Mikhail Yurochkin

Research Staff Member

Mikhail is a Research Staff Member at IBM Research AI in Cambridge. His research interests include Bayesian nonparametrics and scalable Bayesian inference. Recently he has also been working on Optimal Transport and fairness in AI. Before IBM, Mikhail completed PhD in Statistics at the University of Michigan, advised by Prof. Long Nguyen. He received his bachelor degree in applied mathematics and physics from Moscow Institute of Physics and Technology.

Top Work

Topics are more meaningful than words. AI for comparative literature.

Topics are more meaningful than words. AI for comparative literature.

Natural Language Processing

Alleviating label switching with optimal transport

Alleviating label switching with optimal transport

Optimal Transport

Publications with the MIT-IBM Watson AI Lab

Black Loans Matter: Fighting Bias for AI Fairness in Lending
Black Loans Matter: Fighting Bias for AI Fairness in Lending
 
Continuous Regularized Wasserstein Barycenters
Continuous Regularized Wasserstein Barycenters
 
SenSR: the first practical algorithm for individual fairness
SenSR: the first practical algorithm for individual fairness
 
Layer-wise federated learning with FedMA
Layer-wise federated learning with FedMA
 
Topics are more meaningful than words. AI for comparative literature.
Topics are more meaningful than words. AI for comparative literature.
 
Using geometry to understand documents
Using geometry to understand documents
 
SPAHM: Parameter matching for model fusion
SPAHM: Parameter matching for model fusion
 
Alleviating label switching with optimal transport
Alleviating label switching with optimal transport
 
Bayesian Nonparametric Federated Learning of Neural Networks
Bayesian Nonparametric Federated Learning of Neural Networks