Area: Algebraic geometry, machine learning, and signal processing
Supervisors: Kathlén Kohn & Joakim Anden
University: KTH
Email address: vahidsha at kth dot se
Office: 7-th floor, LINDSTEDTSVÄGEN 25
Learning on a Razor's Edge: the Singularity Bias of Polynomial Neural Networks (arXiv preprint arXiv.2505.11846) with Giovanni Luca Marchetti and Kathlén Kohn
Algebra Unveils Deep Learning: An Invitation to Neuroalgebraic Geometry (ICML 2025, Spotlight), with Giovanni Luca Marchetti, Stefano Mereta, Matthew Trager and Kathlén Kohn
On the Geometry and Optimization of Polynomial Convolutional Networks (AISTATS 2024), with Giovanni Luca Marchetti and Kathlén Kohn
Moment Constraints and Phase Recovery for Multireference Alignment (arXiv preprint arXiv:2409.04868), with Emanuel Ström and Joakim Andén
Function Space and Critical Points of Linear Convolutional Networks (SIAM Journal on Applied Algebra and Geometry), with Kathlén Kohn, Guido Montúfar, and Matthew Trager
Geometry of Linear Neural Networks: Equivariance and Invariance under Permutation Groups (SIAM Journal on Matrix Analysis and Applications), with Kathlén Kohn, and Anna-Laura Sattelberger
Algebraic Complexity and Neurovariety of Linear Convolutional Networks (Acta Univ. Sapientiae Math)
New Approach to Character-Free Proof for Frobenius Theorem (Bachelor Project) with Seyedeh Fatemeh ARFAEE ZARANDI
Area: Algebraic geometry
Thesis: Local and global aspects of Deligne-Mumford moduli space of curves
University: EPFL
Modern Methods of Statistical Learning
Commutative Algebra and Algebraic Geometry
Groups and Rings
Machine Learning and Algebraic Geometry