A central problem in machine learning is to learn from data (``big''...
Network Optimization & Control (NOC) Lab
Research in the NOC Lab deals with fundamental problems in the fields of optimization, control, stochastic systems, and data science. Current topics of interest include:
- Robust learning, with applications in many areas, particularly in computational biology problems involving protein modeling and metabolic networks, and computational medicine where it is of interest to develop predictive and prescriptive analytics for a variety of diseases and health conditions.
- Reinforcement learning, with primary applications in autonomous systems, seeking to develop new, robust, bio-inspired control and navigation policies.
- Networks, focusing on optimization and control aspects, where networks are broadly defined to include computer, communication, and sensor networks, supply chains, transportation networks, cyber-physical systems, networks of autonomous agents, social networks, economic networks, and biological networks (e.g., protein interaction networks).
- Machine learning and AI, from statistical learning theory to applied topics such as deep learning with applications in Natural Language Processing (NLP), applications in computational neuroscience, and processing of a variety of health-related datasets.
- Optimization, including many variants such as inverse, robust, distributed, integer, and on-line, with applications in a variety of domains.
If you are interested in joining the lab and are already at Boston University, contact us. If you are interested in applying to Boston University for graduate studies, please visit this informational page.