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Prof Maxim Finkelstein selected as Ewha Global Fellow

26 February 2025 | Story Martinette Brits | Photo Supplied

Prof Finkelstein_web read more edit — Prof Maxim Finkelstein, A1-rated researcher from the University of the Free State, has been selected as the 2024 - 2026 Ewha Global Fellow by Ewha Womans University.

An esteemed researcher from the University of the Free State (UFS), Prof Maxim Finkelstein, has been named a 2024 - 2026 Ewha Global Fellow (EGF) by Ewha Womans University in South Korea.

Prof Finkelstein, an A1-rated researcher from the Department of Mathematical Statistics and Actuarial Science, received this honour in recognition of his outstanding collaboration with Prof Ji Hwan Cha from Ewha’s Department of Statistics. Prof Cha nominated him as a leading expert in his field, highlighting their long-standing partnership and significant contributions to mathematical sciences.

According to Hyang-Sook Lee, President of the Ewha Womans University, the EGF programme “encourages distinguished scholars from all over the world to actively collaborate in research and education with Ewha faculty members.”

The genesis of a unique collaboration

Prof Finkelstein has collaborated extensively with researchers across Europe and the United States but his partnership with Prof Cha is particularly notable. “I started working at the UFS as a Professor in 1998 when he had just obtained his PhD,” recalls Prof Finkelstein.

At the time, Prof Finkelstein was already an established researcher, while Prof Cha was in the early stages. “His letter to me about one of my articles was sent to me by regular mail to my previous working address in Saint Petersburg, Russia, and did not reach me. We eventually connected around 2006, and our collaboration gradually took shape,” he explains.

Over the years, their partnership evolved into a balanced and mutually enriching research relationship. Their joint efforts have resulted in over 120 published papers and two books, setting new standards in the Mathematical Theory of Reliability and its applications. This collaboration has significantly influenced both their careers and contributed to Prof Finkelstein’s recognition with South Africa’s highest research accolades, including an NRF A1 rating in "Mathematical Sciences" in 2021, following his A2 rating in 2015.

A breakthrough in stochastic modelling

One of the major achievements of Prof Finkelstein's collaboration with Ewha has been their pioneering work in stochastic modelling. Their research led to the development of the Generalised Polya Process, a novel model for understanding natural and industrial point events - such as failures in electricity generation, lightning strikes, and hurricanes. By incorporating the ‘history’ of previous events, this model offers a more precise stochastic description of real-world phenomena.

The results of their research have been widely published and have paved the way for further exploration into more complex stochastic processes. Some of their key findings were summarised in the 2018 Springer book Point Processes for Reliability Analysis.

Looking ahead: Future collaboration and continued innovation

Despite being in the later years of his career, Prof Finkelstein remains deeply engaged in research and committed to his partnership with Ewha. Due to the challenges posed by the COVID-19 pandemic, his visits to Ewha were limited, but plans are now in place for future visits. During these visits, he will deliver lectures to students and collaborate with faculty members.

For Prof Finkelstein, continuing his nearly two-decade-long collaboration with Prof Cha remains a vital and exciting part of his academic journey.

News Archive

Mathematical methods used to detect and classify breast cancer masses

2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers.

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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