[Sds-seminars] [Sds-announce] Wednesday at 4pm, Smita Krishnaswamy on "Diffusion Earth Mover’s Distance, Distribution Embeddings and Flows"

[Dan Spielman]

FDS Seminar Series*“Diffusion Earth Mover’s Distance, Distribution
Embeddings and Flows”*

*Speaker: Smita Krishnaswamy*
Associate Professor, Dept of Computer Science, Dept. of Genetics
Programs for Applied Math, Computational Biology & Bioinformatics,
Interdisciplinary Neuroscience
Yale Cancer Center, Wu Tsai-Institute

*Wednesday, October 12, 2022 - 4:00pm*



*Location: *DL220 | 10 Hillhouse Avenue | New Haven,  CT 06520

*Abstract:* We propose a new fast method of measuring distances between
large numbers of related high dimensional datasets called the Diffusion
Earth Mover’s Distance (EMD). We model the datasets as distributions
supported on a common data graph that is derived from the affinity matrix
computed on the combined data. In such cases where the graph is a
discretization of an underlying Riemannian closed manifold, we prove that
Diffusion EMD is topologically equivalent to the standard EMD with a
geodesic ground distance. Diffusion EMD can be computed in {Õ}(n) time and
is more accurate than similarly fast algorithms such as tree-based EMDs. We
also show Diffusion EMD is fully differentiable, making it amenable to
future uses in gradient-descent frameworks such as deep neural networks. We
demonstrate an application of Diffusion EMD to single cell data collected
from 210 COVID-19 patient samples at Yale New Haven Hospital. Here,
Diffusion EMD can derive distances between patients on the manifold of
cells at least two orders of magnitude faster than equally accurate
methods. This distance matrix between patients can be embedded into a
higher-level patient manifold which uncovers structure and heterogeneity in
patients. Finally, we show DEMD’s incorporation into a neural ode framework
we recently developed called MIOFlow (Manifold Interpolating Flows) for
learning dynamics from static snapshot data. MIOFlow uses an autoencoder
with multiscale diffusion distances to find a manifold embedding of data.
Then within this latent space, we use the neural network to learn a
continuous time derivative to perform dynamic optimal transport of static
snapshot measurements of cells and in the process infer continuous dynamics
and single-cell trajectories. We show results of this in a cancer
metastasis system measured using single-cell RNA-sequencing.

*Speaker bio: *Smita Krishnaswamy is an Associate Professor in the
department of Genetics at the Yale School of Medicine and Department of
Computer Science in the Yale School of Applied Science and Engineering and
a core member of the Program in Applied Mathematics. She is also affiliated
with the Yale Center for Biomedical Data Science, Yale Cancer Center, and
Program in Interdisciplinary Neuroscience. Smita’s research focuses on
developing unsupervised machine learning methods (especially graph signal
processing and deep-learning) to denoise, impute, visualize and extract
structure, patterns and relationships from big, high throughput, high
dimensional biomedical data. Her methods have been applied variety of
datasets from many systems including embryoid body differentiation,
zebrafish development, the epithelial-to-mesenchymal transition in breast
cancer, lung cancer immunotherapy, infectious disease data, gut microbiome
data and patient data.

Smita teaches three courses: Machine Learning for Biology (Fall), Deep
Learning Theory and applications (spring), Advanced Topics in Machine
Learning & Data Mining (Spring). She completed her postdoctoral training at
Columbia University in the systems biology department where she focused on
learning computational models of cellular signaling from single-cell mass
cytometry data. She was trained as a computer scientist with a Ph.D. from
the University of Michigan’s EECS department where her research focused on
algorithms for automated synthesis and probabilistic verification of
nanoscale logic circuits. Following her time in Michigan, Smita spent 2
years at IBM’s TJ Watson Research Center as a researcher in the systems
division where she worked on automated bug finding and error correction in
logic.

This presentation will be live and in-person, but remote access is
available here:
https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=7e3a4115-73f7-4205-976d-af1c01266047

*Upcoming:* *The FDS Kickoff Event on 10/14*. *Register here*:
https://fds-kickoff.eventbrite.com
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