NNML
The Neural Networks and Machine Learning lab specializes in:
- Predictive Modeling
- Automated Learning
- Neural Networks
- Data Analysis for Knowledge Discovery
- Pattern/Audio/Facial Recognition
- Path Planning
DML
The Dating Mining Lab is interested in improving and applying data mining in many different areas. We are currently engaged in major research projects in the following areas:
- Computational Health Science
- Metalearning
- Social Capital
PCC
The Perception, Control and Cognition laboratory blends deep neural networks with Bayesian models to create flexible, scalable inference algorithms that can be trained on input from the natural world. Specific focus areas include probabilistic programming, natural language processing, integrated cognitive systems, dynamical systems modeling, robotics, automated decision-making and optimal control.
MIND
The Machine INtelligence and Discovery lab focuses on creating artificial intelligent systems that incorporate robustness, adaptation and creativity in their approaches to problem solving and incorporates neural models, machine learning techniques, and evolutionary computation.
AML
The Applied Machine Learning Laboratory focuses on increasing the practicality of theoretical machine learning algorithms, especially as applied to autonomous robotic systems. We concentrate on making reinforcement learning techniques applicable to large problems, and on making it fast enough for on-line use. The lab is currently pursuing research projects in areas such as knowledge transfer, skill selection, skill composition, function approximation, multi-agent decomposition, and adversarial multi-agent learning. Q-learning, suitable function approximation such as RBF and back-propagation networks, and induced abstract models are the primary methodologies we use to solve problems.