Dhanya Sridhar
Assistant Professor
Dhanya Sridhar
Assistant Professor
About
In brief, my research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently, and discover new knowledge alongside us. The topics I work on span causal representation learning to robust supervised prediction. I'm interested in both technical results and practical algorithms that work for data such as text, images, networks, or multiple modalities.
Research group:
Philippe Brouillard, co-supervised with Alexandre Drouin
Shruti Joshi
Mizu Nishikawa-Toomey, co-supervised with Laurent Charlin
Tom Marty
Cristian Manta, co-supervised with Yoshua Bengio
Sophia Gunluk
Navita Goyal, PhD student, University of Maryland, College Park
Maitreyi Swaroop, MSc student, Indian Institute of Technology, Kharagpur
Selected Publications
Heterogeneous Supervised Topic Models
Dhanya Sridhar, Hal Daumé, D. Blei
Transactions of the Association for Computational Linguistics, 2022
Elliot I. Layne, Dhanya Sridhar, Jason S. Hartford, M. Blanchette
2022
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, D. Blei
Transactions of Machine Learning Research, To Appear, 2022
View all
Courses
Causal Inference and ML
This course combines lectures and seminar-style discussions to cover the foundations of causality and topics like causal representation learning, causal structure discovery, causal abstraction (and its use in understanding large models).
