jc;ml;sc

Organizers

Zac Cross {zacc}† Mathew Huerta‑Enochian {mhuertae}† Joseph McLaughlin {jmclaug2}†

† @uoregon.edu

Email any of us to get on the mailing list!

Summer Itinerary

June 25 - Zac Cross (Research)
July 2 - Michael Dushkoff (Paper)
July 9 - Joseph McLaughlin (Research)
July 16 - Mathew Huerta-Enochian (Paper)
July 23
July 30
August 6
August 13
August 20
August 27
September 3
September 10
September 17

Past meetings

2026.05.28 (Week 9 S26) Master's Theses Blitz!

Master's Theses Blitz!
Presenters
   12 pm — Riya Ponraj
   1 pm — Ryan Kovatch
   2 pm — Ethan Childs
   3 pm — Eric Zander

2026.05.22 (Week 8 S26) Est. κ(A) w/ GNNs

Presenter — Joseph McLaughlin
Paper — Estimating condition number with Graph Neural Networks
In attendance — Simon Casey; Zander Chown; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Riya Ponraj; Matthew Trappett; Eric Zander.

2026.05.15 (Week 7 S26) EEG Signals

Presenter — Zander Chown
Paper — EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network
In attendance — Simon Casey; Zander Chown; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Riya Ponraj; Matthew Trappett; Eric Zander.
Cookies provided by Matthew!

2026.05.08 (Week 6 S26) Linear Transformers

Presenter — Matthew Trappett
Paper — Fast weight programming and linear transformers: from machine learning to neurobiology
In attendance — Simon Casey; Zander Chown; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Hritvik Jekki Venkateshwarulu; Manish Mathai; Joseph McLaughlin; Riya Ponraj; Tamanna Saini; Jonah Tang; Matthew Trappett; Eric Zander.

2026.04.01 (Week 5 S26) Diffusion For World Modelling

Paper — Diffusion For World Modelling;
Website
Presenter — Manish Mathai
« Link to talk »
In attendance — Simon Casey; Zander Chown; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Hritvik Jekki Venkateshwarulu; Riya Ponraj; Manish Mathai; Joseph McLaughlin; Matthew Trappett; Eric Zander.

2026.04.24 (Week 4 S26) EC R-Flow

Paper — Endpoint-Consistent Rectified Flow for the Manifold Hypothesis
Presenter — Mathew Huerta-Enochian
« Link to talk »
In attendance — Simon Casey; Ethan Childs; Zander Chown; Zac Cross; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Matthew Trappett; Eric Zander.
Notes — Paper review and crit. for upcoming submission

2026.04.17 (Week 3 S26) NetEx FW

Paper — Building Trust in Machine Learning-Powered Networking: The Network Explainer Framework
Presenter — Simon Casey
In attendance — Simon Casey; Ethan Childs; Zander Chown; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Matthew Trappett; Eric Zander.

2026.04.10 (Week 2 S26) CHONKNORIS

Paper — Operator Learning at Machine Precision
Presenter — Joseph McLaughlin
Talk (PDF)
In attendance — Simon Casey; Ethan Childs; Zac Cross; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Matthew Trappett; Eric Zander.

2026.04.02 (Week 1 S26) EGNO-3D

Paper — Equivariant Graph Neural Operator for Modeling 3D Dynamics
Presenter — Eric Zander
« Link to talk »
In attendance — Simon Casey; Ethan Childs; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Eric Zander.
Brownies — Provided by Mathew.

2026.03.12 (Week 10 W26) KAN-ODEs

Paper — KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for Learning Dynamical Systems and Hidden Physics
Presenter — Said Efendiyev
« Link to talk »
In attendance — Simon Casey; Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Eric Zander.
Cookies — provided by Michael.

2026.03.05 (Week 9 W26) RenderFlow

Paper — RenderFlow: Single-Step Neural Rendering via Flow Matching
Presenter — Manish Mathai
« Link to talk »
Pathtracing demo — Link
In attendance — Simon Casey; Zac Cross; Said Efendiyev; Kai Iverson; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Eric Zander; Yiyang Zhang.
Cookies — provided by Eric.
Remarks — FM has fewers steps during inference and makes it a great candidate for speeding up image processing applications, and the authors claim it these fewer steps introduce less "fuzziness" and noise. Maybe! It seems like whether you choose Diff or FM you're basically working with the same roll of tools, at least to many of us.

2026.02.26 (Week 8 W26) FLOWER

Paper — FLOWER: A Flow-Matching Solver for Inverse Problems
Presenter — Zac Cross
« Link to talk »
In attendance — Simon Casey; Zac Cross; Said Efendiyev; Kai Iverson; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Tamanna Saini; Eric Zander.

2026.02.20 (Week 7 W26) FM Primer

Paper — Flow Matching Primer (a) (b) (c)
Presenter — Mathew Huerta-Enochian
Missing... (pester Mathew)
About — These three papers form much of the foundational theory of FM. If you can read all or some of them, please do so! If your time is constrained, we recommend reading them in the listed order.
In attendance — Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Manish Mathai; Joseph McLaughlin; Tamanna Saini; Eric Zander; Yiyang Zhang.
Remarks — MH: Standard manifold hypothesis is key assumption of FM, does this actually hold? MM: How did the authors generate the same images in Lipman? Overfitting on a small dataset? (b) does a diffusion-like operation to straighten the flows to more closely approximate OT (note that we are still working within the SMH here). The rationale for straighter flows is faster generation. (c) OT mini batches to learn OT over the whole model. MH: Octagauss? OT fields can cross as long as they do not cross at the same time. MD: why doesn't (c) do a quantitive analysis on the manifold? TS: Greedy vs stochastic OT. MD: Are there limitations when emerge when the problem is high dimensional? (Maybe). EZ: Non-gaussian sources? Varying the gaussian has an impact. Could you do transfer learning this way? TS: applications of greedy OT vs stochastic. MM: How robust is FM to changes in the source? Example application - denoising. JM: does the SMH make sense? Even if the objective is lower-dimension, is it strictly continuous? MH: This is why CNFs are cool(er than FM) because they don't assume the SMH.

2026.02.13 (Week 6 W26) DiffusionSat

Paper — DiffusionSat (2024)
Presenter — Michael Dushkoff
« Link to talk »
In attendance — Zac Cross; Michael Dushkoff; Said Efendiyev; Mathew Huerta-Enochian; Hritvik Jekki Venkateshwarulu; Manish Mathai; Joseph McLaughlin; Eric Zander.
Comments — Stable diffusion trained on clip with a control net. Would this generalize or is this paper just an impressive demonstration? Interesting enough to try in Michael's applied microscope tachography research.

More papers from an even less organized time.