Progress Meeting

Patrick Altmeyer

Delft University of Technology

Arie van Deursen
Cynthia C. S. Liem

July 26, 2025

Agenda

  • Stocktake—what has been accomplished and where are we at?
  • Next Steps—plans for the 3-4 months
  • Big Picture—putting it all together and some tentative career plans
  • Housekeeping—meeting series, …

Stocktake

Papers

Solid progress on chapters 3 to 5 of the thesis:

  • 2 papers published (ECCCo @ AAAI: [Ch3], Stop Making Unscientific AGI Claims @ ICML: [Ch5]).
  • 1 paper in progress (CE and Adversarial Robustness @ ICML/NeurIPS/workshops ❓: [Ch4.1])
  • 1 paper initiated (Counterfactual Training @ ICML ❓ (timing’s good): [Ch4.2])
  • 1 paper (extended abstract) submitted (LaplaceRedux.jl @ JuliaCon)

Talks and Posters

A total of 12 talks and posters

  • Academia (4): AAAI (poster), ECONDAT, Imperial College London, ICML (poster)
  • Companies (3): De Nederlandsche Bank, The Alan Turing Institute, TÜV AI Lab (next week)
  • Software (5): JuliaCon (4 talks), Julia on HPC tutorial @ TU Delft

Open Source and Science

  • Unified ecosystem for Trustworthy AI in Julia, Taija.
    • Continuous maintenance and development, in particular CE.jl, LR.jl and CP.jl.
    • Work on various base and meta packages for parallelization, visualization and more.
  • Published 7 blog posts: personal (4), Taija (3)
  • Mentored two Google/Julia Summer of Code students who contributed to Taija: (1) causal recourse, (2) conformal Bayes.

Graduate School

  • 1 Research project (4 ECTS: research).
  • 1 Software project (2 ECTS: research).
  • 4 Master’s theses (4 ECTS: research)
  • NeurIPS 2024 reviewing (3 ECTS: research).
  • JuliaCon, ICML, AAAI, ECONDAT (2 ECTS each: research).
  • 2-week Dutch course (4 ECTS: transferable).

Total (missing): research (-21.5 ⚠️), discipline (5), transferable (6)

Time Management (1)

  • Last year I thought it should be feasible to have the following papers in (near-) final form by now: ECCCo [Ch3] ✅, LaplaceRedux [not planned as chapter] ✅, ConformalPrediction ❌, 3rd research paper [Ch5] ✅.
  • At that point, I thought we’d be looking at a comprehensive thesis: JuliaCon paper(s) forming the introduction and 3 research papers forming the core.
    • Still missing work on imroving models 🟨🟨🟨🟨⬛

Time Management (2)

4th year can then be used to:

  1. Tackle 4th research paper and collate chapters (thesis).
  2. Ensure Taija and Graduate School credits are in order and next career steps are planned.
  3. Google Summer of Code ✅? Teaching ✅? Research Visit? Internship? Book about Taija?
  4. Write paper on JointEnergyModels.jl.

Next Steps

CE and Adversial Robustness

Promising results but …

  • Need more granular grid for gradient-based adversarial training and additional datasets.
  • Need to test other forms of adversarial robustness (e.g. LBDN), because
    • Link above has been established to some degree.
    • Link above is not surprising, because AE and CE are virtually identical.

@ ICML 2025, NeurIPS Data & Bmks 2025, workshops, ❓

Counterfactual Training

AE ⊆ CE

Adversarial Examples (AE) can be thought of as a specific type of CE. Why not use CE in a similar fashion during training?

  • Literature review ✅
  • Proof-of-concept ✅
  • Exploration phase ⏳ (until mid-November)
  • Run experiments 🔲 (mid-Novermber to early January)
  • Wrap up 🔲 (January)

❓ Aiming for ICML but in 2024 there was little on CE.

Big Picture

Thesis Structure

Chapter 1

Introduction

  1. Broadly: Taija—Trustworthy AI in Julia (reference relevant blog posts, NAACL and student papers.)
  2. Focus: Counterfactual Explanations (JCon Proc) ✅

Thesis Structure

Chapter 2

Algorithmic Recourse in Practice

  1. Focus: Endogenous Macrodynamics in Algorithmic Recourse (SaTML) ✅
  2. Transition: If not minimal costs, then what? (integrate blog post on REVISE)

Thesis Structure

Chapter 3

Faithfulness first, Plausibility second

  1. Focus: Faithful Model Explanations Through Energy-Constrained Conformal Counterfactuals (AAAI) ✅
  2. Intermezzo: Assessing the endogenous macrodynamics caused by ECCCo

Thesis Structure

Chapter 4

Learning Plausible Explanations

  1. Transition: Adversarial Training and Counterfactual Explanations ⏳
  2. Focus: Counterfactual Training ⏳

Thesis Structure

Chapter 5

Trustworthy AI in Times of LLMs

  1. Intermezzo: Stop Making Unscientific AGI Claims (ICML) ✅
  2. Discussion: Can AI be Trustworthy? (reference works by Karol and Aleks)

Thesis Structure

Chapter 6

Causality? Global CE?

Some ideas:

  1. Causality: Causal Abstraction & ECCCo
  2. Global CE: extend ideas presented in T-CREx (Bewley et al. 2024).

Summer 2025

  • Connected with Tom Bewley from J.P. Morgan AI Research at ICML and implemented his paper.
    • Novel approach to generate global counterfactual rule explanations.
    • Motivated me to apply to Summer Associate Program (could work on extension [Ch6]), although I was aiming to defend in September.
  • Alternatively, hoping for HuggingFace to offer internships again but nothing yet.

Beyond PhD

In an ideal world …

  • Pretty set on either Amsterdam or Düsseldorf.
  • Leaning towards industry, but remain curious about opportunities in academia.
  • Desire to continue with Julia, ideally Taija.
  • Continue developing and implementing interesting research ideas in software, add application and deployment (research engineer?).
  • Curious to explore subject areas outside of my PhD but maintain focus on “AI for Good”.

Industry

  • J.P. Morgan AI Research
  • HuggingFace
  • EU AI Office
  • Julia shops: LazyDynamics, EvoVest

If industry, ideally combine my backgrounds in economics and AI.

Academia

  • Potentially, post-doc at Delft/VU/UvA with interesting external partner, i.e. any of the companies on previous slide or:
    • EScience Centre
    • DNB (?)
  • Assistant professor at Delft (EWI/TPM), VU, UvA

Housekeeping

References

Bewley, Tom, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, and Manuela Veloso. 2024. “Counterfactual Metarules for Local and Global Recourse.” In Proceedings of the 41st International Conference on Machine Learning, edited by Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, and Felix Berkenkamp, 235:3707–24. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v235/bewley24a.html.