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

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)