CV

General Information

Full Name Amir Joudaki
Tagline Mathematical understanding of deep neural networks
Location Zurich, SWITZERLAND
Languages English, Persian, German (A2)

Experience

  • Nov 2024 – now
    Postdoctoral Researcher
    ETH Zurich
    • Researching how deep nonlinear models learn by investigating module interactions, forward passes, backward gradients, and the evolution of neural kernels.

Education

  • Feb 2017 – 2024
    Direct Ph.D. (Msc + Ph.D.) in Artificial Intelligence
    ETH Zurich
    • Highly selective program (<5% admissions) focused on the mathematical foundations of deep neural networks and AI for biomedicine.
    • PhD Thesis: 'On a mathematical understanding of deep neural networks'.
    • Authored 8 papers (6 first-authored) in top-tier venues; supervised over 10 MSc projects.
    • Supervisors: Gunnar Ratsch & Francis Bach.
  • Feb 2014 – Jan 2017
    M.Sc. in Cognitive Neuroscience
    International School for Advanced Studies (SISSA)
    • Thesis: 'Modeling activity of electrophysiological recordings in vivo in rats'.
  • Sept 2008 – Sept 2011
    B.Sc. in Computer Engineering
    Sharif University of Technology

Awards

  • 2017
    • Direct Doctorate Fellowship (selected as 1 of 2 out of >100 candidates for ETH Zurich’s direct PhD program)
  • 2011
    • Ranked 42nd in the National Higher Education Entrance Exam (among 50,000 participants)
  • 2007
    • Ranked 369th in the National University Entrance Exam (among 400,000 participants)

Publications

  • Mathematical Foundations of AI
    • Emergence of globally attracting fixed points in deep neural networks with nonlinear activations (AISTATS, poster)
    • Batch normalization without gradient explosion: Towards training without depth limits (ICLR 2024, poster)
    • On the impact of activation and normalization in obtaining isometric embeddings at initialization (NeurIPS 2023, poster)
    • On Bridging the Gap between Mean Field and Finite Width in Deep Random Neural Networks with Batch Normalization (ICML 2023, poster)
    • Batch Normalization Orthogonalizes Representations in Deep Random Networks (spotlighted NeurIPS 2021, top 3% submissions)
    • PCA Subspaces Are Not Always Optimal for Bayesian Learning (NeurIPS 2021 workshop, DistShift)
  • Genomic Sequence Analysis
    • Identifying Biological Priors and Structure in Single-Cell Foundation Models (ICML workshop 2024)
    • Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs (NeurIPS workshop 2023)
    • Aligning distant sequences to graphs using long seed sketches (Journal of Genome Research 2023)
    • Fast Alignment-Free Similarity Estimation By Tensor Sketching (RECOMB 2021)
    • Sparse Binary Relation Representations for Genome Graph (Journal of Computational Biology 27.4, 2020)
  • Dimensionality Reduction
    • Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping (IEEE TPAMI 38(7), 1452–1464)
  • Functional Brain Networks Analysis
    • Properties of functional brain networks affect frequency of psychogenic non-epileptic seizures (Frontiers in Human Neuroscience, 6:335)
    • EEG-based functional brain networks: does the network size matter? (PLoS ONE 7(4): e35673)