About Me

I am a postdoc with Yoshua Bengio studying the causal discovery of cell dynamics at Mila in Montreal. This work is in collaboration with Fabian Theis through the newly formed Helmholtz International Lab, a German-Canadian collaboration.

I completed my PhD in the computer science department at Yale University in 2021 where I was advised by Smita Krishnaswamy. My dissertation can be found here. My research interests are in generative modeling, deep learning, and optimal transport. I’m working on applying ideas from generative modeling, causal discovery, optimal transport, and graph signal processing to understand how cells develop and respond to changing conditions. I’m also interested in generative models for protein design and cofounded a startup to work on these problems.

I grew up in Seattle, Washington, USA, and graduated from Tufts University in 2017 with a BS and MS in computer science. Outside of work, I love sailing and running. I am the 2019 junior North American champion in the 505 class, and I recently ran my first 50 mile race the Vermont 50!

  • Generative Modeling
  • Optimal Transport
  • Single Cell Dynamics
  • Graph Signal Processing
  • Protein Design
  • Causal Discovery
  • PhD in Computer Science, 2021

    Yale University

  • MPhil in Computer Science, 2020

    Yale University

  • MS in Computer Science, 2017

    Tufts University

  • BS in Computer Science, 2017

    Tufts University


(2024). Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. arXiv.

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(2023). A Computational Framework for Solving Wasserstein Lagrangian Flows. arXiv.

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(2023). Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems. arXiv.

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(2023). Learning Transcriptional and Regulatory Dynamics Driving Cancer Cell Plasticity Using Neural ODE-Based Optimal Transport. BioRxiv.



(2024). SE(3)-Stochastic Flow Matching for Protein Backbone Generation. To appear in ICLR (Spotlight).

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(2024). Simulation-Free Schrodinger Bridges via Score and Flow Matching. To appear in AISTATS.
Also presented at Frontiers4LCD Workshop @ ICML 2023.

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(2024). Learnable Filters for Geometric Scattering Modules. Accepted in IEEE Trans. Sig. Proc..

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(2023). Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses. Cell.

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(2023). A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. In NeurIPS.

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(2023). DynGFN: Bayesian Dynamic Causal Discovery Using Generative Flow Networks. In NeurIPS.
Also presented at Frontiers4LCD Workshop @ NeurIPS 2022.

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(2023). Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms. In SIMODS.

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(2023). Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds. In IEEE MLSP.

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(2023). Neural FIM for Learning Fisher Information Metrics from Point Cloud Data. In ICML.

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(2023). Time-Inhomogeneous Diffusion Geometry and Topology. In SIMODS.


(2023). Single-Cell Analysis Reveals Inflammatory Interactions Driving Macular Degeneration. Nature Communications.


(2023). Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport. Accepted in TMLR
Also presented at Frontiers4LCD Workshop @ ICML 2023.

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(2023). Graph Fourier MMD for signals on data graphs. In SAMPTA.

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(2022). Manifold Interpolating Optimal-Transport Flows for Trajectory Inference. In NeurIPS.

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(2022). Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. In ICASSP.

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(2022). Immune Cells and Their Inflammatory Mediators Modify Beta Cells and Cause Checkpoint Inhibitor-Induced Diabetes. JCI Insight.


(2022). Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration. Investigative Ophthalmology & Visual Science.


(2022). Multiscale PHATE identifies multimodal signatures of COVID-19. Nature Biotechnology.


(2021). MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. In IEEE Big Data.

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(2021). A sandbox for prediction and integration of DNA, RNA, and protein data in single cells. In NeurIPS Datasets and Benchmarks.

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(2021). Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators. In Journal of Signal Processing Systems.

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(2021). Data-Driven Learning of Geometric Scattering Networks. In IEEE MLSP.
Also presented at ML4M Workshop @ NeurIPS 2020.

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(2021). Multimodal data visualization and denoising with integrated diffusion. In IEEE MLSP.

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(2021). Diffusion Earth Mover's Distance and Distribution Embeddings. In ICML.
Also presented at LMRL Workshop @ NeurIPS 2020.

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(2021). POT: Python Optimal Transport. In JMLR.

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(2021). Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing. In Nature Biotechnology.

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(2020). Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings. In IEEE Big Data.
Also at GRLB Workshop @ ICML 2020.

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(2020). Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators. In IEEE MLSP.
Best Student Paper Award.

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(2020). TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. In ICML.
Also at LMRL Workshop @ NeurIPS 2019.

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(2020). Interpretable Neuron Structuring with Graph Spectral Regularization. In IDA
Also presented at RLGM Workshop @ ICLR 2019.

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(2019). Finding Archetypal Spaces Using Neural Networks. In IEEE Big Data.

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(2018). Allocate-On-Use Space Complexity of Shared-Memory Algorithms. In DISC.