Mohammad Fereydounian

Mohammad Fereydounian

Ph.D. Student

ESE Department

University of Pennsylvania


I received my Ph.D. from the University of Pennsylvania’s Electrical and Systems Engineering (ESE) Department under the supervision of Prof. Hamed Hassani in December 2023. Additionally, I earned a Master’s degree in Statistics from The Wharton School, University of Pennsylvania, in December 2021. Before joining Penn, I obtained an M.Sc. degree in Pure Mathematics and two B.Sc. degrees in Pure Mathematics and Electrical Engineering, all from the Sharif University of Technology in Tehran, Iran.

I am deeply passionate about mathematics and have dedicated my academic journey to its pursuit. Throughout my undergraduate and graduate studies, I had the opportunity to engage in a wide array of mathematical disciplines, both through coursework and self-study, reaching advanced levels in various areas. These include but are not limited to:

  • Graph Theory
  • Number Theory
  • Combinatorics
  • Abstract Algebra: Group Theory, Ring Theory, Field Theory, Module Theory, Galois Theory, Hypercomplex Algebra
  • Operator Theory and Linear Algebra
  • Probability and Measure Theory
  • Topology
  • Manifolds and Differential Geometry
  • Fourier Analysis, Complex Analysis, Mathematical Analysis, Functional Analysis
  • Ordinary and Partial Differential Equations
  • Logic, Set Theory, and Foundations of Mathematics

Additionally, I delved into applied mathematical domains such as Optimization, Mathematical Statistics, Optimal Transport, Coding and Information Theory, and Numerical Analysis.

Initially drawn to pursuing a Ph.D. in pure mathematics, I recognized a significant disparity between theoretical advancements and practical needs in fields like machine learning. Motivated to bridge this gap, I shifted my focus towards applied areas of mathematics, aiming to connect theoretical concepts with real-world problems. This transition led me to explore diverse fields such as Communications, Coding and Information Theory, Optimization, Network Privacy, Artificial Intelligence, and Graph Neural Networks, where I made meaningful contributions.

Currently, my focus lies in delving into the theoretical aspects of Graph Neural Networks and pioneering analytical techniques within the realm of graphs.


  • Graph Neural Networks
  • Optimal Transport
  • Machine Learning
  • Optimization
  • Information and Coding Theory


  • Ph.D. in Electrical and Systems Engineering, 2023

    University of Pennsylvania

  • M.A. in Statistics, 2021

    The Wharton School, University of Pennsylvania

  • M.Sc. in Pure Mathematics, 2016

    Sharif University of Technology, Tehran, Iran

  • B.Sc. in Pure Mathematics, 2014

    Sharif University of Technology, Tehran, Iran

  • B.Sc. in Electrical Engineering, 2014

    Sharif University of Technology, Tehran, Iran


Graduate Fellowship for Teaching Excellence

CTL Graduate Fellows are nominated for their teaching excellence by their departments and then selected by CTL from nominees across the university. The fellowship includes a $6,000 stipend and CTL Graduate Fellows are responsible for organizing workshops for graduate students across the university, consulting with and observing other TAs, and contributing to other activities to help graduate students develop as teachers.

Certificate in College and University Teaching

The CTL Teaching Certificate offers a structure through which interested graduate students can prepare themselves to become faculty in the future. The certificate is noted on the student’s transcript, as a statement from the University of Pennsylvania that a graduate student has pursued advanced training in teaching.

Best Teaching Assistant Award for A Doctoral Student

This award is granted in recognition of an exeptional performance as a teaching assistance among the PhD students of ESE department.

The Solomon M. Swaab Endowment Fund

The Solomon M. Swaab Endowment Fund includes a $2000 stipend and is awarded in recognition of impressive achievements of a student which ESE faculty find to be exceptional among the already exceptional students to whom Dean’s Fellowships is offered.

The Dean’s Fellowship for The Graduate Study

This fellowship is worth $67000 per year and is awarded to ESE PhD students in recognition of their exceptional performance and potential for continued high achievement in graduate work.

Guest Lecturer

Reinforcement Learning (ESE 680-005)

Taught 1 case study lecture on Wasserstein metric and its connection to reinforcement learning.

Statistical Learning (ESE 542)

Taught 3 lectures on the statistical analysis of multi-dimensional regression.

Machine Learning (CIS 520)

Taught 2 tutorial sessions on convex optimization review.

Linear Systems Theory (ESE 500)

Taught 4 lectures on the stability of linear systems, the MMSE analysis for linear systems, and Kalman filters.

Galois Theory (Abstract Algebra III)

Taught 2 lectures on Galois field extensions.


Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach

Low-complexity decoding of a class of Reed-Muller subcodes for low-capacity channels

The exact class of graph functions generated by graph neural networks

Safe Learning under Uncertain Objectives and Constraints

Hidden Information, Teamwork, and Prediction in Trick-Taking Card Games

Non-asymptotic Coded Slotted ALOHA

Channel Coding at Low Capacity

Twin Edge Coloring of Graphs