Spring 2017 Projects with the Smart-X {Cities, Buildings, Grids} Group, CCLS

  1. Intelligent Building Management
    We are re-basing our Total Property Optimization software on an agent based system called BEMOSS. Looking for ML based Time Series forecasting and SCADA experience. Desire to use deep (reinforcement) learning to redo the models.
  2. Causal Inference for Treatments of Con Ed assets
    We are studying effectiveness of several inspection/maintenance programs on the Con Ed secondary grid using casual inference. Looking for statistics and causal inference experience.
  3. Fast Dynamic Programing via Quantum Computing
    We are studying ways to use quantum computing to speed up dynamic programing/reinforcement learning. Pursuing recent developments in the paper Reinforcement Learning Using Quantum Boltzmann Machines from 1QBit and using D-Wave hardware. Looking for people knowledgeable in quantum computing and (deep) reinforcement learning.
  4. Using Reinforcement Learning for Threat Minimization
    Idea is to use Approximate Dynamic Programing/Reinforcement Learning to learn policies that mitigate threats to systems. Threats could be cyberattacks or climate change threats, etc. Looking for people knowledgeable in reinforcement learning and simulation of complex systems.
  5. Machine Learning based human-in-the-loop alarm fatigue reduction
    Research of false alarms in intensive care units (ICUs) can reduced by building ML models of system/patient based on electronic health records, monitor data, and physiological data. We have data from a neurological ICU. Looking for students with interest in ML methods for unbalanced data, and false positive minimization.
  6. Evaluating Lumberyard: Time Series Indexing at Scale
    project dealing with a way to compress, index and create features from high frequency time series data with a Symbolic Aggregation method based on iSax.  An implementation called Lumberyard was done which will be the starting point and the code base for Lumberyard is here.  We may look at phase measurements on electrical grids as in the original application, or epilepsy data. We are interested in blending this with deep learning.

Contact:
Albert Boulanger
Center for Computational Learning Systems
Interchurch
475 Riverside Dr, Suite 850
[email protected]
212 870 1276

Directions to where we are. You need to sign in downstairs.   We are at the easternmost end of the suite, past the kitchen.