Dean's Seminar Series: Professor Tina Eliassi-Rad
- 29 Aug 2019 12:00 pm - 1:30 pm
- Clayton Campus: S9 Lecture Theatre, 16 Rainforest Walk
Caulfield Campus: E.224, Building E
- Open to:
- Register here:
In his 1997 Machine Learning textbook, American computer scientist Tom Mitchell defined the well-posed learning problem as follows:
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
Associate Professor of Computer Science at Northeastern University in Boston, MA, Tina Eliassi-Rad, will expand on Tom Mitchell’s operational definition of Machine Learning while also delving into:
- Current tasks, experiences and performance measures as they pertain to the fairness of machine learning.
- New findings in terms of impossibility results.
- An alternative task definition whose goal is to provide more context to the human decision-maker.
- A null model for fairness and a demonstration of how to use deviations from this null model to measure and identify prejudices in the data.
Incase you've missed the seminar, here's the recorded version.
About Professor Tina Eliassi-Rad
Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern University's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison.
Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 80 peer-reviewed publications (including a few best paper and best paper runner-up awardees); and has given over 190 invited talks and 13 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning.
Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, she served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy. For more details, visit http://eliassi.org.
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