Zander W. Blasingame
I am a postdoctoral fellow at Aithyra in Vienna, Austria, working with Alex Tong on generative models for the life sciences. My research interests are mostly in the use of diffusion- and flow-based generative models for controllable generation and editing.
Previously, I received my Ph.D. in Electrical and Computer Engineering from Clarkson University in 2025, supervised by Chen Liu. During my Ph.D. I focused on controllable generation with flow/diffusion models along with downstream applications in biometrics. Examples include developing reversible numerical schemes for diffusion SDEs, numerical schemes for computing the gradients through the ODE/SDE solve of diffusion models, and developing face morphing methods with diffusion models.
Χριστὸς ὁ βασιλεὺς
Christ the King
In my free time I am an amateur athlete and theologian. I love athletics and reached a 1145 lbs / 520 kg powerlifting total, and currently spend my training on weightlifting and indoor / beach volleyball. During grad school I was a middle blocker / opposite hitter for the Clarkson Men's Club Volleyball team.
I am always open to new collaborations — if you'd like to work together, please don't hesitate to reach out.
Recent highlights all →
- 2026 Apr 30Rex accepted as a Spotlight at ICML 2026.
- 2026 Jan 12Moved to Vienna and joined Aithyra as a Postdoctoral Fellow with Alex Tong.
- 2025 Oct 17Selected as a Top Reviewer at NeurIPS 2025.
- 2025 Sep 18Greed is Good accepted at NeurIPS 2025.
- 2024 Sep 25AdjointDEIS: Efficient Gradients for Diffusion Models accepted in the main track at NeurIPS 2024.
Selected publications all →
bib
@inproceedings{blasingame2026rex,
title = {Rex: A Family of Reversible Exponential (Stochastic) Runge--Kutta Solvers},
author = {Blasingame, Zander W. and Liu, Chen},
booktitle = {Forty-third International Conference on Machine Learning},
year = {2026},
}bib
@inproceedings{passaro2026stochastic,
title = {Stochastic Few-step Models},
author = {Passaro, Romeo and Blasingame, Zander W. and Bronstein, Michael M. and Tong, Alexander},
booktitle = {ICLR 2026 DeLTa and ReALM-GEN workshops},
year = {2026},
}bib
@inproceedings{blasingame2025greed,
title = {Greed is Good: A Unifying Perspective on Guided Generation},
author = {Blasingame, Zander W. and Liu, Chen},
booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year = {2025},
}bib
@inproceedings{blasingame2024adjointdeis,
title = {Adjoint{DEIS}: Efficient Gradients for Diffusion Models},
author = {Blasingame, Zander W. and Liu, Chen},
booktitle = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year = {2024},
}Recent writings all →
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2025 May 23
Deriving the Drift and Diffusion Coefficients for Diffusion Models Where do the popular variance-preserving SDE coefficients actually come from? A derivation, working backwards from the desired transition kernel.
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2024 Dec 3
Gradients for Time-Scheduled Conditional Variables in Neural Differential Equations A càdlàg extension of the continuous adjoint equations for piecewise-continuous conditional inputs.
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2024 Nov 20
The Continuous Adjoint Equations for Diffusion Models A guided tour through AdjointDEIS — exponential integrators for the adjoint ODE/SDE of diffusion models.