Hi, I'm Asal 👋

(pronounced æsæl, means honey🍯 in Farsi)

I currently work as a backend developer at Certn. I love to code and here are the things I crave for the most being a developer:

  • that satisfying feeling of knowing the users love something I've built 🥰👩‍💻
  • writing code that reads so nice and clean! 🧼🪨
  • that "aha" moment after wrestling with a stubborn bug 🪲🔫
  • when them tests pass after a long day of debugging!âś…
  • finally "getting it" when I learn something new and if first seems so difficult 🤓, and then I build something great with my new skill 🔧🧰

Skills

Languages and Frameworks
  • Python
    • Django
    • FastAPI
    • Flask(-RESTPlus)
  • Java
    • Spring Boot
  • Javascrip
    • Vue.js
    • React.js
  • SQL
Technologies, Tools and Libraries
  • RDBMS
    • Postgres
    • MySQL
  • NoSQL
    • Elasticsearch (ELK stack)
    • Redis
    • Neo4j
    • MongoDB
  • Cloud
    • AWS

  • Visualization
    • D3.js
    • Plotly and Dash
  • Machine Learning
    • Pytorch
    • Scikit-learn
  • Airflow
  • Docker

Resume

Experiencelogo

Intermediate Backend Developer

May 2022 – Present

Certn, Vancouver, Canada

  • Team Crim and Team Canada (previous teams)
    • Collaborated with the Product and UX teams to deliver feature requests
    • Built the OneID product for API customers using the latest ID verification vendor at the time, leading to ~33% automation improvement
  • Team Site Resilience Engineering (SRE) (current team)
    • Increased the security of the internal tools using OAuth 2.0 and Okta
    • Increasing the system reliability by resolving failures
    • Collaborating on performance optimization projects with the SRE Team
Python Django FastAPI Docker OpenSearch AWS SQS S3 Celery React.js Okta

Full Stack Engineer

Jul. 2021 – May 2022

SceneBox (Acquired by Applied Intuition), Vancouver, Canada

  • Increased user productivity by integrating data labelling solutions into the company's data-ops platform
  • Ensured data availability and flexible data schema by configuring data backup and migration workflows
  • Reduced response time of the platform by ~30% via conducting scalability tests, API profiling, and caching
  • Enhanced the dashboards with new features for a more feature-rich UI and improved UX
Python React Elasticsearch Kibana Docker Airflow Redis

Research Assistant

Sept. 2019 – May 2021

DeepSense, Dalhousie University, Halifax, Canada

  • Built a visual analytics framework for data mining and interactive visualization that helps airport staff with:
    • Discovering hidden delay-related patterns in aviation ground handling logs
    • Using the patterns to address performance issues at airports
Python SPMF Flask Dash Javascript

Teaching Assistant

Jan. 2020 – Dec. 2020

Dalhousie University, Halifax, Canada

Visual Analytics
  • Recorded and edited online lectures to make a better learning experience from home for students during the pandemic.
  • Helped students with course material, homework and project, along with grading.
Learning Centre at the Faculty of Computer Science
Helped undergraduate students with course material and homework.

Software Developer

Sept. 2018 – Jun. 2019

Shahid Beheshti University, Tehran, Iran

  • Resolved reported bugs for the document repository microservice for the Food and Drug Administration
  • Improved data integrity by implementing document validation checks for new schemas
  • Added advanced document retrieval queries, filters and tables to the customer-facing website
Spring Boot Thyemeleaf Elasticsearch Kibana MySQL

Intern

Jul. 2016 – Sept. 2016

Iran Air IT Department, Tehran, Iran

  • Demonstrated QlikView as the potential BI tool to analyze company’s IT expense statistics
  • Created dashboard for an overview of expense records by department over time, region, and such
QlickView MSSQL

Teaching Assistant

Sept. 2015 – Dec. 2015

Shahid Beheshti University, Tehran, Iran

Discrete Mathematics
I assisted the chief TA by creating homework and quiz questions

Educationlogo

Masters of Computer Science

Sept. 2019 - May 2021

Dalhousie University, Halifax, Canada

GPA A

Courses:
  • Machine Learning for Big Data
  • Visual Analytics
  • Advanced Topics in NLP
  • Deep Learning

Received full funding and stipend

Sponsored by the faculty to attend Grace Hopper Conference 2020

B.Sc. in Software Engineering

Sept. 2013 – Feb. 2018

Shahid Beheshti University, Tehran, Iran

GPA 17.73 out of 20 (3.71/4.0)

Certificateslogo

AWS Cloud Technical Essentials

Dec. 2023

Credentials

Publicationslogo

A. Jalilvand, M. Neshati, “Channel Retrieval: Finding Relevant Broadcasters on Telegram”, Social Network Analysis and Mining, 2020

Java Apache Lucene MySQL trec_eval

A. Askari, A. Jalilvand, M. Neshati, “On Anonymous Commenting: A Greedy Approach to Balance Utilization and Anonymity for Instagram Users”, 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Projects

Here I've listed the projects I've done back when I was a student.

