectation.
Beyond technical excellence, authorship is an act of leadership. It requires a willingness to share knowledge, to help others, and to move the needle in how science is understood and applied. Writing should trigger discussion, critical thinking, and, at times, uncomfortable self-reflection. This demands courage, but also respect and humility. Emotional intelligence is essential, as readers may recognize themselves in what is written. Authorship ultimately requires persistence, resilience, and a clear purpose—contributing to improved science, better decisions, and more equitable patient outcomes.
Alexandros stresses that data sharing is important, but it should not be treated as an absolute principle—it is a responsibility that must be exercised with care. When done properly, it enhances transparency, enables reproducibility, and accelerates scientific progress, particularly in fields such as real-world evidence and artificial intelligence. However, one of the biggest challenges is not only access to data, but also what authors choose to share. Too often, they publish what aligns with our narratives and underreport less favourable findings. This publication bias limits learning. They need a shift in mindset—from celebrating only successes to also valuing failures, as these are often the most informative. In this sense, the goal is not simply to share data, but to share honest evidence. This requires governance, context, and integrity to ensure that data serve science with integrity—and ultimately lead to better, more equitable patient outcomes.
(by Sasa Zhu, Brad Li)