The following areas and topics are presently of interest in our group.

Informative  Optimization:

• Variational Information-theoretic Inference
• Manifold approaches to Quantify Uncertainty and Recursive Estimation (ManiQURE).
• Informative Neural Ensemble Kalman Learning
• Natural Scales in Hierarchical Uncertainty Quantification and Recursive Estimation
• Quantifying Uncertainty for Coherent Structures using Field Coalescence
• Learning to Predict Uncertainty using Dynamic Data Driven Deep Learning 
• Ensemble Learning in Non Gaussian Data Assimilation
• Quantifying Extreme Rare Event Tails (QuERET)
• Decisions with imprecise probabilities

Statistical Transport & Inference for Coherent Structures (stics.mit.edu):   

Fluids have coherent structures in turbulence. These features have always been used to describe fluids but they are rarely used to solve inference problems. We've wondered why that is. This area considers patterns emergent in coherent fluids as a means to efficient Inference. Here are some problems we have solved: 

• Data Assimilation by Field Alignment
• Scale Cascaded Alignment
• Quantifying Uncertainty for Coherent Structures using Field Coalescence
• Coherent Random Fields and Principal Appearance and Geometry Modes
• Field alignment for Blending Fields (FABle)
• Adaptive Reduced Order Modeling by Alignment (AROMA)
• Dynamically Deformable Sampling Plans
• Field Alignment System and Testbed: Publicly released, patented codes that exploit pattern information in data assimilation.

Dynamics and Optimization of Learning Systems  (DOLS)

• Dynamic Data Driven Deep Learning
• Informative Neural Ensemble Kalman Learning
• Informative Learning of Dynamics from Data
• Neural Networks and Geometric Chaos
• Stable Hybrid Dynamical Systems
• Stochastic Dynamics of Learning
• Learnability and Predictability
• Learning Sparse human relevance feedback strategies for high recall (sloop.mit.edu)
• Learning Machines for System Dynamics: Parameterization, Reduction, Uncertainty Quantification, Upscaling and Downscaling, Simulation
• Neural models of intensification and decay

Sensing and Imaging

• Tracking and Localization in Distributed Sensor Networks
• Distributed Sensor Network for Flood Monitoring
• Synthetic Aperture Fluid Imaging and Reconstruction
• Autonomous Remote and Insitu Measurement of Plume Composition using UAS

Planning and Control

• Markerless Augmented Reality,  
• Visual Servo Control
• View Point Control
• Ensemble Control
• Retrospective Cost Adaptive Control for Autopilots
• Glider Planning 
• Decisions with uncertain probabilities