Yazan Abulaimoun

resume pdf

Education

Experience

Skills

Awards & Achievements

Certifications

Research

Hello! I'm Yazan Abulaimoun, a researcher specializing in Human-Centered Transportation and Urban Systems.

My work leverages data science, network analysis, and machine learning to investigate the relationships between urban design, transportation networks, community dynamics, and sustainability.

I hold a Bachelor of Science in Spatial Data Science from Arizona State University and am pursuing my PhD in Engineering and Applied Science at CU Denver. My current research emphasizes the development of equitable, sustainable, and accessible urban mobility solutions, aiming to enhance livability and walkability in urban environments.

My technical skillset includes proficiency with spatial analysis (GIS), network modeling, and visualization tools augmented by programming expertise in Python, R, and JavaScript. These skills are further enriched through my ongoing Master's in Computer Science at Georgia Tech, specializing in Machine Learning and AI.

I'm passionate about exploring innovative methods to quantify urban form and dynamics, and I look forward to collaborating on projects that push the boundaries of sustainable and human-focused urban transportation research.

Education

PhD in Engineering and Applied Science Expected August 2028

University of Colorado

Civil/Transportation

Master of Science in Computer Science Expected August 2026

Georgia Institute of Technology

College of Computing (Online/Part-Time)

Course highlights

CS 7638 Robotics: AI Techniques (Spring 2024)

Implement filters (including Kalman and particle filters) in order to localize moving objects whose locations are subject to noise.
Implement search algorithms (including A*) to plan the shortest path from one point to another subject to costs on different types of movement.
Implement PID controls to smoothly correct an autonomous robot’s course.
Implement a SLAM algorithm for a robot moving in at least two dimensions.

CS7646: Machine Learning for Trading (Spring 2024)

This course introduces the real-world challenges of implementing machine learning-based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN, and regression trees and how to apply them to actual stock trading situations.

CS6750: Human-Computer Interaction (Fall 2023)

The principles and characteristics of human-computer interaction, such as direct manipulation, usability affordances, and interaction design heuristics.
The workflow for designing and evaluating user-centered designs, from needfinding to prototyping to evaluation.
The current state of research and development in human-computer interaction, such as augmented reality, wearable devices, and robotics.

Bachelor of Science in Data Science Graduated Summa Cum Laude, May 2023

Arizona State University Phoenix, AZ

School of Mathematical & Statistical Sciences

Urban Planning Minor

Associate of Arts degree in Architecture Graduated with Honors, December 2021

Community College of Philadelphia Philadelphia, PA

Experience

Research Aide (Part-Time) January 2023 - May 2023

Arizona State University (ASU) Phoenix, AZ

§ Advanced grant-funded research initiatives across topics related to humane technology and A.I

§ Mentored and guided a team of (5) Lincoln Center Undergraduate Researchers

Preceptor (Part-Time) September 2022 - May 2023

Arizona State University Phoenix, AZ

§ Provided one-on-one mentorship to students in the School of Geographical Sciences and Urban Planning

§ Delivered subject-level tutoring for GIS courses and technical assistance with ArcMap and ArcGIS Pro

Project Designer, Assistant Project Manager (Full-Time) August 2019 - March 2021

Genesis Architectural LLC Willow Grove, PA

§ Developed and refined processes for design and engineering, manufacturing, and installation departments.

§ Led the transition to cloud-based PLM systems to accommodate remote work necessities during COVID-19.

Project Designer (Full-Time) January 2018 - July 2019

M Cohen & Sons Broomall, PA

§ Managed and reviewed project shop drawings and submittals to ensure conformance with design specifications

§ Guided the development of new point cloud survey processes for enhanced accuracy in fabrication and installation

Skills

Programming

Python, R Studio, JavaScript, Java

Computer Aided Design (CAD)Inventor, Sketchup Pro, AutoCAD, Grasshopper, Lumion, Rhinoceros 3D, Revit

Geographical Information Systems

ArcGIS Pro, ArcMap, QGIS3

Data and Visualization

SQL, Tableau, Excel, Power Query

Document Control & 2D Graphics

Adobe CS and Blubeam Revu, Sketchbook Pro

Awards & Achievements

§ Best Visualization Award, American Statistical Association Datafest, ASU, Spring 2023

§ Dean's' List, ASU, All Semesters(2021-2023)

§ Online Undergraduate Research Scholarship, Arizona State University, Awarded Thrice (2022-2023)

§ Lincoln Center for Applied A.I Ethics Scholarship, Arizona State University, Awarded Twice (2022-2023)

Certifications

§ Geographical Information Sciences (GIS) Certificate, Arizona State University, Issued 05/23

§ Google Advanced Data Analytics Professional, Coursera, Expected 01/24

§ Google Business Intelligence Professional, Coursera, Expected 01/24

§ Tableau Prep: Clean, Transform and Prep, Linkedin Learning, Issued 03/23

§ Geo Apps MOOC, ESRI, Issued 03/22

§ Spatial Data Science, ESRI Issued 10/21

Research

Opinion Formation and Graph Dynamics: January 2023 - May 2023

From Modeling to Empirical Applications Arizona State University

§ Conducted sentiment analysis of the Brexit Debate using NLP, ML and various architectures in PyTorch, analyzing a dataset of 51 million Twitter posts from 2.8 million users over four years

§ Initialized baseline models with RNNs and CNNs to test and train transformer architectures.

Quantitative Analysis of Slime Mold Growth August 2022 - May 2023

Analyzing citizen science data using machine learning Arizona State University

§ Analyzed over 250,000 images by training a neural network (DETECTRON2) to generate segmentation masks used to derive key growth characteristics.

§ Collaborated with an interdisciplinary team, leveraging their domain expertise to fine-tune the machine learning model and enhance accuracy in determining growth patterns.

Land Use and Pedestrian Safety August 2022 - December 2022

for Selected Arterials in Chicago, IL USA Arizona State University

§ Aggregated land uses through a combination of Google Places API and custom scripting and manual methods.

§ Utilized geospatial analytics to correlate pedestrian safety data with land use patterns, uncovering insights that could inform urban planning initiatives.

Applied Mathematics Research Experience for Undergraduates May 2022 - August 2022

National Science Foundation Arizona State University

§ Applied machine learning (ML) techniques for image denoising and classification, utilizing different neural network architectures within Python's PyTorch package.

§ Identified optimal configurations for specific image datasets by conducting quantitative analysis between different neural network models and traditional denoising methods.