Nic Fishman
@njw.fish
Using computers to read the entrails of modernity (statistics, optimization, machine learning), focused on applications in the social and biological sciences.
Currently: Stats PhD @Harvard
Previously: CS/Soc @Stanford, Stat/ML @Oxford
https://njw.fish
Currently: Stats PhD @Harvard
Previously: CS/Soc @Stanford, Stat/ML @Oxford
https://njw.fish
8/ 🧬 Synthetic promoter design
Sequences binned by expression → GDEs provide a powerful embedding space for regulatory sequence design.
🦠 SARS-CoV-2 spike protein dynamics
GDEs embed monthly lineage distributions and recover smooth latent chronologies.
Sequences binned by expression → GDEs provide a powerful embedding space for regulatory sequence design.
🦠 SARS-CoV-2 spike protein dynamics
GDEs embed monthly lineage distributions and recover smooth latent chronologies.
May 26, 2025 at 3:52 PM
8/ 🧬 Synthetic promoter design
Sequences binned by expression → GDEs provide a powerful embedding space for regulatory sequence design.
🦠 SARS-CoV-2 spike protein dynamics
GDEs embed monthly lineage distributions and recover smooth latent chronologies.
Sequences binned by expression → GDEs provide a powerful embedding space for regulatory sequence design.
🦠 SARS-CoV-2 spike protein dynamics
GDEs embed monthly lineage distributions and recover smooth latent chronologies.
7/ 🧫 Morphological profiling (20M images)
GDEs model phenotype distributions induced by perturbations and generalize to unseen conditions.
🧬 DNA methylation (253M reads)
We learn tissue-specific methylation patterns directly from raw bisulfite reads — no alignment necessary.
GDEs model phenotype distributions induced by perturbations and generalize to unseen conditions.
🧬 DNA methylation (253M reads)
We learn tissue-specific methylation patterns directly from raw bisulfite reads — no alignment necessary.
May 26, 2025 at 3:51 PM
7/ 🧫 Morphological profiling (20M images)
GDEs model phenotype distributions induced by perturbations and generalize to unseen conditions.
🧬 DNA methylation (253M reads)
We learn tissue-specific methylation patterns directly from raw bisulfite reads — no alignment necessary.
GDEs model phenotype distributions induced by perturbations and generalize to unseen conditions.
🧬 DNA methylation (253M reads)
We learn tissue-specific methylation patterns directly from raw bisulfite reads — no alignment necessary.
6/ 🧬 scRNA-seq lineage tracing
Each clone is a population of cells → a distribution over expression. GDEs predict clonal fate better than prior approaches.
🧪 CRISPR perturbation effects
GDEs can improve zero-shot prediction of transcriptional response distributions.
Each clone is a population of cells → a distribution over expression. GDEs predict clonal fate better than prior approaches.
🧪 CRISPR perturbation effects
GDEs can improve zero-shot prediction of transcriptional response distributions.
May 26, 2025 at 3:51 PM
6/ 🧬 scRNA-seq lineage tracing
Each clone is a population of cells → a distribution over expression. GDEs predict clonal fate better than prior approaches.
🧪 CRISPR perturbation effects
GDEs can improve zero-shot prediction of transcriptional response distributions.
Each clone is a population of cells → a distribution over expression. GDEs predict clonal fate better than prior approaches.
🧪 CRISPR perturbation effects
GDEs can improve zero-shot prediction of transcriptional response distributions.
5/ GDEs are built to scale: at inference, you can embed or generate from large samples without retraining.
We show that GDE embeddings are asymptotically normal, grounding this with results from empirical process theory.
We show that GDE embeddings are asymptotically normal, grounding this with results from empirical process theory.
May 26, 2025 at 3:51 PM
5/ GDEs are built to scale: at inference, you can embed or generate from large samples without retraining.
We show that GDE embeddings are asymptotically normal, grounding this with results from empirical process theory.
We show that GDE embeddings are asymptotically normal, grounding this with results from empirical process theory.
4/ With this simple setup, we find surprisingly elegant geometry:
GDE latent distances track Wasserstein-2 (W₂) distances across modalities (shown for multinomial distributions)
Latent interpolations recover optimal transport paths (shown for Gaussians and Gaussian mixtures)
GDE latent distances track Wasserstein-2 (W₂) distances across modalities (shown for multinomial distributions)
Latent interpolations recover optimal transport paths (shown for Gaussians and Gaussian mixtures)
May 26, 2025 at 3:50 PM
4/ With this simple setup, we find surprisingly elegant geometry:
GDE latent distances track Wasserstein-2 (W₂) distances across modalities (shown for multinomial distributions)
Latent interpolations recover optimal transport paths (shown for Gaussians and Gaussian mixtures)
GDE latent distances track Wasserstein-2 (W₂) distances across modalities (shown for multinomial distributions)
Latent interpolations recover optimal transport paths (shown for Gaussians and Gaussian mixtures)
🚨 New preprint 🚨
We introduce Generative Distribution Embeddings (GDEs) — a framework for learning representations of distributions, not just datapoints.
GDEs enable multiscale modeling and come with elegant statistical theory and some miraculous geometric results!
🧵
We introduce Generative Distribution Embeddings (GDEs) — a framework for learning representations of distributions, not just datapoints.
GDEs enable multiscale modeling and come with elegant statistical theory and some miraculous geometric results!
🧵
May 26, 2025 at 3:49 PM
🚨 New preprint 🚨
We introduce Generative Distribution Embeddings (GDEs) — a framework for learning representations of distributions, not just datapoints.
GDEs enable multiscale modeling and come with elegant statistical theory and some miraculous geometric results!
🧵
We introduce Generative Distribution Embeddings (GDEs) — a framework for learning representations of distributions, not just datapoints.
GDEs enable multiscale modeling and come with elegant statistical theory and some miraculous geometric results!
🧵
Another key insight: Not all fairness metrics are created equal. Our sensitivity analysis shows how different metrics respond to measurement biases - some are surprisingly fragile, others more robust. 3 / 5
December 12, 2024 at 5:08 PM
Another key insight: Not all fairness metrics are created equal. Our sensitivity analysis shows how different metrics respond to measurement biases - some are surprisingly fragile, others more robust. 3 / 5
Our research reveals a critical challenge: real-world datasets often have multiple measurement errors, and small measurement errors can completely change fariness analysis. We analyzed 14 benchmark datasets to understand these complex interactions. 2 / 5
December 12, 2024 at 5:07 PM
Our research reveals a critical challenge: real-world datasets often have multiple measurement errors, and small measurement errors can completely change fariness analysis. We analyzed 14 benchmark datasets to understand these complex interactions. 2 / 5
Excited to share our new work on causal sensitivity analysis for fairness metrics at #NeurIPS2024! We've developed a causal sensitivity analysis framework to understand how underlying measurement biases (encoded by DAGs) impact machine learning fairness evaluations. 1 / 5
December 12, 2024 at 5:07 PM
Excited to share our new work on causal sensitivity analysis for fairness metrics at #NeurIPS2024! We've developed a causal sensitivity analysis framework to understand how underlying measurement biases (encoded by DAGs) impact machine learning fairness evaluations. 1 / 5
Okay here’s my best shot at the core ideas:
October 6, 2023 at 3:43 PM
Okay here’s my best shot at the core ideas: