Smita Krishnaswamy

joaofelipe.rocha@mila.quebec

Github

Google Scholar

Publications

GeoSinkhorn Flow: Geometry-Aware Flow Matching for Perturbation-Conditioned Dynamics in Single cell Data Phenoscapes

Ke Xu

Alistair Wilkinson

Ni Shuang

Guillaume Huguet

Gerstein Mark

Christopher J. Tape

Single-cell technologies increasingly enable large-scale profiling of complex biological systems across many experimental perturbations, but… (see more) understanding how a system reorganizes across an entire space of conditions is difficult to interpret from single-cell embeddings alone. Phenoscapes address this by embedding entire cell-state distributions, rather than individual cells, as points in a data-driven landscape. Thus, a single dot represents one experimental condition rather than one cell, yielding a map of the perturbation or patient landscape that reveals how phenotype varies continuously across conditions. Phenoscapes remain a comparatively new and underexplored representation and existing static phenoscapes position experimental conditions relative to one another without specifying trajectories that describe how a cellular population moves from one condition to another. Flow- and trajectory-based models for single-cell data are numerous, but existing approaches operate at the level of individual cells, are not built to respect the geometry of a phenoscape, and typically cannot generalize their predicted flows to conditions not seen during training. Here we introduce GeoSinkhorn Flow, a perturbation-conditioned, geometry-aware flow-matching framework specialized designed to convert a static phenoscape into a dynamic phenoscape, in which each condition-level point is additionally connected by geodesic, condition-aware transport trajectories that are consistent with the phenoscape's underlying geometry. GeoSinkhorn Flow can also model transitions directly between time points without constructing a phenoscape. GeoSinkhorn Flow uses heat-diffusion geometry to define manifold-aware transport costs and trains a geometric autoencoder whose latent Euclidean distances stay aligned with the static phenoscape geometry while extending it to unseen cells. By explicitly conditioning the learned transport dynamics on perturbation-specific gene or drug embeddings, GeoSinkhorn Flow generalizes its trajectories to new measurement conditions, produces intermediate progression states along a trajectory, and extends predictions to unseen cells and held-out perturbations. An unbalanced optimal transport variant further learns growth or transition weights, so that changes in relative cell-state abundance or transition propensity are modeled together with state movement. Across synthetic manifolds, patient-derived organoid drug responses, Perturb-seq knockdowns, and time-resolved CyTOF signaling, GeoSinkhorn Flow recovered intrinsic manifold geometry, inferred biologically coherent response trajectories, and generalized to held-out drug combinations, perturbation genes, and time points. Together, these results establish GeoSinkhorn Flow as a perturbation-conditioned, phenoscape-consistent dynamical framework, distinct from standard flow-matching approaches, for converting static phenoscapes into dynamic ones that generalize across experimental conditions.

2026-10-21

bioRxiv (accepted)

Same Proteins, Different Verdicts: Toxicity-Classifier Clearance Is Not a Portable Safety Measure

Ananya Krishna

Bruce James Wittmann

DNA synthesis screening creates one of the few control points between a designed protein and its physical production. Deployed screens mainl… (see more)y search for similarity to catalogued sequences of concern. Generative methods can reduce that similarity while preserving predicted structural properties, so recent studies have also used academic toxicity classifiers to assess whether a redesign remains harmful. This practice assumes that classifier clearance is stable enough to support safety claims and comparisons among redesign methods. We test that assumption after an apparent success in our own protein redesign study failed to reproduce. We rescored the same historical sequences with a specified ToxinPred3 version and found that it labelled 94 of 100 sequences non-toxic for each of two redesign methods, removing the original separation. We then compared three evaluators on 176 unmodified proteins that CommEC flags. ToxinPred2 calls 156 toxic, whereas the published ToxinPred3 rule calls only 6 toxic. A fixed negative motif term changes 50 ToxinPred3 decisions. The disagreement also changes conclusions about redesign: OAE passes CommEC for 36 proteins, but random substitutions at the same positions pass 37 (McNemar p=1.0). Protein-language-model probes and sparse-autoencoder features do not provide an independent functional label. Toxicity-classifier clearance therefore depends on the evaluator, version, threshold, input type, and positive class. It does not constitute a general safety property of a redesign.

2026-10-01

ICBINB-BIO @ Neural Information Processing Systems (poster)

The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis

Aakash Patel

Panayiotis Ketonis

Shreya Saxena

J. Adam Noah

David van Dijk

Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without comput… (see more)ational backgrounds. We present the AI Neuroscientist, a language agent for interactive data exploration. The system integrates a large language model (LLM) with a neuroimaging toolset to perform quality control, modeling, and visualization. This allows researchers to query data quality and specify analysis parameters directly in natural language, providing a transparent and interactive alternative to conventional scripted pipelines for small-scale data exploration. We demonstrate these capabilities using functional near-infrared spectroscopy (fNIRS) data, and evaluate the agent on a custom fNIRS benchmarking suite against general-purpose LLM agents with code sandboxes. Future extensions will generalize the architecture to additional modalities, including functional magnetic resonance imaging (fMRI) data, and expand the benchmarking suite to additional fNIRS tasks.

2026-09-30

AgenticLS @ Neural Information Processing Systems (poster)

Mapping Alzheimer’s neuropathology signatures to the whole brain transcriptome using machine learning data-fusion

L. M. Hodgson

David A Bennett

Elisabeth B. Binder

Danilo Bzdok

Abstract In Alzheimer’s disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patte… (see more)rns. Yet, a systematic bias of single-cell genomics toward sampling mostly cortical tissue limits our understanding of the whole-brain transcriptomic vulnerability to AD. Here, we develop a machine learning method to extrapolate local AD neuropathology signatures to the whole brain. By analyzing gene expression profiles of over two million cortical cells from 427 humans spanning the AD-pathology spectrum, we derive transcriptomic estimators of AD neuropathology. After extensive validations on datasets with known ground truth, we apply this framework to three million cells from 108 brain regions in the Siletti whole human brain atlas and derive an anticipated brain map of transcriptomic signatures indexing AD neuropathology. This interrogation of regions spanning the cortical, subcortical, and brainstem structures uncovers transcriptomic signatures associated with hyperphosphorylated tau in the medulla oblongata, dorsal raphe nucleus, and the tuberal and mammillary regions of the hypothalamus. At the cellular level, assessments of these signatures across 31 cell populations identify VGLUT1/2 expressing neurons, astrocytes, and microglia as key neuropathology-resembling populations. Within the hippocampus, pathology signatures surface in the rostral cornu ammonis (CA) subfields, particularly in the CA1 pyramidal neurons and dentate granule cells. β-amyloid-like signatures localize to the neocortex with laminar selectivity — most prominently in upper layer somatostatin+ intratelencephalic neurons (L2-L3), but also in deep layer intratelencephalic and corticothalamic neurons (L5-L6). Neocortical astrocytes and microglia exhibiting disease associated signatures similarly demonstrate a unique laminar preference. Together, this study provides the first whole human brain map of AD pathology-associated transcriptomic signals, and exposes cell type, region, and cortex layer specific vulnerabilities.

2026-08-23

bioRxiv (accepted)

César Miguel Valdez Cordova

Towards Distilling the Representational Geometry of Language Models

Andrew J Steindl

Dhananjay Bhaskar

Ian Adelstein

Large neural networks are increasingly combined through weight-space merging and distilled into smaller models, but weights and activations … (see more)are not canonical coordinates: two networks can implement similar functions while differing by permutations, rescalings, or basis changes. We ask whether these operations can instead be guided by the geometry of a model's representation space. For each model, we summarize the relationships among probe examples using a diffusion operator built from internal activations. We then test a simple distillation procedure in which a student is trained to match a fixed diffusion-geometry target derived from a teacher. Across Pythia, BERT, and DeBERTa-v3, this target does not generally make the student inherit the teacher's specific representation geometry. Instead, distilled students usually remain closer to matched task-only controls, and the objective has little effect on held-out performance. These results suggest that representation-geometry objectives must be evaluated against the geometry a student would learn from ordinary training alone.

2026-07-15

Topology, Algebra, and Geometry in Data Science (poster)

Human learning of noninvasive brain–computer interfaces via manifold geometry

Erica L. Busch

E. Chandra Fincke

Guillaume Lajoie

Nicholas B. Turk‐Browne

2026-06-08

Nature Neuroscience (published)

ImmunoFoundation: A Multimodal Foundation Model for Immunogenicity Prediction and Peptide Optimization

João Felipe Rocha

Hiren Madhu

Jenny Yongjia Liu

Apurva Mishra

Chen Liu

Rishabh Anand

Rex Ying

Peptide immunogenicity, whether a peptide presented by an MHC molecule elicits a T-cell response, is central to designing vaccines, cancer i… (see more)mmunotherapy, and therapeutic proteins. Existing tools rely on a single modality, such as peptide sequences or peptide-MHC interactions, and often ignore the T-cell response that depends on the TCR-peptide-MHC complex (TCR-pMHC) and its three-dimensional structure. The scarcity of labeled TCR-pMHC data with known structures makes it difficult to build a model that captures how all components of the TCR-pMHC contribute to immunogenicity. However, a foundation model of TCR-pMHCs can learn transferable representations across components, which can be adapted to immunogenicity, binding, and TCR specificity tasks, even with limited labeled data. We introduce **ImmunoFoundation**, a self-supervised multimodal backbone for protein-complex representation, fine-tuned for peptide--MHC immunogenicity. The model couples an ESM-2 sequence encoder with a graph transformer over structure, fused via cross-modal attention. Pretraining follows a curriculum that progressively introduces structural inductive bias. **ImmunoFoundation** ourperforms prior multimodal class-I predictors on cancer neoepitope and infectious-disease tasks.

2026-05-27

FM4LS @ International Conference on Machine Learning (poster)

César Miguel Valdez Cordova

scShapeBench: Discovering geometry from high dimensional scRNAseq data

Andrew J. Steindl

João Felipe Rocha

Brian Tshilengi Di Bassinga

Zachary Warren

Matthew Scicluna

Shabarni Gupta

Leire Torices

Daniel Neumann

Timothy J. Mann

Ihuan Gunawan

Dhananjay Bhaskar

John G. Lock

Christine L. Chaffer

High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these da… (see more)tasets determine the types of information that can be extracted. For example, clustered data supports cell-type identification, trajectory structures support transition analysis, and archetypal structures capture continua of cellular behaviors. Existing analysis pipelines often assume a specific shape. The standard Seurat pipeline combines UMAP visualization with Louvain clustering and therefore assumes clustered data, while tools such as Monocle and SPADE assume tree-like structures, and flow-based models such as MIOFlow and Conditional Flow Matching target trajectories. Choosing which pipeline to apply is therefore often left to bioinformaticians who visually inspect datasets before selecting an analysis strategy. With the rise of agentic AI scientists, automating shape detection is increasingly important for selecting downstream analysis pipelines. To address this problem, we introduce scShapeBench, a benchmark dataset for shape detection containing both synthetic and expert-annotated single-cell datasets. Synthetic datasets are sampled from ground-truth skeleton graphs with controlled variance. Real single-cell datasets are curated from diverse sources and annotated by experts into four categories: clusters, single trajectory, multi-branching, and archetypal. We additionally introduce scReebTower, a baseline method that uses diffusion geometry to extract Reeb graphs and connect visualization with pipeline selection. We provide topology-aware evaluation metrics and compare scReebTower against PAGA and Mapper on synthetic and real data. Our results indicate that scReebTower outperforms existing baselines. Overall, our contributions span benchmarks, evaluation metrics, and a baseline for automated shape detection in single-cell data.

2026-05-11

arXiv (preprint)

arxiv.org

RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics.

