I use computers and artificial intelligence (AI) to rapidly increase how quickly we can discover and develop new medicines. I use technology to create new approaches that many different scientists can use in their own research to do things like look at medical data in fresh ways or predict who is likely to get sick in different populations. Some of my research focuses on ensuring that this work is done inclusively and equitably, meaning a wide range of populations are fairly represented throughout the process. I also find ways to make these tools as inexpensive and easy-to-use as possible, so that people all over the world who may not have access to lab equipment can participate in and enhance the process of discovery.

I use and further develop cutting-edge tools like AI and machine learning, and apply these techniques to biological questions and data. I write code in programming languages such as R and Python and produce new programs or techniques that other people can use as well. I also partner closely with collaborators in different disciplines like medicine, engineering, pharmacology, and chemistry, to bridge gaps and find ways to improve our processes. I sometimes collaborate with communities or analyze techniques to ensure that the technologies we’re using are fair – for example, studying ways to ensure that genetic datasets include a wide range of diverse ethnicities.

Understanding diseases and developing new medicines to treat them has historically been an extremely slow, expensive, and difficult process that has been led by relatively few people. By using new technologies to make that process faster, easier, and available to a much wider range of scientists across fields, we can massively increase the rate of life-saving discoveries. And by finding ways to prioritize diverse data sets, support inclusive leadership, and unlock these technologies by sharing them broadly with people across the globe, we can invite and uplift new perspectives that could benefit everyone.