Principal Research Scientist and Head of the AnyScale Learning For All (ALFA) Group, Computer Science and Artificial Intelligence Laboratory (CSAIL)
Una-May O’Reilly is a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory and heads the AnyScale Learning For All Group (ALFA). ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large-scale knowledge mining, prediction and analytics. ALFA conducts research in artificial adversarial intelligence related to cybersecurity, MOOC data analytics, and data-driven medical modeling. O’Reilly has expertise in agile data science systems with rapid intelligent data analytics capabilities. These systems span organization and visualization of raw data through to machine learning inference. She educates the forthcoming generation of data scientists, teaching them how develop state of art techniques that address the challenges spanning data integration to knowledge extraction.
O’Reilly earned a BS in computer science at the University of Calgary, and after three years at Bell Northern Research, earned an MCS and PhD in computer science at Carleton University. Her dissertation was one of the first to explore the artificial intelligence topic of genetic programming. She has published more than 100 research papers.
- Al-Dujaili, A. and O’Reilly, U.-M. (2020). Sign Bits Are All You Need for Black-Box Attacks. The International Conference on Learning Representations (ICLR).
- Ding, J.M., Hemberg, E., O’Reilly, U.-M. (2019). Transfer Learning using Representation Learning in Massive Online Open Courses. International Learning Analytics and Knowledge Conference.
- Kelly, J., DeLaus, M., Hemberg, E., O’Reilly, U.-M. (2019). Adversarially Adapting Deceptive Views and Reconnaissance Scans on a Software Defined Network. IEEE/IFIP International Workshop on Analytics for Network and Service Management (AnNet).
- Rusak, G., Al-Dujaili, A., O’Reilly, U.-M. (2018). AST-Based Deep Learning for Detecting Malicious PowerShell. ACM SIGSAC Conference on Computer & Communications Security