  • All
  • Machine Learning
  • NLP
  • Visual Analytics
  • Interactive Visualization
  • Web Service
SeRViz: an Interactive Visualization Framework for the Analysis of Sequential Rules and Frequent Itemsets

* Masters thesis

Exploratory research on log of ground handling operations at Halifax Stanfield International Airport collected by Assaia Apron AI
Objective: Finding delay-related patterns affecting turnaround performance.
Methodology: Using data mining and visualization of patterns with a novel matrix-based approach
Evaluation: 1) Domain expert feedback on visual prototype 2) User test for measuring workload with NASA-TLX tool for a set of analytical tasks with both plain-text and SeRViz prototype. The NASA-TLX results for the two set of tasks are compared with paired t-test.
Results: SeRViz is a novel visual analytics tool for mining and exploring frequent patterns. Based on our experiments it reduces the cognitive load of users for the said tasks compared to the popular off-the-shelf data mining tool, SPMF.
Python SPMF Flask Javascript Vue.js D3

Code Video Demo Thesis on DalSpace online library
Exploratory Data Analysis and Quantile Regression with LightGBM and Pytorch

Having learnt about quantile regression, I decided to try it myself and it resulted in this Kaggle kernel. I did a series of exploratory data analysis steps to understand the data better, then I tried two tree-based and neural network models for quantile regression. I also performed linear regression assumption check to see if it makes sense to use quantile regression on this data after all. More detail is explained in the kernel.
Python Pytorch scikit-learn LightGBM Plotly


Open In Colab Code
Acoustic Scene Classification

* Teamed with two other MSC students.

This project was done as the final project of Deep Learning course, summer 2020. We built a CNN model to classify audio samples into different acoustic scenes (indoor, outdoor, transportation). We used data augmentation to generate more training samples and prevent the network from overfitting. Furthermore, we used Grad-CAM, a visual explanation technique, to analyze which parts of the spectrograms are most influential in CNN's final decision. We managed to outperform the DCASE 2020 Task 1 Subtask B baseline accuracy by 5.98%.
Python Pytorch Plotly

The team decided not to publish the code for now, in case we wanted to use it for DCASE 2021 competition.
Extractive Text Summarization and Keyword Extraction

This project was done as the final project of Advanced Topics in NLP course, winter-spring 2020. In this project, we aim to improve off-the-shelf products for text summarization and keyword extraction. These products use statistical techniques such as Bag-of-Words, which fail when it comes to semantical relationships between words. We tried to see if we can address this problem with word vectors. The experimental results show that there is a trade-off between the performance improvement of state-of-the-art methods and the efficiency of the baselines.
Python Gensim

Code
Reuters Corpus text classification with Keras

This project was done as the final project of Machine Learning for Big Data course, fall 2019. The goal is to train a model to classify news articles into topics. I used deep autoencoders for feature extraction and deep neural network for classification. I tried both TF-IDF and Glove word embeddings for text representation to find out which one performed best with this dataset. In the Reuters Corpus, each article has multiple topics, which was a chance for me to explore both multi-class (just take one topic per document) and multi-label (multiple topics per document) classifications as well.
Python Tensorflow Keras scikit-learn

Open In Colab Code
Airbnb Recommender System

* Teamed with three other MSC students.

This project was done as the final project of Visual Analytics course, fall 2019. It aims to construct a recommender system for Airbnb listings. This project consists of two significant parts, machine learning, and visualization.On the machine learning side, core implementation is a K-means clustering algorithm to cluster listings. On the other part, some of the key features are maps, listing details, and some analysis to ease the choice among the options. Using the recommender system, the search space for the users will be narrowed down to a select set of options to choose from among them.
Python scikit-learn Flask Javascripts D3 HTML CSS

Demo Code
* Please be patient with the demo as it takes a while for the project to fully load.
Interactive RadVis and Star Coordinates with D3

RadVis and Star Coordinates are used for visualization of multivariate datasets in a 2D projection. This interactive implementation allows the user to select dataset of choice, change colours, move the anchors/axes and more.
Javascript D3 HTML CSS

Demo Code
Telegram Bot-Creator Bot

Bachelor's thesis project, Jan. 2018, Shahid Beheshti University.
Telegram is one of the most popular messaging apps in the world, and bots are one of its popular features. The goal of this project was to create a platform where ordinary people with no knowledge of programming can create and customize their bot.
PHP Laravel MySQL

Vedio Demo Code

A fun wrap up!

Thank you for scrolling down this far!