Research
@
DCL Lab
@
DCL Lab
Our work focuses on distributed computing methods applied to scalable machine learning i.e. Distributed Computing for Machine Learning (DC4ML). We also employ machine learning techniques to scale distributed computing algorithms, particularly, shared-memory based concurrent data structures – Machine Learning for Distributed Computing (ML4DC). Check out the specific topics for more detail.
Federated Learning
Federated Learning, emerged as a special case of Distributed Machine Learning, is now an establis...
Distributed Machine Learning
Distributed machine learning (DML) utilizes multiple computers to collaboratively train a single ...
Learned Data Structures
Learned data structures use machine learning techniques to improve the performance of queries. Tr...
Concurrent Data Structures
Concurrent data structures are designed to handle simultaneous access and modification by multipl...