Hi, my name is

Amir.

I explore human behavior & AI applications

A passionate researcher in pedestrian studies and applied AI. My work focuses on developing models and simulations to analyze crowd dynamics, optimize emergency evacuation strategies, and enhance urban mobility and safety. I leverage machine learning, complexity science, and data-driven insights to better understand how people navigate cities and interact with their environments.

Resume

About Me

I am a transportation engineer and researcher exploring AI-driven solutions for pedestrian safety, urban mobility, and evacuation. My work integrates AI with transportation engineering to enhance modeling, simulation, and decision-making in complex environments. With expertise in pedestrian dynamics, traffic flow, and machine learning, I develop innovative tools to improve urban planning and disaster response. As cities evolve, I aim to bridge AI innovation with real-world challenges, creating smarter and safer urban spaces.

I am currently working on EvacuAIDi, an AI-powered framework that optimizes emergency evacuation strategies by integrating large language models (LLMs), and pedestrian dynamics modeling. This system provides adaptive evacuation guidance, especially for individuals with disabilities, ensuring a more inclusive and efficient response during emergencies.

Technical Skills:
  • Python, R
  • PostgreSQL, BigQuery
  • AI & ML
  • Cloud Computing
  • Traffic Simulation
  • Pedestrian Simulation
  • Data Analysis
  • QGIS

Experience

Graduate Research Assistant - USU
Aug 2022 - present
PEDAT Dashboard
I am currently working as a Graduate Research Assistant at the Singleton Transportation Lab at Utah State University. My research focuses on developing AI-powered tools in pedestrian and evacuation studies.
Algorithm Engineer - Ramona
Nov 2015 - Jul 2022
Developed core algorithms for a Traffic Guide Sign Design Software, enabling automated detection of required sign locations using interactive maps, guidelines, and manuals. Designed the system to generate production-ready outputs and support multi-language traffic signs, ensuring compliance with regulatory standards. Collaborated with developers to refine implementation, conducted rigorous testing, and validated results to enhance accuracy and usability.
Transport Modeler & Data Scientist - IRIANA
May 2015 - Jun 2020

Worked on various projects involving transportation modeling, data analysis, and AI-driven solutions for urban mobility and infrastructure planning. Designed and implemented computational models to support decision-making in large-scale transportation projects.

Key Projects:

  • Crowd Evacuation Modeling
  • Transportation Master Plans
  • Airport Design & Planning
  • Freight Master Plans
  • Traffic Impact Studies
Pedestrian Modeling Specialist & Transportation Researcher - IUST
Aug 2012 - May 2015

Worked on various projects related to pedestrian modeling, emergency evacuation, and transportation network resilience to support urban planning and mobility strategies.

Key Projects:

  • Pedestrian simulation and modeling in urban environments
  • Emergency evacuation studies for highways and tunnels
  • Transportation network resilience analysis and optimization
  • Traffic flow assessments and impact studies

Education

2022 - 2025
Ph.D. in Civil and Environmental Engineering
Utah State University

Transportation Engineering specialization

Dissertation: Artificial Intelligence (AI)-Guided Crowd Evacuation: A Novel Approach to Enhancing Emergency Response (EvacuAIDi)

2010 - 2013
Master of Science in Civil and Environmental Engineering
Azad University: South Tehran Branch

Transportation Engineering specialization

Thesis: Pedestrian Dynamic Modeling using Social Force Model: A Heterogeneous Approach

2005 - 2009
Bachelor of Science in Civil Engineering
Azad University of Ahvaz

Tools

Crowd-Analyzer
Machine Learning OpenCV YOLO GUI
Crowd-Analyzer
Crowd Analyzer is a Python application designed to analyze pedestrian and crowd mobility patterns using computer vision and machine learning techniques. It incorporates YOLO for object detection, Kalman filters for tracking, and various methods for density and speed estimation.
PEDAT
Streamlit Dashboard Google Cloud
PEDAT
PEDAT is a Streamlit-based web application designed to visualize pedestrian volume data in Utah. It offers an interactive and user-friendly interface to analyze and understand pedestrian traffic patterns effectively.
Live Demo
CrashAutoML
Machine Learning AutoML Streamlit
CrashAutoML
This app is designed to analyze crash severity data using automated machine learning (AutoML) methods. It allows users to upload CSV files containing crash data, select features and target variables for modeling, and handle data imbalances using SMOTE (Synthetic Minority Over-sampling Technique).
EvacuAIDi
AI/ML Streamlit LLMs A* Algorithm
EvacuAIDi
This tool optimizes evacuation planning in stadiums using advanced algorithms, image processing, and AI to generate personalized evacuation routes tailored to user locations and specific needs.
PedImpute
Machine Learning DBSCAN LSTM GRU
PedImpute
This tool evaluates pedestrian volume data quality for safety and urban planning. It detects anomalies and imputes missing values using ML and deep learning. Analyzing Utah’s traffic signal data (2018–2022) with EpiEnv variables, it finds DBSCAN best for anomaly detection and Random Forest, LSTM, and GRU effective for imputation. Results highlight EpiEnv factors' impact on data accuracy.
Awesome Crowd Dynamics Hub
Resource Collection Documentation
Awesome Crowd Dynamics Hub
A curated collection of open-source tools and resources for studying and analyzing crowd dynamics, pedestrian flow, and evacuation strategies. It includes libraries, datasets, research papers, and software solutions that are valuable for researchers, urban planners, and developers working in the field of crowd dynamics.

Get in Touch

Feel free to reach out if you’re interested in my research or would like to collaborate on projects related to pedestrian dynamics and AI applications.
Email me