Portrait of Smita Krishnaswamy

Smita Krishnaswamy

Affiliate Member
smita.krishnaswamy@mila.quebec
Associate Professor, Yale University
Université de Montréal
Yale
Research Topics
AI in Health
Brain-computer Interfaces
Cognitive Science
Computational Biology
Computational Neuroscience
Data Geometry
Data Science
Data Sparsity
Deep Learning
Dynamical Systems
Generative Models
Geometric Deep Learning
Graph Neural Networks
Information Theory
Manifold Learning
Molecular Modeling
Representation Learning
Spectral Learning
Website
Google Scholar
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Biography

Our lab works on developing foundational mathematical machine learning and deep learning methods that incorporate graph-based learning, signal processing, information theory, data geometry and topology, optimal transport and dynamics modeling that are capable of exploratory analysis, scientific inference, interpretation and hypothesis generation big biomedical datasets ranging from single-cell data, to brain imaging, to molecular structural datasets arising from neuroscience, psychology, stem cell biology, cancer biology, healthcare, and biochemistry. Our works have been instrumental in dynamic trajectory learning from static snapshot data, data denoising, visualization, network inference, molecular structure modeling and more.

Publications

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease
Manik Kuchroo
Jessie Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Alexander Tong
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Benjamin Israelow
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana
John Fournier
Shelli Farhadian … (see 7 more)
Charles S. Dela Cruz
Albert I. Ko
F. Perry Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2020-11-17
bioRxiv (preprint)
doi.org
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio
Rim Assouel
Victor Schmidt
Tristan Sylvain
Smita Krishnaswamy
Yoshua Bengio
2020-06-23
ArXiv (preprint)
arxiv.org
arxiv.org
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
Alexander Tong
Guy Wolf
Smita Krishnaswamy
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep … (see more)learning methods for this task often rely on autoencoder reconstruction error, sometimes in conjunction with other penalties. We show that this approach exhibits intrinsic biases that lead to undesirable results. Reconstruction-based methods can sometimes show low error on simple-to-reconstruct points that are not part of the training data, for example the all black image. Instead, we introduce a new unsupervised Lipschitz anomaly discriminator (LAD) that does not suffer from these biases. Our anomaly discriminator is trained, similar to the discriminator of a GAN, to detect the difference between the training data and corruptions of the training data. We show that this procedure successfully detects unseen anomalies with guarantees on those that have a certain Wasserstein distance from the data or corrupted training set. These additions allow us to show improved performance on MNIST, CIFAR10, and health record data. Further, LAD does not require decoding back to the original data space, which makes anomaly detection possible in domains where it is difficult to define a decoder, such as in irregular graph structured data. Empirically, we show this framework leads to improved performance on image, health record, and graph data.
2019-05-26
Journal of Signal Processing Systems (published)
doi.org