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Google Brain, |
I am a research scientist at Google Brain, working broadly in the area of machine learning and artificial intelligence. Before Google, I was at IBM TJ Watson Research Center in Yorktown Heights, NY. I graduated with a Ph.D. from Department of Computer Science at University of Maryland where my advisor was Hal Daumé III. I have also worked on robust automatic speech recognition during my Masters at CRSS, advised by John H. L. Hansen.
Recent News (more)
Our work on implicit regularization in β-VAEs to appear at ICML’20.
I am serving as an area chair for NeurIPS’20.
Our work on injective flow models to appear at AISTATS’20.
Our work on guaranteed disentanglement with weak supervision to appear at ICLR’20.
I am serving as an area chair for ICLR’20.
Our work on adaptive fine-tuning for transfer learning to appear at CVPR’19.
I am serving as an area chair for ICLR’19.
Our work on scale-invariant flatness measures for loss surfaces is out.
I joined Google Brain as a research scientst.
Two papers appearing in NeurIPS’19 (Unsupervised domain adaptation, Delta-encoder for few-shot learning).
I am co-organizing a workshop Towards learning with limited labels: Equivariance, Invariance, and Beyond at ICML/FAIM 2018.