Aylin Caliskan is a Postdoctoral Research Associate and a CITP Fellow at Princeton University. Her research interests include fairness in machine learning, privacy, and security. Her work involves the heavy use of machine learning and natural language processing to characterize and quantify aspects of human behavior. Her research builds upon the key element of feature extraction for rigorous analysis of large-scale corpora and machine learning models. Her recent work on fairness, accountability, and transparency, particularly uncovering bias in language models, has received great attention upon the publication of "Semantics derived automatically from language corpora contain human-like biases" at Science. She continues investigating bias in joint visual-semantic models of artificial intelligence to explore their intersections with natural intelligence and society. Her doctoral research on the two main realms, privacy and security, combines machine learning with natural language processing. The applications of this research complement each other by enhancing security and preserving privacy. She demonstrated large-scale de-anonymization of programmers of source code and executable binaries. She also performed authorship attribution on authors of micro-text in social media and l33tsp34k in cyber criminal forums via stylometric analysis. Her joint work on semi-automated anonymization of writing received the Privacy Enhancing Technologies Symposium Best Paper Award. Aylin holds a PhD in Computer Science from Drexel University and a Master of Science in Robotics from the University of Pennsylvania.