Office: GHC 8205
Email: ninamf AT cs DOT cmu DOT edu
Research My main research interests are in
machine learning, artificial intelligence, and theoretical computer
Current research focus includes:
Developing foundations and principled, practical algorithms
for important modern learning paradigms. These include
interactive learning, distributed learning, learning
representations, life-long learning, and metalearning. My
research addresses important challenges of these settings,
including statistical efficiency, computational efficiency,
noise tolerance, limited supervision or interaction, privacy,
low communication, and incentives.
Foundations and applications of data driven algorithm design.
Design and analysis of algorithms on realistic instances (a.k.a.
beyond worst case).
Computational and data-driven approaches in game theory and
Computational, learning theoretic, and game theoretic aspects
of multi-agent systems. Analyzing the overall behavior of
complex systems in which multiple agents with limited
information are adapting their behavior based on past
experience, both in social and engineered systems contexts.
For more information see the links below and my resume.
Current and Recent Selected
Program Committee Co-chair for ICML 2016 (all talks
Program Committee Co-chair for COLT
2014 (all talks available here).
Tutorial Chair for ICML 2019. Workshop Chair for FOCS
2019 and 2020.