Portrait of Smita Krishnaswamy

Smita Krishnaswamy

Affiliate Member
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
LinkedIn
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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

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
Chen Liu
Danqi Liao
Alejandro Parada-Mayorga
Alejandro Ribeiro
Marcello DiStasio
Smita Krishnaswamy
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (see more) biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.
2024-10-04
ArXiv (preprint)
doi.org
arxiv.org
DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images
Chen Liu
Danqi Liao
Alejandro Parada-Mayorga
Alejandro Ribeiro
Marcello DiStasio
Smita Krishnaswamy
The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (see more) biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.
2024-10-04
ArXiv (preprint)
doi.org
arxiv.org
ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
Siddharth Viswanath
Dhananjay Bhaskar
David R. Johnson
João Felipe Rocha
Egbert Castro
Jackson Grady
Alex T. Grigas
Michael Perlmutter
Corey S. O'Hern
Smita Krishnaswamy
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress… (see more) has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.
2024-10-01
arXiv (published)
doi.org
arxiv.org
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi
Dhananjay Bhaskar
Erica L. Busch
Laurent Caplette
Rahul Singh
Guillaume Lajoie
Nicholas B. Turk-Browne
Smita Krishnaswamy
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (see more)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.
2024-09-27
ArXiv (preprint)
doi.org
arxiv.org
Abstract PR-05: Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk
Cathy C. Garcia
Aarthi Venkat
Daniel C. McQuaid
Sherry Agabiti
Alex Tong
Rebecca Cardone
Richard G. Kibbey
Smita Krishnaswamy
Mandar Deepak Muzumdar
For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (see more)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis. Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.
2024-09-15
Cancer Research (published)
doi.org
Abstract PR-05: Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk
Cathy C. Garcia
Aarthi Venkat
Daniel C. McQuaid
Sherry Agabiti
Alex Tong
Rebecca Cardone
Richard G. Kibbey
Smita Krishnaswamy
Mandar Deepak Muzumdar
For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (see more)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis. Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.
2024-09-15
Cancer Research (published)
doi.org
Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
Xingzhi Sun
Charles Xu
João F. Rocha
Chen Liu
Benjamin Hollander-Bodie
Laney Goldman
Marcello DiStasio
Michael Perlmutter
Smita Krishnaswamy
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (see more)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.
2024-09-14
ArXiv (preprint)
doi.org
arxiv.org
Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
Xingzhi Sun
Charles Xu
João Felipe Rocha
Chen Liu
Benjamin Hollander-Bodie
Laney Goldman
Marcello DiStasio
Michael Perlmutter
Smita Krishnaswamy
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (see more)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.
2024-09-14
ArXiv (preprint)
doi.org
arxiv.org
Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun
Danqi Liao
Kincaid MacDonald
Yanlei Zhang
Guillaume Huguet
Guy Wolf
Ian Adelstein
Tim G. J. Rudner
Smita Krishnaswamy
Non-linear dimensionality reduction methods have proven successful at learning low-dimensional representations of high-dimensional point clo… (see more)uds on or near data manifolds. However, existing methods are not easily extensible—that is, for large datasets, it is prohibitively expensive to add new points to these embeddings. As a result, it is very difficult to use existing embeddings generatively, to sample new points on and along these manifolds. In this paper, we propose GAGA (geometry-aware generative autoencoders) a framework which merges the power of generative deep learning with non-linear manifold learning by: 1) learning generalizable geometry-aware neural network embeddings based on non-linear dimensionality reduction methods like PHATE and diffusion maps, 2) deriving a non-euclidean pullback metric on the embedded space to generate points faithfully along manifold geodesics, and 3) learning a flow on the manifold that allows us to transport populations. We provide illustration on easily-interpretable synthetic datasets and showcase results on simulated and real single cell datasets. In particular, we show that the geodesic-based generation can be especially important for scientific datasets where the manifold represents a state space and geodesics can represent dynamics of entities over this space.
2024-06-17
ICML.cc/2024/Workshop/GRaM (published)
openreview.net
openreview.net
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Holly Steach
Siddharth Viswanath
Yixuan He
Xitong Zhang
Natalia Ivanova
Matthew Hirn
Michael Perlmutter
Smita Krishnaswamy
2024-05-17
Lecture Notes in Computer Science (published)
doi.org
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
2024-05-17
Communications Biology (published)
doi.org
Novel cell states arise in embryonic cells devoid of key reprogramming factors
Scott E. Youlten
Liyun Miao
Caroline Hoppe
Curtis W. Boswell
Damir Musaev
Mario Abdelmessih
Smita Krishnaswamy
Valerie A. Tornini
Antonio J. Giraldez
The capacity for embryonic cells to differentiate relies on a large-scale reprogramming of the oocyte and sperm nucleus into a transient tot… (see more)ipotent state. In zebrafish, this reprogramming step is achieved by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Yet, it remains unclear whether cells lacking this reprogramming step are directed towards wild type states or towards novel developmental canals in the Waddington landscape of embryonic development. Here we investigate the developmental fate of embryonic cells mutant for NPS by analyzing their single-cell gene expression profiles. We find that cells lacking the first developmental reprogramming steps can acquire distinct cell states. These states are manifested by gene expression modules that result from a failure of nuclear reprogramming, the persistence of the maternal program, and the activation of somatic compensatory programs. As a result, most mutant cells follow new developmental canals and acquire new mixed cell states in development. In contrast, a group of mutant cells acquire primordial germ cell-like states, suggesting that NPS-dependent reprogramming is dispensable for these cell states. Together, these results demonstrate that developmental reprogramming after fertilization is required to differentiate most canonical developmental programs, and loss of the transient totipotent state canalizes embryonic cells into new developmental states in vivo.
2024-05-15
bioRxiv (preprint)
doi.org