Professor Anna Choromanska did her Post-Doctoral studies in the Computer Science Department at Courant Institute of Mathematical Sciences in NYU and joined the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering in Spring 2017 as an Assistant Professor. She is affiliated with the NYU Center for Data Science.
Prof. Choromanska’s research interests focus on machine learning both theoretical and applicable to the variety of real-life phenomena. Currently, her main research projects focus on numerical optimization, deep learning, large data analysis, and learning from data streams. Prof. Choromanska also works on machine learning for robotics and autonomous systems. She collaborates with NVIDIA (New Jersey lab) on the autonomous car driving project.
Prof. Choromanska was a recipient of The Fu Foundation School of Engineering and Applied Science Presidential Fellowship at Columbia University in the City of New York. She co-authored several international conference papers and refereed journal publications, as well as book chapters. The results her works are used in production by Facebook (training production vision systems and entry to COCO competition) and Baidu, and in product development by NVIDIA. She is also a contributor to the open source fast out-of-core learning system Vowpal Wabbit (aka VW). Prof. Choromanska gave over 50 invited and conference talks and serves as a book editor (MIT Press volume), organizer of top machine learning events (workshops at conferences such as the International Conference on Neural Information Processing Systems), and a reviewer and area chair for several top machine learning conferences and journals.
Upcoming Abstract Summary
Data-driven challenges in AI: scale, information selection, and safety:
The talk will focus on data-driven challenges in AI. First, the talk will focus on scaling algorithms to massive data sets. The multi-class and multi-label classification problems will be addressed, where the number of classes (k) is extremely large, with the goal of obtaining train and test time complexity logarithmic in the number of classes. A reduction of these problems to a set of binary classification problems organized in a tree structure will be discussed. A extensions to deep learning will be provided. Second, the talk will consider a problem of information selection for efficient inference in the context of autonomous platforms equipped with multiple perception sensors. Third, the talk will address safety issues in modern AI systems and develop GAN-based on-line monitoring framework for continuous real-time safety/security in learning-based control systems dedicated to autonomous vehicles.