Danqi Liao

Chen Liu

Xingzhi Sun

Dié Tang

Haochen Wang

Scott Youlten

Srikar Krishna Gopinath

Haejeong Lee

Ethan C. Strayer

Antonio J. Giraldez

Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains … (see more)challenging due to limited data, complex sequence-function relationships, and the narrow space of biologically viable sequences. Generative methods that drift away from the data manifold can yield sequences that fail to fold, translate poorly, or are otherwise nonfunctional. We present RNAGenScape, a property-guided manifold Langevin dynamics framework for mRNA sequence generation that operates directly on a learned manifold of real data. By performing iterative local optimization constrained to this manifold, RNAGenScape preserves biological viability, accesses reliable guidance, and avoids excursions into nonfunctional regions of the ambient sequence space. The framework integrates three components: (1) an autoencoder jointly trained with a property predictor to learn a property-organized latent manifold, (2) a denoising autoencoder that projects updates back onto the manifold, and (3) a property-guided Langevin dynamics procedure that performs optimization along the manifold. Across three real-world mRNA datasets spanning two orders of magnitude in size, RNAGenScape increases median property gain by up to 148% and success rate by up to 30% while ensuring biological viability of generated sequences, and achieves competitive inference efficiency relative to existing generative approaches.

2026-05-07

arXiv (published)

pubmed.ncbi.nlm.nih.gov

MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data

Xingzhi Sun

João Felipe Rocha

Brett Phelan

Dhananjay Bhaskar

Guillaume Huguet

Yanlei Zhang

D. S. Magruder

Alexander Tong

Ke Xu

Oluwadamilola Fasina

Mark Gerstein

Natalia Ivanova

Christine L. Chaffer

Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disea… (see more)se. A key challenge is inferring continuous trajectories from discrete snapshots. Biological complexity stems from stochastic cell fate decisions, temporal proliferation changes, and spatial environmental influences. Current methods often use deterministic interpolations treating cells in isolation, failing to capture the probabilistic branching, population shifts, and niche-dependent signaling driving real biological processes. We introduce Manifold Interpolating Optimal-Transport Flow (MIOFlow) 2.0. This framework learns biologically informed cellular trajectories by integrating manifold learning, optimal transport, and neural differential equations. It models three core processes: (1) stochasticity and branching via Neural Stochastic Differential Equations; (2) non-conservative population changes using a learned growth-rate model initialized with unbalanced optimal transport; and (3) environmental influence through a joint latent space unifying gene expression with spatial features like local cell type composition and signaling. By operating in a PHATE-distance matching autoencoder latent space, MIOFlow 2.0 ensures trajectories respect the data's intrinsic geometry. Empirical comparisons show expressive trajectory learning via neural differential equations outperforms existing generative models, including simulation-free flow matching. Validated on synthetic datasets, embryoid body differentiation, and spatially resolved axolotl brain regeneration, MIOFlow 2.0 improves trajectory accuracy and reveals hidden drivers of cellular transitions, like specific signaling niches. MIOFlow 2.0 thus bridges single-cell and spatial transcriptomics to uncover tissue-scale trajectories.

2026-03-22

arXiv (preprint)

arxiv.org

DYMAG: Rethinking Message Passing Using Dynamical-systems-based Waveforms

Dhananjay Bhaskar

Xingzhi Sun

Yanlei Zhang

Charles Xu

Oluwadamilola Fasina

Michael Perlmutter

We present DYMAG, a graph neural network based on a novel form of message aggregation. Standard message-passing neural networks, which often… (see more) aggregate local neighbors via mean-aggregation, can be regarded as convolving with a simple rectangular waveform which is non-zero only on 1-hop neighbors of every vertex. Here, we go beyond such local averaging. We will convolve the node features with more sophisticated waveforms generated using dynamics such as the heat equation, wave equation, and the Sprott model (an example of chaotic dynamics). Furthermore, we use snapshots of these dynamics at different time points to create waveforms at many effective scales. Theoretically, we show that these dynamic waveforms can capture salient information about the graph, including connected components, connectivity, and cycle structures. Empirically, we test DYMAG on both real and synthetic benchmarks to establish that DYMAG outperforms baseline models on recovery of graph persistence, generating parameters of random graphs, as well as property prediction for proteins, molecules and materials. Our code is available at https://github.com/KrishnaswamyLab/DYMAG.

2026-02-26

Conference on Topology, Algebra, and Geometry in Data Science (published)

proceedings.mlr.press

HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation

Hiren Madhu

Ngoc Bui

Ali Maatouk

Leandros Tassiulas