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Academic Report on Research Collaboration Visit to the Missouri University of Science and Technology

Missouri University of Science and Technology, Host:

Prof. Sajal K. Das

Prof. Rodney Uphoff

University of the Western Cape Partners:

Prof. Antoine Bagula 

Mrs Lauren Arendse 

Introduction

In April 2025, Patrick Sello, a PhD student from the University of the Western Cape (UWC), undertook an academic research visit to the Missouri University of Science and Technology (Missouri S&T) as part of a collaborative initiative focused on federated learning and digital twin technologies. The primary objective of the trip was to strengthen ongoing research partnerships between UWC and Missouri S&T, explore joint opportunities for advancing machine learning methodologies, and initiate concrete academic outputs that reflect both theoretical innovation and practical application. This report provides an overview of the visit, highlights the collaborative processes that were established, and reflects on the outcomes achieved, particularly the initiation of two research papers addressing critical dimensions of digital and accountable artificial intelligence (AI).

The research trip was carefully planned in alignment with my broader doctoral and professional objectives, which place strong emphasis on bridging theoretical frameworks in machine learning with pressing real-world challenges in healthcare and cyber-physical systems. Missouri S&T provided an ideal setting for this work owing to its strong expertise in distributed systems, high-performance computing, and applied AI research. The trip was not only academically enriching but also strategically significant in positioning future collaborations on a global stage.

Objectives of the Visit

The trip was designed with several interrelated objectives:

1. Strengthening Research Collaboration – to deepen existing academic networks and establish working groups capable of advancing federated learning and digital twin research.

2. Knowledge Exchange – to share insights on ongoing projects in healthcare data governance, cyber-physical systems, and intelligent twins, while learning from Missouri S&T’s parallel research efforts.

3. Development of Joint Outputs – to conceptualise and initiate co-authored publications that address gaps in the discourse on explainability and accountability in AI.

4. Capacity Building – to enhance my own research skills through exposure to cutting-edge laboratories, seminars, and peer exchanges.

These objectives provided a structured framework that ensured the visit was purpose-driven and yielded measurable outcomes.

Activities Undertaken

During the stay, multiple academic engagements were undertaken that collectively contributed to the successful fulfilment of the objectives.

• Workshops and Seminars: I participated in a series of workshops hosted by Missouri S&T’s Computer Science Department. These sessions facilitated robust exchanges on technical challenges in federated learning, particularly around issues of non-independent and identically distributed (non-IID) data, frugal labelling, and model heterogeneity.

• Collaborative Meetings: Several structured meetings were convened with faculty and postgraduate researchers. These discussions enabled the alignment of research interests, the identification of overlapping expertise, and the design of joint study frameworks.

• Laboratory Visits: I engaged with Missouri S&T’s experimental environments for simulations. 

• Paper Development Sessions: Intensive writing and brainstorming sessions were organised, culminating in the drafting of two papers that focus respectively on Digital and Explainable AI and Accountable AI. These papers are designed to make significant contributions to the academic discourse and are targeted at reputable IEEE conference outlets.

Key Outcomes

The most tangible outcomes of the visit can be summarised as follows:

1. Initiation of Two Academic Papers:

• Digital and Explainable AI: This paper critically examines how digital twin frameworks can be enhanced by incorporating explainability features, ensuring that model outputs are interpretable to diverse stakeholders, including engineers, clinicians, and policymakers.

• Accountable AI: This paper interrogates the legal, ethical, and technical mechanisms through which AI models, particularly in federated settings, can be rendered accountable in both their design and deployment.

2. Strengthening Institutional Linkages: The trip established a clear pathway for continued research collaboration, including possibilities for joint supervision of doctoral students, shared access to datasets, and reciprocal visits between institutions.

3. Research Capacity Building: The visit provided me with valuable exposure to new methods for integrating federated learning pipelines with digital twin architectures, knowledge that will be instrumental in both my doctoral work and applied projects.

4. Global Visibility: By aligning my research trajectory with Missouri S&T’s international initiatives, the visit increased the visibility of our joint work within the broader AI research community.

Reflections

The success of the trip lies not only in its tangible academic outcomes but also in the spirit of collaboration and mutual respect that characterised every engagement. The Missouri S&T faculty and student community demonstrated exceptional openness, enabling the co-creation of knowledge across disciplinary and cultural boundaries. The trip underscored the importance of physical academic exchanges in an era often dominated by virtual interactions: while digital platforms enable continuity, in-person visits still provide unmatched opportunities for building trust, understanding nuances, and accelerating intellectual progress.

Secondly, the initiation of two research papers marks a critical step in my own academic development. The focus on explainability and accountability speaks to pressing global debates about the ethical deployment of AI, particularly in sensitive domains such as healthcare and municipal services. These themes resonate strongly with my broader research agenda, ensuring that the outputs of this trip are directly relevant and impactful.

Acknowledgements

I want to acknowledge the concerted efforts of all those who made this academic trip possible: Prof. Sajal, Prof. Bagula, Prof. Uphoff, Dr Anusha, Mrs Lauren, Mr Navid, Mr Arindam, and the rest of the UWC and Missouri S&T teams. The institutional support from Missouri S&T and UWC was invaluable, offering access to facilities, intellectual resources, and collegial networks. I am equally grateful for the support from my home institution and my supervisors, Prof. Bagula and Prof. Sajal, whose guidance shaped the focus of the visit and ensured its alignment with my doctoral research objectives. The collaboration of peers, research staff, and administrative teams on both sides must also be recognised; their logistical and intellectual contributions ensured that the visit was seamless and productive.

Conclusion

The April 2025 research collaboration visit to Missouri S&T was a resounding success. It achieved its primary objectives of fostering meaningful collaboration, strengthening institutional linkages, and producing concrete research outputs. The initiation of two academic papers on digital explainable AI and accountable AI stands as evidence of this success. Beyond publications, the trip deepened professional relationships, enhanced my research capacity, and provided a platform for future cross-institutional collaboration.

Looking ahead, the visit has laid the foundation for long-term research partnerships that will continue to explore the intersections of federated learning, digital twins, and responsible AI. These collaborations will not only advance academic knowledge but also contribute to practical solutions in sectors such as healthcare, municipal governance, and cyber-physical systems. The trip thus represents both a milestone in my academic journey and a launching pad for impactful future work.