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Name: Behnam Roshanfekr

Type: User

Behnam Roshanfekr

About Me

I am a PhD candidate in the department of Computer Engineering at Amirkabir University of Technology (Tehran Polytechnic) where I received my M.Sc. in 2017. My research field of academia is Graph Signal Processing. In my free time, I thoroughly enjoy playing chess.

Education

Amirkabir University of Technology (Tehran Polytechnic), Ph.D. in Artificial Intelligence, Sep.2017 - Present
Amirkabir University of Technology (Tehran Polytechnic), M.Sc. in Artificial Intelligence, Sep.2014 – Feb.2017
Ferdowsi University of Mashhad, B.Sc. in Computer Engineering, Software, Sep.2010 – Sep.2014

Publications

B. Roshanfekr, S. Khadivi, and M. Rahmati, "Sentiment analysis using deep learning on Persian texts." Iranian Conference on Electrical Engineering (ICEE) on IEEE, pp. 1503-1508, 2017.
S. Mohtaj, B. Roshanfekr, A. Zafarian, and H. Asghari, “Parsivar: A Language Processing Toolkit for Persian”, LREC, 2018.
B. Roshanfekr, M. Amirmazlaghani, and M. Rahmati. "Learning graph from graph signals: An approach based on sensitivity analysis over a deep learning framework." Knowledge-Based Systems 260 (2023): 110159.
A. Amouzad, Z. Dehghanian, S. Saravani, M. Amirmazlaghani, and B. Roshanfekr. "Graph isomorphism U-Net." Expert Systems with Applications 236 (2024): 121280.

Projects

Parsivar: A Language Processing Toolkit for Persian
- Parsivar is a Python library for preprocessing Persian texts. This toolkit performs various kinds of activities comprised of normalization, space correction, tokenization, stemming, parts of speech tagging and shallow parsing.

Skills

Programming Languages: Python, C/C++, Java, Matlab
Deep learning frameworks: Tensorflow, Keras, Pytorch
Tools & Software: PostgreSQL/MySQL, Apache spark

Interests

Graph Signal Processing
Deep Learning
Machine Learning

Contact

Behnam Roshanfekr's Projects

active-semi-supervised-learning-using-sampling-theory-for-graph-signals

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

attention_is_all_you_need

autoencoders-variants

Pytorch implementations of various types of autoencoders

awesome-deep-community-detection

Deep and conventional community detection related papers, implementations, datasets, and tools.

awesome-multi-view-clustering

collections for advanced, novel multi-view clustering methods(papers , codes and datasets)

bert-pytorch

Google AI 2018 BERT pytorch implementation

bert_experimental

code and supplementary materials for a series of Medium articles about the BERT model

bgcn

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

boostan

استایل لاتک برای کتاب، پایان نامه، گزارش

broshanfekr

captum

Model interpretability and understanding for PyTorch

causal_discovery_toolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

char_based_cnn

courses

This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)

deep-learning-with-pytorch-1.x

Deep Learning with PyTorch 1.x, published by Packt

deepscm

Repository for Deep Structural Causal Models for Tractable Counterfactual Inference

doc2vec

es_rnn

The repository contains current, slightly updated, version of ES_RNN - a hybrid Exponential Smoothing/Recurrent NN method that won M4 Forecasting Competition