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Official Site of Mahnaz Behroozi

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About Me

I am the changer of technical interviews' future!

I am a Ph.D. candidate in Computer Science at North Carolina State University where I am advising by Dr. Chris Parnin in the alt-code lab. My research identifies cognitive barriers to technical interview candidates using eye-trackers while they are solving a coding problem. The ultimate goal of my current research is to enhance the technical interview process to help candidates show their skills and to help the recruiters suffer less from false negatives. 

My Ph.D. is funded by the National Science Foundation.

I also hold a BS and MS in Computer Science and Engineering.


My Research

Human Computer Interaction

Data Mining & Machine Learning


Published Work

Towards Scientific Study of Technical Interviews Using Eye Tracking

Mahnaz Behroozi

VL/HCC 2019: IEEE Symposium on Visual Languages and Human-Centric Computing (to appear)

This is a graduate consortium paper describing different phases of my doctoral research and my future work.

Hiring is Broken: What Do Developers Say About Technical Interviews?

Mahnaz Behroozi, Chris Parnin and Titus Barik.
VL/HCC 2019: IEEE Symposium on Visual Languages and Human-Centric Computing (to appear)
We examined developer perceptions about technical interviews through qualitative analysis on Hacker News posts. 12 full papers accepted out of 40 submissions (33%)

 Beyond the Code Itself: How Programmers Really Look at Pull Requests.

Denae Ford, Mahnaz Behroozi, Alexander Serebrenik, and Chris Parnin
ICSE 2019: 41st ACM/IEEE International Conference on Software Engineering, Software Engineering in Society Track (SEIS)
We find programmers spend significant time considering secondary technical and social signals when evaluating code. 8 papers accepted out of 27 submissions (29.6%)

Can we predict stressful technical interview settings through eye-tracking?

Mahnaz Behroozi and Chris Parnin.
ACM ETRA 5th International Workshop On Eye Movements in Programming (EMIP)
We demonstrate a preliminary classifier that can detect when candidates are stressed in an interview.

Dazed: Measuring the Cognitive Load of Solving Technical Interview Problems At the Whiteboard.

Mahnaz Behroozi, Alison Lui, Ian Moore, Denae Ford, Chris Parnin.

ICSE 2018: 40th International Conference on Software Engineering, NIER Track

We find evidence of higher cognitive load and stress associated with problem-solving on whiteboards. 28 out of 101 papers accepted (27%)

A multiple-classifier framework for Parkinson’s disease detection based on various vocal tests

Mahnaz Behroozi and Ashkan Sami.
International journal of telemedicine and applications 2016.
We proposed a classification method to enhance detection of people with Parkison's disease using their vocal tests


Contact Me

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