As a Junior Professor in the School of Physical Sciences and Mathematics at University of San Carlos of Guatemala (USAC), Dr María Eugenia Cabrera’s Catalán researches the phenomenology of physics. Her work builds on the discovery of the Higgs boson particle in 2012, expanding the knowledge of what we know about the Standard Model of particle physics and the fundamental forces of nature.

María studies dark matter, the most abundant component of matter in our universe, known only by its gravitational interactions and key to developing a more fundamental theory of nature. She focuses on the interaction between newly discovered particles such as the Higgs boson; understanding these dynamics can help scientists understand the interactions that could be expected between massive weakly coupled dark matter particles in particle collider experiments, or seen by underground detectors that attempt to measure how dark matter interacts with the Earth.

María first became interested in physics in secondary school, thanks to the inspiration of her physics teacher. She majored in physics at USAC, then received a scholarship to pursue a postgraduate diploma at the Abdus Salam International Centre for Theo­retical Physics (ICTP) in Italy. There, she became fascinat­ed by high energy physics and made this the focus of her PhD research at the Institute for Theo­retical Physics in Madrid. She graduated Cum Laude in 2011 with the distinction of Doctor Europeus.

Following a postdoctoral position at the GRAPPA Institute at the University of Amsterdam, during a second postdoc at the Institute of Physics at the University of Sao Paolo in 2016, María learned that her alma mater USAC was opening permanent research positions in Physics and Mathematics for the first time, with 75 percent of the time available for research. Six months later she accepted one of these positions. Now, she is working to put her country and region on the world physics map, having helped to organize the first Central American Meeting of High Energy Physics, Cosmology and High Energy Astrophysics in 2020, and dedicating much of her time to supervising and tutoring undergraduate students in particle physics.

Dr Khongorzul Dorjgotov is a mathematician whose research focuses on fractional differential equations (FDEs), an emerging field in applied and theoretical mathematics with many applications in science and engineering. FDEs have been recognized as an excellent tool for describing complex systems and processes in many applied sciences, including physics, chemistry, biology and economics. The study of these types of equations is becoming increasingly popular as they can more accurately model a given physical system or process than conventional differential equations.

Khongorzul began studying applied mathematics as an undergraduate student at the National University of Mongolia (NUM), where she won an award for best research in a student competition. She went on to do her master’s degree in the same subject, before winning a MEXT scholarship from the Japanese government for doctoral study at Kyushu University in Japan. It was there that she first started her research on FDEs, focusing on Lie theory and the symmetry approach. During the final year of her PhD, she out-competed many international students  to win Best Poster at the Forum “Math-for-Industry” 2017. After completing her PhD, she returned to Mongolia, becoming one of the few women with a mathematics PhD in the country. She began working as a lecturer at NUM in 2018.

Now, Khongorzul is working to establish the study of fractional calculus in Mongolia. In addition to serving as senior lecturer and supervising graduate students, she is working to increase collaboration between the industrial and academic sectors. To this end, she recently organized, with fellow researchers, the first Mongolian “Study Group” workshops, an internationally recognized method of technology and knowledge transfer between academic mathematicians and industrial sectors. Khongorzul hopes that not only the industrial representatives but other decision-makers will be able to apply the results of mathematical research to do their jobs more effectively.

Halima Twabi is a biostatistician from Malawi who employs advanced statistical methodologies and data-driven approaches to understand health issues. Through these approaches, her work identifies factors contributing to health disparities among vulnerable populations such as women and children and assesses intervention effectiveness, especially those affected by HIV infection, in socio-economically disadvantaged communities. Her previous work has examined the effects of maternal HIV on adverse pregnancy outcomes, such as low birth weight and perinatal mortality. Her research has also utilized innovative statistical methods to investigate the influence of unhealthy food consumption on childhood overweight, a growing challenge in sub-Saharan Africa. In 2023, she was named a Young Affiliate of The World Academy of Sciences and received the 2020 L’Oréal-UNESCO Young Talent Award for Sub-Saharan Africa. She is a fellow of the DELTAS Sub-Saharan Africa Consortium on the Advancement of Biostatistics (SSACAB) program and a founding member of the Girls in Science, Technology, Engineering, Arts, and Mathematics (Gi-STEAM) network. She currently serves as the Treasurer for both the International Biometric Society’s Sub-Saharan Africa Network (IBS-SUSAN) and its Malawi region. She is currently collaborating on a project ‘Strengthening Women’s Research Network and Capacity to Address Women’s Health in Sub-Saharan Africa’ project, funded by the Bill and Melinda Gates Foundation’s Global Grand Challenges as a country lead for Malawi.

Paul is a now an Emeritus Reader in Medical Statistics at King’s College London, where he has worked in public health science and maternal and children’ health research since 1996. He moved into medical research after a career teaching mathematics and statistics and gained an MSc in Medical Statistics from LSHTM. He has extensive expertise in study design, sample size calculation, diagnostic and screening methods, growth data and measurement, and statistical analysis of maternal and pregnancy outcomes, including postpartum haemorrhage, preterm birth, pre-eclampsia and fetal growth.