Ina’s research centers around how animal behavior is regulated throughout development and in response to experience and environment. The current focus of her research group at the University of Toronto is understanding the role of epigenetic and epitranscriptomic gene regulation in development, behavior, and health and disease. The interaction between genes and an experienced environment shapes how individuals differ in their behavior. Ina’s work aims to explain the molecular pathways through which the environment interacts with genes, the long-term effects of these interactions, and differences between individuals by combining classical genetics, molecular biology, transcriptomics, behavioral assays, and bioinformatics.
Megan is interested in understanding biological structures and the inverse design of artificial nanomachines. Megan is currently using tools developed in the context of training neural networks to help usher in a new paradigm in molecular dynamics simulation to answer how biomolecules have evolved to capitalize on their non-equilibrium environments at Harvard University. By applying automatic differentiation and state-of-the-art optimizers to full simulations of biomolecules, she is working to improve DNA and protein models by systematically ‘training’ their parameters and exploring optimal molecular designs.
Andreas pivoted his research from tropical weather forecasting to agricultural forecasts using satellite imagery at the Stanford AI Lab. With the aim to alleviate food insecurity in Africa, he tested and developed different machine learning models to improve the prediction of crop yields and the mapping of crop types in Africa. As the continent largely lacks sufficient data to train these data-hungry models, he was also involved in a data collaboration effort in Tanzania. This campaign was jointly implemented with researchers from the University of Maryland and the startup Flamingoo Foods, which Andreas co-founded several years ago. Andreas believes that the current technological revolutions in the field of AI will be crucial to improve the resilience of food supply chains around the world and contribute to food security.
Mercy Nyamewaa Asiedu
Mercy is interested in the intersection of global health equity, machine learning, and medical devices. Growing up in Ghana, Mercy’s personal experiences fed her passion for using culturally appropriate technology to democratize health. During her PhD, she developed devices and algorithms for cervical cancer screening before pivoting her science to develop novel deep learning algorithms to improve image quality for portable, point-of-care ultrasound devices. Mercy has co-founded two companies, one to commercialize her cervical cancer screening device and the other to develop patient-centered mobile health applications for collecting health data and increasing access to healthcare. Mercy’s research interests lie in developing fair and robust algorithms for equitable health in underrepresented environments.