Erfan Pakdamanian


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About Me

I'm Erfan Pakdamanian, a data-driven Human Factors Engineer and a UX Researcher. It is my passion to collaborate with creative individuals to apply HF principles and machine learning techniques to the design of human-AI collaborations for the sake of creating safer and more intuitive products that benefit society as a whole.

Prior joining Ford Motor Company as a Human Factors Engineer, I received my Ph.D. in Systems Engineering at the University of Virginia (UVa), advised by Prof. Lu Feng. I was also a Data Science Presidential Fellow and a member of the Link Lab-the center of research excellence in Cyber-Physical Systems at UVa.

Research Interests

My research focuses on finding innovative ways to integrate machine learning and human factors principles into the design and analysis of complex human-in-the-loop systems. In particular, I'm interested in evaluating human-computer interaction through the study of user experience.

My contact

  • +1 (434)326-8582
  • Ann Arbor, MI
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  • I have experience and expertise spanning the complete life cycle of user research (planning, conducting, managing, and documenting).

  • I envision combining machine learning tools and human factors expertise to redefine the baseline for safety and simplicity in device design.
  • I am passionate about drawing insights from the subtle and complex ways we (humans) accomplish tasks.

  • I desire to excel in placing a human-centered focus on the work (context, end-user impact, etc), exploring solutions that work in practice and have a significant impact on people's lives.

  • I enjoy working in teams and undertaking challanging projects.






Python 80%
HTML 77%
CSS 70%
R 60%
Figma 85%
Quantitative UX Research 80%
Usability Testing 90%


Paper Publication     NEW!

Jul. 2021

ICCV'21. Virtual

Our paper "MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning" has been accepted for publication at the International Conference on Computer Vision (ICCV) 2021. (Acceptance Rate: 1617 /6236 = 25.9%)
[pdf] [arXiv] [code] [BibTeX]

Award     NEW!

Apr. 2021

CHI'21. Yokohama, Japan

I am delighted to announce that I have won Gary Marsden Travel Award form SIGCHI.

Paper Publication     NEW!

Dec. 2020

CHI'21. Yokohama, Japan

Our paper "DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data" has been accepted for publication at the ACM Conference on Human Factors in Computing Systems (CHI 2021). (Acceptance Rate: 731/2844 = 25.7%)
[pdf] [arXiv] [code] [BibTeX]

Paper Publication    

Dec. 2020

ICCPS'21. Nashville, US

Our paper "Trust-Based Route Planning for Automated Vehicles" has been accepted for publication at the ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2021). (Acceptance Rate: 26%)
[pdf] [arXiv] [BibTeX]

Paper Publication

Sep. 2020

AIAA'21. Virtual

Our paper "Formal Analysis of a Neural Network Predictor in Shared-Control Autonomous Driving" has been accepted for publication at the AIAA SciTech Forum 2021.
[pdf] [BibTeX]


Jun. 2020

University of Virginia, VA

I am honored to announce that I received Engineering Grant Ripple Entreprenurial Fellowship.

Paper Publication

Jul. 2020

AutoUI'20. DC, US

Our paper "Toward Minimum Startle After Take-Over Request: A Preliminary Study of Physiological Data" has been accepted for poster presentation at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI 2020)
[pdf] [BibTeX]

Book Publication

Mar. 2020

Our book chapter "Fundamentals and Emerging Trends of Neuroergonomic Applications to Driving" has been published, detailing the state-of-the-art neuroergonomic applications used in vehicle automation and navigation technologies
[pdf] [BibTeX]


Safe-SCAD, Predicting Driver Behavior [Webpage]

2019 - Present

Sponsored by Toyota InfoTech & University of York

I'm the primary researcher in the Safe-SCAD project, a collaborative project between UVA, Carnegie Mellon University, University of York. The project aims to understand the driver’s role in the safe operation of the semi-autonomous vehicles. I contributed to the project by:

  • Designing, developing and integrating various mixed traffic driving scenarios using Matlab Simulink..
  • Managing and training a team of 4 undergrad research assistants primarily responsible for lab experimentation data entry, and coding.
  • Developing surveys, recruting 25 participants (including pilots).
  • Measuring task completion, reaction time, error rates, and quality of takeover.
  • Pre-processing and analyzing user's subjective and objective data (GSR, Heart Rate, Eye-Tracker, and EEG)
  • Developing the first neural network model for reliably predicting multiple aspects of driver's takeover behavior, called DeepTake, by using Python, and Tensorflow.
  • Evaluating the performance of DeepTake and compared with traditional machine learning techniques(SVM, Logistic Reg., Naive Bayes, Random Forest) where DeepTake improved the performance from 78% to 93% on average.

