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Curriculum vitae — click the icon above to download the latest PDF.

General Information

Full Name Alireza Abdollahpoorrostam
Position M.Sc. Researcher, EPFL
Affiliations Chair of Risk Analytics and Optimization (RAO); Signal Processing Laboratory (LTS4)
Location Lausanne, Switzerland
Email alireza.abdollahpoorrostam [at] epfl.ch
Languages Persian (native), English

Research Summary

  • I am drawn to the mathematical foundations of deep learning — why neural networks fail under adversarial perturbations and distribution shift, and what principled algorithms follow.
  • My research spans robustness, generalization, and post-training methods (model merging, model editing) for foundation models, with recent work on the optimal-transport geometry of adversarial training and distributionally robust optimization.
  • I aim to derive rigorous guarantees and translate them into deployable algorithms, with work appearing at NeurIPS, ICML, and ICLR.

Education

  • 2024 – Present
    M.Sc. in Communication Systems — Machine Learning track
    École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
    • Chair of Risk Analytics and Optimization (RAO), advised by Prof. Daniel Kuhn — optimal-transport geometry of adversarial training and distributionally robust learning.
    • Research Scholar at Signal Processing Laboratory (LTS4), advised by Prof. Pascal Frossard — robust fine-tuning and weight-space merging for vision–language foundation models.
  • 2019 – 2024
    B.Sc. in Electrical Engineering & Computer Science
    Amirkabir University of Technology (Tehran Polytechnic), Iran

Publications

  • Conference Papers
    • A. Abdollahpoorrostam, N. Dimitriadis, A. Hazimeh, P. Frossard. Model Soups Need Only One Ingredient. International Conference on Machine Learning (ICML 2026).
    • M. Salmani, A. Abdollahpoorrostam, S.-M. Moosavi-Dezfooli. A General Framework for Black-Box Attacks under Cost Asymmetry. International Conference on Learning Representations (ICLR 2026).
    • A. Abdollahpoorrostam, M. Abroshan, S.-M. Moosavi-Dezfooli. SuperDeepFool: A New Fast and Accurate Minimal Adversarial Attack. Advances in Neural Information Processing Systems (NeurIPS 2024).
  • Under Review
    • A. Abdollahpoorrostam, Ehsan Sharifian, Buse Şen, Marco Cuturi, Daniel Kuhn. Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning. Under review.
  • Workshop Papers & Preprints
    • A. Abdollahpoorrostam. In Search of the Successful Interpolation: On the Role of Sharpness in CLIP Generalization. NeurIPS 2024 AdvML-Frontiers Workshop.
    • A. Abdollahpoorrostam, A. Sanyal, S.-M. Moosavi-Dezfooli. Unveiling CLIP Dynamics: Linear Mode Connectivity and Generalization. ICML 2024 Foundation Models in the Wild Workshop.

Research Experience

  • Sept. 2025 – Present
    Graduate Researcher
    EPFL — Chair of Risk Analytics and Optimization (RAO)
    • Advisor: Prof. Daniel Kuhn, Lausanne, Switzerland.
    • Bridging optimal transport and adversarial training: OT-geometric formulations of adversarial training via Brenier maps, toward principled, provably robust learning algorithms.
    • Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning (with Ehsan Sharifian, Buse Şen, Marco Cuturi, Daniel Kuhn) — under review.
  • Sept. 2024 – Aug. 2025
    Graduate Researcher
    EPFL — Signal Processing Laboratory (LTS4)
    • Advisor: Prof. Pascal Frossard, Lausanne, Switzerland.
    • Published MonoSoup, a hyperparameter-free, data-free post-hoc method that recovers Model-Soup-level out-of-distribution accuracy from a single fine-tuned checkpoint by applying SVD to per-layer updates and re-weighting high-/low-energy directions; reduces ensembling cost from $\mathcal{O}(K)$ to $\mathcal{O}(1)$. (ICML 2026)
  • 2022 – 2025
    External Research Collaborator
    Trustworthy ML Collaboration with Dr. S.-M. Moosavi-Dezfooli
    • Remote / Tehran / Lausanne.
    • Co-developed SuperDeepFool, a minimal-$\ell_2$ adversarial attack with a geometric reformulation of the DeepFool iteration. (NeurIPS 2024)
    • Asymmetric-cost black-box attack, designing Asymmetric Search and Asymmetric Gradient Estimation (AGREST) algorithms for black-box settings. (ICLR 2026)
    • Concurrent collaboration with Dr. Amartya Sanyal (U. Copenhagen) on linear mode connectivity of robust fine-tuning. (ICML 2024 FM-Wild Workshop)

Academic Service

  • Reviewer — NeurIPS, ICML, ICLR

Technical Skills

Programming Python, C/C++, Bash, MATLAB, LaTeX
ML Frameworks PyTorch, JAX
Languages Persian (native), English

Research Interests

  • Adversarial robustness
    • Minimal-norm and geometry-aware adversarial attacks
    • Certified defenses and provable robustness
  • Distributionally robust optimization & optimal transport
    • Brenier-map formulations of adversarial training
    • Wasserstein-DRO for deep learning
  • Foundation models & post-training
    • Model merging, model soups, weight-space ensembling
    • Robust fine-tuning of vision–language models (CLIP)
    • Linear mode connectivity and the loss landscape