Throughout my career, I have worked on data-driven solutions that pushed the boundaries for a variety of challenging and cross-product problems. My current focus is on trustworthy machine learning in the wild: not only proposing novel solutions to technical problems that ensure qualities such as fairness, transparency, privacy, and robustness, but also implementing them in real-world use cases. I deeply believe in the power of data to bring about positive changes in the world, and have collaborated with stakeholder organizations such as UNICEF Office of Innovation and Chicago Department of Public Health to devise solutions towards achieving these changes. Recently, I co-founded the Trustworthy ML Initiative as an effort to build a community and facilitating dialogue among researchers and practitioners in this field.