Driver Perception and Safety Feedback

2018 - 2020

Sponsored by UVA School of Data Science

The goal of my project is developing a novel methodology to predict the cognitive states of Autonomous Vehicle drivers and alerts them such that they are able to respond in a timely and informed fashion. As the data scientist and human factors engineer I facilitated:

  • Conducting literature reviews across human factors fundamentals and emerging technologies for perception and cognition applications.
  • Writing grant proposals, designing, and conducting extensive study on the effect of takeover modalities on perception and mental readiness
  • Developing driving scenarios using Simulink and Unity
  • Measing task completion time, reaction time, error rates, and biometric data (HR, eye-Tracker).
  • Analyzing EEG data using spectral power analyses with MATLAB Toolbox EEGLab and NeuroScan software.
  • Analyzing surveys and physiological data from 10 individuals using univariate, multivariate, regression, and advanced statistical analyses to predict how cognitive state and mental readiness would change wrt. type of given alarm.
  • Developed machine learning algorithms that consider external hazards in conjunction with drivers’ cognitive states.

Cognitive Trust in Human-Autonomous Vehicle Interactions

2018 - 2020

Sponsored by NSF & Toyota InfoTech

  • Developed a signal Temporal Logic (STL) to check evolution of the driver trust to the automated system.
  • Formalized the human-automation interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state.

Effect of Worker's mood on productivity

2015 - 2016

Led the creation, execution, and analysis of individuals' mood (e.g. appetite and tiredness), emotion (e.g. happy, anxious, and distressed), and cognitive state on productivity and social contagion.

  • Planned and wrote the proposal on investigating the effect of workers' mood changes on the productivity throughout a shift.
  • Applied human factors principles and usability heuristics to identify the strengths and shortcomings of existing interactions in the workplace.
  • Designed, ran, analyzed and presented ethnographic research.
  • Led the data collection, interviews and survey responses capturing employees feedback and moods.
  • Captured mood states prior and during a shift by qualitative methods (interviews and observation) and modeled their negative and positive moods in the subsequent time period using quantitative methods (parametric statistics)
  • Presented the research findings, usability data, and product development process and the main effect of mood change on each phase on the Proceeding of WSC conference 2016.


My research tends to find innovative solutions to exising human-computer interaction problems, mainly in transportation sector. We conducted research to understand how humans interact with automated vehicles and how automation can faciliate their requirements.

  • All
  • Proceedings
  • Posters
  • Book

MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning [ICCV 2021]

DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data [CHI 2021]

Trust-Based Route Planning for Automated Vehicles [ICCPS 2021]

Formal Analysis of a Neural Network Predictor in Shared-Control Autonomous Driving [AIAA 2021]

Toward Minimum Startle After Take-Over Request: A Preliminary Study of Physiological Data [AutoUI 2020]

Fundamentals and Emerging Trends of Neuroergonomic Applications to Driving

A Case Study of Trust on Autonomous Driving [ITSC 2019]

Discrete Event Simulation of Driver’s Routing Behavior Rule at a Road Intersection [WSC 2019]

The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve [HFES 2018]

Simulating the Effect of Workers' Mood on the Productivity of Assembly Lines [WSC 2016]

UX Portfolio

My ux research journey is mainly about efficiently collaborating with users, and stakeholders through empathy and agile methodologies to ensure my design and ideas success and could potentially create marketplace disruption.

  • All

Contex-Aware Advisory Warnings


Services & Awards

I have had the great privilege of working with some of the most talented individuals in different organizations. I am also honored to have had the chance of reviewing great papers for the following journals and conferences.


I got multiple certifications to prove my desire to learn and increase competencies


Hard work sometimes DOES pay off

Paper Reviewer

Reviewed 20+ papers for journals and conferences in areas of HCI, UI, Human Factors, Machine Learning, and Cyber-Physical Systems.


Let's keep in touch. I'd like to hear from you.


Beautiful Ann Arbor, MI