Inspired by biological reproduction, this research uses evolutionary algorithms to evolve mathematical equations that describe physical systems. Unlike black-box AI, these models are transparent and adaptable. By combining evolution with graph neural networks, the approach improves simulations for applications such as traffic control, robotics, and engineering design.

Partner choice increasingly reflects shared career aspirations, intensifying income inequality. Using Danish registry data and machine learning, this research shows assortative matching by education and career focus has risen since the 1980s. If pairing patterns had remained unchanged, today’s income inequality would be over 40% lower, highlighting family formation as a key economic force.

Lead contamination in drinking water threatens millions. This research combines physics-based pipe models with machine learning to identify lead pipes using vibration data. Generating thousands of simulated signals enabled a classifier with 99% accuracy, offering a noninvasive, cost-effective method to locate hidden lead pipes and support safer water infrastructure worldwide.

This research shows that genetic risk scores alone are insufficient for predicting chronic disease. By incorporating social and environmental factors using machine learning, disease prediction improves substantially, especially for disadvantaged populations. Integrating genetic and social risk is essential for equitable, effective personalized medicine.

This study looks at how to keep data safe in MongoDB, a type of database used by many businesses to store large amounts of information. As more companies use MongoDB, it becomes a target for hackers who may try to steal or delete important data. While there has been a lot of research into protecting traditional databases, there is less focus on databases like MongoDB. This study explores ways to detect and stop harmful activities in MongoDB, as well as how to recover deleted data. By analyzing the database’s logs, we can track and prevent unauthorized actions. The goal is to create a tool that helps protect databases from attacks like data theft or loss, and ensures data is recoverable if something goes wrong. This tool  will help businesses protect their data and recover it when necessary.

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.

This research investigates the neural “language” of vision, asking whether the brain encodes images using compositional or symbolic patterns. Using machine learning and artificial neural networks, the work reveals evidence for a compositional visual code, informing the future design of advanced visual prosthetics.

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.

This research explores swarms of small, modular robots that cooperate like ant colonies to perform complex tasks. Using control theory, optimization, and machine learning, the work enables resilient, energy-efficient robotic systems that adapt in real time, with applications ranging from disaster response and space exploration to medical technologies.

My research develops navigable high-altitude stratospheric balloons that combine satellite-level coverage with drone-level detail at low cost. Using machine-learning trajectory models and altitude-based steering, fleets can monitor wildfires, deforestation, and environmental change in real time. This technology enables scalable, sustainable remote sensing for global environmental protection.