Ph.D. in Machine Learning,
Machine Learning Department,
School of Computer Science,
Carnegie Mellon University, Pittsburgh, PA.
Data mining and machine learning, with emphasis on mining and modeling large networks, link analysis and text mining.
Specialties: Artificial intelligence, machine learning, data mining, social networks, text and web mining, mining large graphs and networks, applications of machine learning and data mining.
Talks and Events
2022 Keynote: Deep learning with Knowledge Graphs
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems. In this talk I will discuss recent methodological advancements that automatically learn to encode graph structure into low-dimensional embeddings. I will also discuss industrial applications, software frameworks, benchmarks, and challenges with scaling-up graph learning systems.