Aylin Caliskan is an assistant professor of computer science at George Washington University. Her research interests include the emerging science of bias in machine learning, fairness in artificial intelligence, data privacy, and security. Her work aims to characterize and quantify aspects of natural and artificial intelligence using a multitude of machine learning and language processing techniques. In her recent publication in Science, she demonstrated how semantics derived from language corpora contain human-like biases. In addition, she developed novel privacy attacks to de-anonymize programmers using code stylometry. Her presentations on both de-anonymization and bias in machine learning are the recipients of best talk awards. Her work on semi-automated anonymization of writing style furthermore received the Privacy Enhancing Technologies Symposium Best Paper Award. Her research has received extensive press coverage across the globe. Aylin holds a PhD in Computer Science from Drexel University and a Master of Science in Robotics from the University of Pennsylvania. She has previously spoken at 29C3, 31C3, 32C3, and 33C3. @aylin_cim