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
X

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

CTR-LoRA: Curvature-Aware and Trust-Region Guided Low-Rank Adaptation for Large Language Models
Zhuxuanzi Wang
Mingqiao Mo
Xi Xiao
Chen Liu
Chenrui Ma
Yunbei Zhang
Xiao Wang
Smita Krishnaswamy
Tianyang Wang
Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory … (see more)budgets. Although previous methods improve efficiency through low-rank updates, quantization, or heuristic budget reallocation, they often decouple the allocation of capacity from the way updates evolve during training. In this work, we introduce CTR-LoRA, a framework guided by curvature trust region that integrates rank scheduling with stability-aware optimization. CTR-LoRA allocates parameters based on marginal utility derived from lightweight second-order proxies and constrains updates using a Fisher/Hessian-metric trust region. Experiments on multiple open-source backbones (7B-13B), evaluated on both in-distribution and out-of-distribution benchmarks, show consistent improvements over strong PEFT baselines. In addition to increased accuracy, CTR-LoRA enhances training stability, reduces memory requirements, and achieves higher throughput, positioning it on the Pareto frontier of performance and efficiency. These results highlight a principled path toward more robust and deployable PEFT.
2025-10-11
ArXiv (preprint)
arxiv.org
arxiv.org
Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets
David R. Johnson
Rishabh Anand
Smita Krishnaswamy
Michael Perlmutter
We introduce a novel version of the geometric scattering transform for geometric graphs containing scalar and vector node features. This new… (see more) scattering transform has desirable symmetries with respect to rigid-body roto-translations (i.e.,
2025-10-01
ArXiv (preprint)
arxiv.org
arxiv.org
Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets
David R. Johnson
Rishabh Anand
Smita Krishnaswamy
Michael Perlmutter
2025-10-01
ArXiv (preprint)
arxiv.org
arxiv.org
HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu
João Felipe Rocha
Tinglin Huang
Siddharth Viswanath
Smita Krishnaswamy
Rex Ying
2025-09-25
ArXiv (preprint)
doi.org
arxiv.org
HEIST: A Graph Foundation Model for Spatial Transcriptomics and
Proteomics Data
Hiren Madhu
João Felipe Rocha
Tinglin Huang
Siddharth Viswanath
Smita Krishnaswamy
Rex Ying
2025-09-25
ArXiv (preprint)
www.ncbi.nlm.nih.gov
A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks
Howard Dai
Nyambura Njenga
Hiren Madhu
Siddharth Viswanath
Ryan Pellico
Ian Adelstein
Smita Krishnaswamy
The development of self-supervised graph pre-training methods is a crucial ingredient in recent efforts to design robust graph foundation mo… (see more)dels (GFMs). Structure-based pre-training methods are under-explored yet crucial for downstream applications which rely on underlying graph structure. In addition, pre-training traditional message passing GNNs to capture global and regional structure is often challenging due to the risk of oversmoothing as network depth increases. We address these gaps by proposing the Laplacian Eigenvector Learning Module (LELM), a novel pre-training module for graph neural networks (GNNs) based on predicting the low-frequency eigenvectors of the graph Laplacian. Moreover, LELM introduces a novel architecture that overcomes oversmoothing, allowing the GNN model to learn long-range interdependencies. Empirically, we show that models pre-trained via our framework outperform baseline models on downstream molecular property prediction tasks.
2025-09-02
ArXiv (preprint)
arxiv.org
arxiv.org
Learning Laplacian Eigenvectors: a Pre-training Method for Graph Neural Networks
Howard Dai
Nyambura Njenga
Benjamin Whitsett
Catherine Ma
Darwin Deng
Sara de 'Angel
Alexandre Van Tassel
Siddharth Viswanath
Ryan Pellico
Ian Adelstein
Smita Krishnaswamy
2025-09-02
ArXiv (preprint)
arxiv.org
arxiv.org
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt
Alex Diaz-Papkovich
Michael Bleher
Kerstin Bunte
Corinna Coupette
Sebastian Damrich
Enrique Fita Sanmartin
Fred Hamprecht
EmHoke-'Agnes Horv'at
Dhruv Kohli
Smita Krishnaswamy
John A. Lee 0001
Boudewijn P. F. Lelieveldt
Leland McInnes
Ian T. Nabney
Maximilian Noichl
Pavlin G. Polivcar
Bastian Rieck
Guy Wolf
Gal Mishne … (see 1 more)
Dmitry Kobak
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from b… (see more)iology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.
2025-08-21
ArXiv (preprint)
doi.org
arxiv.org
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt
Alex Diaz-Papkovich
Michael Bleher
Kerstin Bunte
Corinna Coupette
Sebastian Damrich
Enrique Fita Sanmartin
Fred A. Hamprecht
EmHoke-'Agnes Horv'at
Dhruv Kohli
Smita Krishnaswamy
John A. Lee 0001
Boudewijn P. F. Lelieveldt
Leland McInnes
Ian T. Nabney
Maximilian Noichl
Pavlin G. Polivcar
Bastian Rieck
Guy Wolf
Gal Mishne … (see 1 more)
Dmitry Kobak
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from b… (see more)iology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.
2025-08-21
ArXiv (preprint)
doi.org
arxiv.org
Revealing dynamic temporal trajectories and underlying regulatory networks with Cflows
Alexander Tong
Manik Kuchroo
Shabarni Gupta
Aarthi Venkat
Beatriz P. San Juan
Laura Rangel
Brandon Zhu
John G. Lock
Christine L. Chaffer
Smita Krishnaswamy
While single-cell technologies provide snapshots of tumor states, building continuous trajectories and uncovering causative gene regulatory … (see more)networks remains a significant challenge. We present Cflows, an AI framework that combines neural ODE networks with Granger causality to infer continuous cell state transitions and gene regulatory interactions from static scRNA-seq data. In a new 5-time point dataset capturing tumorsphere development over 30 days, Cflows reconstructs two types of trajectories leading to tumorsphere formation or apoptosis. Trajectory-based cell-of-origin analysis delineated a novel cancer stem cell profile characterized by CD44hiEPCAM+CAV1+, and uncovered a cell cycle–dependent enrichment of tumorsphere-initiating potential in G2/M or S-phase cells. Cflows uncovers ESRRA as a crucial causal driver of the tumor-forming gene regulatory network. Indeed, ESRRA inhibition significantly reduces tumor growth and metastasis in vivo. Cflows offers a powerful framework for uncovering cellular transitions and dynamic regulatory networks from static single-cell data.
2025-08-07
bioRxiv (preprint)
doi.org
CellForge: Agentic Design of Virtual Cell Models
Xiangru Tang
Zhuoyun Yu
Jiapeng Chen
Yan Cui
Yanjun Shao
Weixu Wang
Fang Wu
Yuchen Zhuang
Wenqi Shi
Zhi Huang
Arman Cohan
Xihong Lin
Fabian Theis
Smita Krishnaswamy
Mark B. Gerstein
Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantiti… (see more)es such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.
2025-08-04
ArXiv (preprint)
doi.org
arxiv.org
CellForge: Agentic Design of Virtual Cell Models
Xiangru Tang
Zhuoyun Yu
Jiapeng Chen
Yan Cui
Yanjun Shao
Weixu Wang
Fang Wu
Yuchen Zhuang
Wenqi Shi
Zhi Huang
Arman Cohan
Xihong Lin
Fabian Theis
Smita Krishnaswamy
Mark B. Gerstein
Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantiti… (see more)es such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.
2025-08-04
ArXiv (preprint)
doi.org
arxiv.org