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A Summary of basic related-information about me

Basics

Name Behzad Nourani
Label AI Researcher
Email behzadnourani.ai@gmail.com
Phone -
Url https://behzadnouranikoliji.github.io
Summary -

Work

  • 2021 - 2025
    Machine Learning Researcher
    Tübingen AI Center
    Performing research in Machine Learning. Designing novel algorithms that solve real-life industrial problems and outperform state-of-the-art in various realistic scenarios.
    • Bandit Algorithms on Graphs, Multi-Agent and Network Systems, AutoML, Tabular Foundation Models
  • 2019 - 2021
    Machine Learning Research Engineer
    DIET department of Sapienza University of Rome and Signal Processing Laboratory (LTS4) of EPFL
    Performing research in Signal Processing for Big Data. Designing novel algorithms for the analysis of the data of Financial Markets as well as the data of Epidemics.
    • Multi-Variate Time Series Models, Causal Structures, Graph Structure Learning from Streaming Data, Time-Varying Graphs, Regularization, and Optimization
  • 05.2022 - 09.2022
    Lecturer
    University of Tübingen
    We instructed the participants on how to design a recommender system using multi-armed bandits frameworks. We managed 2 teams, with 5 students in each team. We used the Scrum framework to manage the corresponding projects.
    • Title: Bandit algorithms for designing recommender systems

Education

  • 2017 - 2020

    Rome, Italy

    Master's degree
    Sapienza University of Rome, Rome, Italy
    Engineering in Computer Science
    • Database Management Systems (SQL)
    • Knowledge Representation and Semantic Technologies (SPARQL,Protégé)
    • Software Engineering (Web Services)
    • Information Retrieval (MapReduce, Apache Spark)
    • Visual Analytics (Tableau)

Certificates

German Language
Goethe Institute
Docker
Docker Certified Associate
Amazon Web Services
Amazon Web Services

Publications

  • 2024.10.10
    Clusters Agnostic Network Lasso Bandits
    ICLR-2025
    This is a novel framework that can be used in the design and analysis of large-scale online recommender systems. The framework identifies clusters of nodes in a network of users/items and finds a solution for each cluster separately. It does not require any prior knowledge of the underlying graph that connects the users/items.
  • 2024.09.30
    Online Influence Maximization with Semi-Bandit Feedback under Corruptions
    Arxiv
    This is a novel framework that can be used in online marketing, influence maximization, and online recommender system design and analysis. The framework identifies the most influential nodes in a network to spread a signal while considering that some nodes in the network might be contributing to the generation of a certain amount of adversarial data.
  • 2023.09.30
    Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards
    European Conference on Artificial Intelligence (ECAI) 2023
    This is a novel framework that can be used for the design and analysis of recommender systems in nonstationary environments as well as other real-world applications where the causal system in the data generation process changes in time. The framework models a time-varying causal system. In the design of the framework, we used Change Point Detectors to track nonstationarity of the data.
  • 2022.07.23
    Linear Combinatorial Semi-Bandit with Causally Related Rewards
    International Joint Conferences on Artificial Intelligence (IJCAI) 2022
    This is a novel framework that can be used for the analysis and design of online recommender systems as well as the analysis of data of epidemics. The framework relies on Structural Equation Modelling to model a causal system. The goal is to find places in the system that make the utiliy signal maximized.

Skills

Machine Learning
Reinforcement Learning
AutoML
Foundation Models
Tabular Data
Statistical Inference
Algorithm Design
Graph Signal Processing
Python (PyTorch, TensorFlow, scikit-learn, Numpy, Pandas, Matplotlib, CVXPY, NetworkX), C++, MATLAB

Languages

German
B2
English
C2

Interests

Machine Learning/Deep Learning
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References

Doctor Claire Vernade
Professor Sergio Barbarossa

Projects