Research
Making AI provably reliable
Neural network verification is where I've done hands-on research; the other directions are areas I'm building depth in through coursework and projects. No publication record yet — this is the road toward one.
Current focus
Certified robustness of neural networks
Research Intern · The Digital Research Center of Sfax (CRNS) · Jul 2026
- Evaluated the certified robustness of convolutional neural networks on the VNN-COMP CIFAR2020 benchmark with α,β-CROWN — 147 properties under L∞ perturbations (ε = 2/255 and 8/255).
- Reproduced the official competition verdicts with 93.2% agreement (137 / 147), including all 103 certified-safe (UNSAT) instances; divergences clustered near the 300 s timeout.
- Automated parsing and analysis of α,β-CROWN verification logs in Python, extracting verdicts, runtimes, and Branch-and-Bound statistics into structured reports.
- Investigated Branch-and-Bound behaviour and robustness under different perturbation budgets, and built a PySide6 GUI to drive verification experiments from one place.
Interests
- 01
Neural Network Verification
Formally bounding what a network can output for every input in a region: interval and symbolic bound propagation, linear relaxations, and Branch-and-Bound search.
Related work
- 02
Trustworthy AI
ML systems whose behaviour holds up under distribution shift and adversarial perturbation — backed by monitoring and guarantees rather than hope.
- 03
Machine Learning
Learning algorithms end to end, from classical models to transformers — rebuilt from scratch whenever that is the best way to understand them.
- 04
Statistical Learning
Hypothesis testing, inference, and the statistical foundations that turn model outputs into defensible conclusions.
- 05
Scientific Computing
Vectorized numerical methods in NumPy and SciPy — the machinery behind fast bound propagation, signal processing, and statistical tests.
Related work
- 06
Complex Systems
How global behaviour emerges from many interacting parts — networks, stochastic dynamics, and the graph-theoretic tools used to study them.