diff --git a/projects/interpretable-ml.yml b/projects/interpretable-ml.yml
new file mode 100644
index 0000000..a8d20c7
--- /dev/null
+++ b/projects/interpretable-ml.yml
@@ -0,0 +1,51 @@
+---
+name: Towards Interpretable Machine Learning in High-Energy Physics
+postdate: 2026-03-03
+categories:
+  - ML/AI
+durations:
+  - Any
+experiments:
+  - Any
+skillset:
+  - Python
+  - ML
+status:
+  - Available
+project:
+  - Any
+location:
+  - Any
+commitment:
+  - Any
+program:
+  - Any
+shortdescription: Survey interpretability techniques for ML models used in HEP, and propose practical guidelines for the field.
+description: >
+  Machine learning is now everywhere in particle physics — from identifying what kind of particle
+  created a jet, to filtering interesting collisions in real time, to generating simulated data.
+  These models work impressively well, but we often have little idea why they make the decisions
+  they do.
+
+  A growing toolbox of interpretability methods exists — techniques like SHAP values and attention
+  maps can highlight which inputs matter most, feature importance rankings from decision trees can
+  reveal what a model has learned. A [Nature Reviews Physics commentary](https://doi.org/10.1038/s42254-022-00456-0) argued
+  that interpretability is essential for ML in physics, yet there is no agreed-upon standard for
+  what "interpretable" even means in this context, let alone best practices for achieving it.
+
+  In this project, you will survey and hands-on compare interpretability methods across different
+  ML tasks in high-energy physics. Starting from existing trained models, e.g. jet
+  classifiers, you will apply post-hoc explanation tools (such as
+  SHAP and attention visualisation), compare them against other alternatives, and ask: do these methods agree? Do they
+  reveal real physics? Can we reverse-engineer what a model has
+  learned and express it in terms a physicist would recognise?
+
+  The main deliverable will be twofold — firstly a practical set of guidelines: when should HEP physicists use which
+  interpretability approach, what are the pitfalls, and where are the open problems? Secondly: an open source repository of
+  tools that can be used to understand ML models.
+
+contacts:
+  - name: Liv Våge
+    email: liv.helen.vage@cern.ch
+
+mentees:
diff --git a/projects/logic-gate-nn.yml b/projects/logic-gate-nn.yml
new file mode 100644
index 0000000..0956f41
--- /dev/null
+++ b/projects/logic-gate-nn.yml
@@ -0,0 +1,55 @@
+---
+name: Logic Gate Neural Networks for Jet Substructure Classification
+postdate: 2026-03-03
+categories:
+  - ML/AI
+durations:
+  - Any
+experiments:
+  - Any
+skillset:
+  - Python
+  - ML
+status:
+  - Available
+project:
+  - IRIS-HEP
+location:
+  - Any
+commitment:
+  - Any
+program:
+  - IRIS-HEP fellow
+shortdescription: Build ultra-fast jet classifiers with differentiable logic gate networks
+description: >
+  At the Large Hadron Collider (LHC), protons smash together billions of times per second,
+  producing sprays of particles called "jets." Figuring out what created each jet is one of the most
+  important classification problems in particle physics. Today's best classifiers use large neural networks,
+  but the LHC's trigger system needs to make decisions in under a microsecond on specialised hardware chips called FPGAs. These chips
+  are built from simple logic gates — tiny circuits that compute basic Boolean operations like AND,
+  OR, and XOR.
+
+  So what if we built a neural network entirely out of those same logic gates? That's exactly what
+  differentiable logic gate networks do. Instead of multiplying numbers through layers of neurons,
+  they wire together simple Boolean operations and learn which gate to use at each position. A
+  [NeurIPS 2022 paper](https://arxiv.org/abs/2210.08277) showed how to train these networks with
+  standard gradient descent — and the results are staggering: they are among the fastest machine
+  learning models, capable of classifying over a million images per second on a single
+  CPU core. This idea has already been applied to jet classification as a benchmark task, alongside
+  related approaches like [LogicNets and PolyLUT](https://arxiv.org/abs/2506.07367), and real-time
+  jet classification on trigger hardware has been [demonstrated with latencies around 100 ns](https://doi.org/10.1088/2632-2153/ad5f10).
+
+  In this project you will work with the [HLS4ML jet tagging dataset](https://zenodo.org/records/3602260), which contains simulated LHC jets across five classes
+  (gluon, light quark, W, Z, and top). Using our library [torchlogix])https://github.com/ligerlac/torchlogix) you'll build a logic gate network classifier
+  , benchmark it against conventional neural network baselines, and explore how accuracy
+  trades off against network size and depth. Stretch goals include comparing against other
+  logic-gate-based approaches or exploring what it takes to get these models running on real
+  hardware.
+
+contacts:
+  - name: Liv Våge
+    email: liv.helen.vage@cern.ch
+  - name: Lino Gerlach
+    email: lino.oscar.gerlach@cern.ch
+
+mentees: