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 is a machine learning approach that allows multiple parties to collaboratively...
Distributed Machine Learning
Distributed machine learning (DML) utilizes multiple computers to train a single machine learning...
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...
Real Time Graph Analytics
Given a dataset representing the entities and relations among them, performing analysis via repre...
Federated Neural Architecture Search
Given the requirements in terms of dataset and constraints in terms of computation budget, neural...