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From Equations to Impact: Majid Sikosana Tackles Kidney Transplant Rejection

This June, Majid Lingani Ncube Sikosana is crossing the stage at convocation, celebrating the culmination of years of dedication to a complex problem in medicine: how to better predict kidney transplant rejection.

Hailing from Edmonton, ß÷ßäÉçÇø, Majid combined his passion for mathematics with a deeply human concern—the need for faster, more accurate diagnosis in transplant medicine. His thesis, A Probabilistic Approach to Kidney Transplant Biopsy Lesion Scores, tackled a critical question in clinical practice: how can we use math to quantify the risk of rejection seen in kidney biopsies?

“Quantifying certainty is not easy at all,” Majid reflects, a lesson he learned while building probabilistic equations to interpret biopsy data. These equations aim to help doctors make earlier and more confident decisions, potentially improving outcomes for kidney transplant patients.

His work earned him a spot at the 2024 American Society of Transplantation meeting in San Diego, where he presented his research among top minds in the field. Even more impressively, his findings have been published in the journal , marking a major achievement early in his academic career.

While Majid’s research is mathematically intricate, his motivation is simple: to help people. And the one thing he says he can’t live without? Family.

Congratulations, Majid—we can’t wait to see what equations you tackle next.