This research uses immune cell “molecular fingerprints” to rapidly detect cancer from a single drop of blood. By combining nanosensors and machine learning, subtle changes in B cells can be identified within minutes. The approach offers fast, accurate, and low-cost cancer detection with the potential to significantly improve early diagnosis and survival.

Antibiotic resistance threatens to return medicine to a pre-antibiotic era. This research uses machine learning to study how bacteria balance resistance to antibiotics and bacteriophages. By revealing genetic trade-offs between attack and defense, the work enables smarter combination therapies that exploit bacterial weaknesses and prevent otherwise deadly infections.

The speaker develops RADARS, a programmable RNA-guided gene-delivery system that activates only in cells with specific RNA “fingerprints.” Their thesis tackles weak activation when target RNA is rare, creating new mechanisms to bind targets more tightly. These innovations aim to enable safer, cell-specific cancer therapies through precise molecular control.

This project develops an “Aptamer Express,” a DNA-based Trojan horse designed to bypass the brain’s protective barriers, target tumours, and deliver cancer-killing drugs directly to brain cancer cells. The approach aims to overcome treatment resistance, improve precision, and reduce side effects, offering new hope for patients and their families.

This research targets the earliest stage of allergic and asthmatic immune reactions by blocking key cytokine “messages” sent from T cells to B cells. Using drug-discovery techniques, the project identifies compounds that prevent immune overreaction before symptoms begin, aiming to develop a new class of long-lasting preventative allergy and asthma treatments.