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Home » Distributed Artificial Intelligence Research (DAIR) Lab

Distributed Artificial Intelligence Research (DAIR) Lab

The Distributed Artificial Intelligence Research Lab directed by Professor Anita Raja, is concerned with the design and development of intelligent single and multi-agent systems. Lab members conduct research in distributed computing, convention formation, cascading risks, clinical informatics, monitoring and control of computation, complex networks, machine learning, resource-bounded reasoning, and reasoning under uncertainty.

  1. Advanced Machine Learning for Prediction and Prevention of Preterm Births

Our approach to preterm birth prevention brings to bear stochastic methods to derive accurate, multidimensional prevention models from large collections of observational data. These methods will support prevention relative to different stages of pregnancy. [Project Page]

Collaborators: Prof. Ansaf Salleb-Aouissi, Columbia University; Prof. Itsik Pe’er, Columbia University, Dr. Ron Wapner, Columbia University Medical Center, Dr. Cynthia Gyamfi,Columbia University Medical Center.

Students: Anton Goretsky, Adam Catto, Alisa Leshchenko

Funding Sponsors:

NIH 5R01 :

NIH 3R01:

NSF Eager Link:

2. Distributed Traffic Routing to Reduce Congestion

We develop distributed algorithms to enable connected vehicles to overcome the traffic congestion problem efficiently and cost-effectively. [More Info]

Collaborators: Prof. Ana Bazzan, UFGRS; Prof. Hasan, UNL,

Students: Yaroslava Shynkar, Marin Marinov, Nigel Ferrer

Funding Sponsor: PSC-CUNY Enhanced Grant

3. Software Engineering for Machine Learning

Collaborators: Prof. Raffi Khatchadourian

Students: Jack Kruse, Manal Zneit

4. Machine Learning for Finance

Collaborators: Prof. Alan Wolf

Students: Daniel Rozenzaft, Patrick Du

Click here for DAIR Lab Past Projects

DAIR Lab Sponsors (2003-Present)

Many thanks to the sponsors for their support of this lab’s work over the years:

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