On 30 April 2025, Rajabbek Teshayev, PhD student in "Management" presented his research at the SOBE Research Colloquium titled "Improving Healthcare Management Tools in Uzbekistan Based on Foreign Experience." He highlighted the continued use of paper-based records in Uzbekistan’s healthcare system and proposed voice-to-text technology as a modern solution to improve efficiency. Drawing from international experience, on-site modelling, and experimental research, Teshayev demonstrated the potential of digital tools to ease administrative burdens and enhance patient care. The session concluded with engaging discussion on practical applications of the findings. Fruitful recommendations and feedback were provided to the presenter by experienced academic staff based on their experience and best international practices.

After several discussions, his dissertation was recommended to proceed to the next stage of defense/VIVA according to Supreme Attestation committee requirements.

On March 4, 2025, The Graduate School of Westminster International University in Tashkent (WIUT) conducted the Doctorate Admission Interviews.

Candidates who applied as Independent Researchers for the PhD and DSs programs were invited to participate in the next stage of the selection process following the successful evaluation of their applications. The shortlisted applicants were invited to present their proposed research projects related to Uzbekistan and the region. As part of the process, applicants delivered short presentations outlining their research objectives, methodology, and preliminary.

The interview stage is a key part of WIUT’s commitment to supporting high-quality, locally relevant research and building the next generation of scholars in Uzbekistan. Final decisions will be made following the interview stage, with successful candidates advancing to the formal admission process.

On April 9, 2025, Westminster International University in Tashkent (WIUT) hosted a research colloquium as part of the School of Business and Economics (SOBE) series, featuring Farrukh Ataev, a final-year PhD student in Econometrics and Statistics.

The presentation, titled “Total Factor Productivity Estimation: Evidence from the Great Silk Road Region,” explored the critical role of total factor productivity (TFP) in understanding economic growth and development.

Farrukh Ataev’s research highlights the efficiency with which inputs such as labor and capital are transformed into outputs, shedding light on the broader drivers of economic performance. Despite the strategic importance of the Silk Road region, comprehensive analyses of TFP in this area have remained limited—particularly within the context of Uzbekistan.

The presentation offered valuable insights for researchers and policymakers, enriching the academic discourse on productivity and its determinants in historically significant but under-researched regions. The session concluded with a thought-provoking discussion on the practical implications of improving productivity in the Silk Road economies.

After several discussions, his dissertation was recommended to proceed to the next stage of defense/VIVA according to Supreme Attestation committee requirements.

On February 12, 2025, the seminar, titled “Improving Econometric and Statistical Bases for Credit Risk Assessment in Credit Institutions,” was presented by Olmas Isakov, a final-year PhD student in Econometrics and Statistics and an independent researcher.

Drawing from his ongoing doctoral research, the presenter explored one of the most critical challenges faced by credit institutions—credit risk assessment. His presentation highlighted the importance of adopting advanced econometric and machine learning techniques to evaluate and predict non-performing loans (NPLs), a key indicator of financial vulnerability in lending institutions.

Isakov introduced a dynamic panel data approach to identify the determinants of NPLs and presented a machine learning workflow designed to predict loan delinquency in microfinance institutions (MFIs). The workflow included the application of eight supervised machine learning methods: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Classification Tree, Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting, and Neural Networks.

Participants gained valuable insights into how traditional statistical methods and cutting-edge machine learning tools can be integrated to improve credit risk models. The session concluded with an engaging discussion, where attendees explored the practical implications of the research and its potential applications in the financial sector.

After several discussions, his dissertation was recommended to proceed to the next stage of defense/VIVA according to Supreme Attestation committee requirements.