Aylin Caliskan

- Title:
- Assistant Professor
- Office:
- SEH 4590 | Office Hours: by appointment
- Phone:
- 202-994-5919
- Email:
- [email protected]
- Website:
- seas.gwu.edu/~aylin
Aylin Caliskan is an assistant professor of computer science at The 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. Prior to that, 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. Before joining the faculty at The George Washington University, she was a postdoctoral researcher and a fellow at Princeton University's Center for Information Technology Policy.
- Ph.D., Computer Science, Drexel University
- M.S., Robotics, University of Pennsylvania
- Machine learning
- Bias and fairness in machine learning
- AI ethics
- Privacy & Security
- Data Science
- Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. Semantics derived automatically from language corpora contain human-like biases. Science 2017
- Aylin Caliskan-Islam, Richard Harang, Andrew Liu, Arvind Narayanan, Clare Voss, Fabian Yamaguchi, and Rachel Greenstadt. De-anonymizing Programmers via Code Stylometry. 24th Usenix Security Symposium (Usenix 2015)
- Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard Harang, Konrad Rieck, Rachel Greenstadt, and Arvind Narayanan. When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries. The Network and Distributed System Security Symposium (NDSS 2018)