My Skills

Industry Knowledge

Machine Learning
70%

Software Engineering
85%

Operating Systems
70%

Programming Languages

Python
90%

C++
70%

C
70%

Java
80%

Java Script
60%

HTML
70%

CSS
60%

Verilog
50%

PHP
50%

VHDL
40%

Frameworks & Libraries

Django
80%

React.js
70%

Numpy & Pandas
75%

Pytorch
60%

Other Technologies

MySQL
70%

Git
80%

Maven
65%

Docker
60%

Kubernetes
50%

Linux
80%

Interpersonal skills

Teamworking
90%

Self-Learning
90%

Teaching
80%

Problem-Solving
75%

Honors

Ranked in the Top 0.1%

In Iranian University Entrance Exam among 148,000 participants

Bachelor Scholarship

Awarded a 4-year full scholarship from University of Tehran

Ranked

Among Top students in Computer Engineering Field

Iranian Mathematics Olympiad

Accepted in first round of Iranian Mathematics Olympiad

Academic Projects

All
Compilers
Machine Learning
Web Application
Operating Systems

Compilers & Code Generation

Siphia Compiler

Sophia Language

Project Description

Sophia is an object-oriented Java-like programming language which supports inheritance. Sophia compiler has been implemented in four phases using Java in IntelliJ IDEA.

It has been developed as a student project for Compiler Design and Programming Languages course.

Phase I, Lexical and Syntax Analyzer: Sophia's grammar was implemented using ANTLR and as well as a parser to create abstract syntax tree (AST) of input program.

Phase II, Name Analyzer: The name analyzer of the language was implemented to gather information about classes, fields, methods, local variables, and scopes of program and finding name analysis errors like undefined variables or redefinition.

Phase III, Type Analyzer: A type checker was implemented to check the types of variables in input program, finding relevant errors based on Sophia's documentation and setting initial value for various types.

Phase IV, Code Generation: The machine code was generated by converting Sophia's code to jasmin as a middle layer. Jasmin assembler converts its codes to class files which can be executed by JVM later.

Machine Learning

Neural Network with Pytorch

Project Description

The Neural Network Introduction to NN and image classification with NN in Pytorch using Google Colab to analyze different variables and methods and their effects on neural networks' performance. These are consisted of learning rate, batch size, activation function, momentum, number of epoch, regularization, etc.

The implemented NN has to do image classification, learn features of a dataset consisted of pictures from 52 different types of accessories, and predict new pictures. It has been developed as a student project for Artificial Intelligence course.

Machine Learning

Application of SciKit-Learn

Machine Learning with SciKit-Learn Library

Project Description

Various machine-learning techniques Introduction to Scikit-learn. The problem was predicting customer satisfaction and predict whether they come back later to that market or not based on some information. First I cleaned data, encode categorical columns and normalize data. Then I used some models like KNN, Desicion Tree and Logistic Classifier (and tune hyperparameters) and compared them. Then I tried Bagging, Random Forest and Hard-Voting Classifier and compared them.

Various pre-processing techniques was applied to the dataset as well as using different classifiers such as decision tree, KNN, random forests, etc. It has been developed as a student project for Artificial Intelligence course.

Machine Learning

Bayesian Network

Text Processing using Bayesian Network

Project Description

A Bayesian Network Introduction to NLP. Using bayesian and bag of word to predict category of news from it's description. First I preprocessed data and then predict news category using bayesian rule and BOW.

Practical techniques like bag of words were used to obtain more accurate result. It has been developed as a student project for Artificial Intelligence course.

Machine Learning

Genetic Algorithm

Replacement Decoding using Genetic Algorithm

Project Description

A Genetic Algorithm Learning and implemeting Genetic Algorithm tools and functions to Decrypting Substitution Ciphers with Genetic Algorithms.

The algorithm uses a given dictionary as its reference for acceptable words. Mutation and cross-over were implemented as fast and effective as possible, as well as the application of practical techniques like eliminating stop words to obtain more accurate result. It has been developed as a student project for Artificial Intelligence course.

Machine Learning

Price Estimation

Price Estimation with Regression

Project Description

A Regression Model Predicting phone price based on information in phone advertising. Our data set was a collection of phone advertising containing phone_brand, title, description, etc. I preprocessed data, extracting new features from advertising descriptions using NLP tools, encoding categorical columns, etc. Finally use some models for predicting phone prices such as LinearRegression, TreeRegression, and lightgbm. I also tune hyperparameters for each model to achieve an optimized model.

As well as bag of words, various techniques were used to preprocess text messages. Pre-processing was the critical part of the projects, since text data was consisted of both English and spoken Persian words with spelling problems. It has been developed as a student project for Artificial Intelligence course.

Machine Learning

Search Problems

Informed and Uninformed Search Algorithm

Project Description

Various search algorithms Implementing informed and uninformed search algorithms from scratch in python.

Uninformed search algorithms like BFS and DFS as well as informed one like A* were implemented. I also compared their performance based on problem. It has been developed as a student project for Artificial Intelligence course.

Web Application

Bolbolestan

Bolbolestan, students portal and enrolling for classes

Project Description

Bolbolestan is a web application for students portal and enrolling for classes procedure. It has been developed during 8 phases as a student project for Internet Engineering course.

Loghme's Back end : Implemented with Java in IntelliJ IDEA using various tools and technologies such as Maven, Apache Tomcat, Spring MVC, MySQL as database, etc. and deployed using Kubernetes and Docker.

Loghme's Front end : Implemented using HTML, CSS, JavaScript, React.js, Nginx (with Kubernetes and Docker for deployment), etc.

Operating Systems

XV6 Operating System

Working woth XV6

Project Description

XV6 is a teaching operating system which was edited and improved as a student project for Operating Systems course. Having implemented various features, I have been familiar with technical aspects of operating systems.

Introduction to Xv6 OS: I got familiar with Xv6, its execution, debugging, improving its console by adding some features, and implementing new program to copy text data from a file to another one.

XV6 System Calls: Several new system calls were implemented and added to the OS which enable XV6 to run commands out of the directory in which their execution file exists, sleep a process for an arbitrary period using Xv6's ticks, and obtain current process's pid and its children and grandchildren.

Process Scheduling: A multilevel feedback queue scheduler (MFQ) was implemented which includes lottery, HRRN, and SRPF methods as its scheduling levels in Xv6.

Process Synchronization Mechanisms: Two synchronization mechanisms were added to prevent out-of-order execution of processes, and use mutex more than once in recursive programs.

Operating Systems

Monitoring

Solving a Simulated Problem using Monitors

Project Description

Monitors are exploited to achieve process synchronization in operating system. Having used its feature, I tried to solve a simulated traffic problem to minimize the air pollution caused by cars. This means that by receiving information about cars' pollution and a map of the city as well as using monitors, the program tries to find the best schedule for cars to move across the streets. The monitors are implemented from scratch.

It has been developed as a student project for Operating Systems course.