Naser Al Madi, PhD

Associate Professor

Department of Computer Science
Colby College

 Office Hours:
M-W-F 1:30 pm - 3:30 pm
(or by appointment)


Research

Carl Sagan argued that reading is “perhaps the greatest of human inventions, binding together people who never knew each other” and allowing one to be “inside the mind of another person, maybe somebody dead for thousands of years.” With the emergence of modern technology and computer languages, the study of reading gained significant practical implications, including the development of better reading interfaces and tools. Moreover, reading source code shares many aspects with reading natural text, yet research shows that reading source code differs fundamentally from natural text in purpose, syntax, semantics, and viewing strategy.

The first goal of my research is to understand how vision, attention, memory, and language processing are coordinated to support our amazing capacity to read natural language and source code. My second goal is to utilize this understanding to build tools and methods that make reading easier and faster, overcoming the difficulties associated with reading in general and reading source code in particular. Because these processes are extremely complex, my research relies on eye-tracking experiments as a methodology to measure cognition as it is occurring, in a non-invasive and ecologically valid manner.

I get to work on research with amazing students. I've supervised a range of independent studies and research projects. I like to work with motivated students who are interested in research in cognitive and human aspects of software engineering and eye tracking.

I can provide effective research mentorship to a limited number of students, so I am only able to work with a few new students each year. Email me if interested.

For a complete bibliography, see Google Scholar. Representative papers for each research direction are listed under Research projects below.

Research projects

Major directions in my lab, with representative themes from published and ongoing work.

Eye tracking tools & data correction methodologies

fix8 GUI showing eye-tracking correction over lines of text

Screenshot from the open-source fix8 tool (see Al Madi et al., Behavior Research Methods, 2025; drift correction in JEMR, PACM HCI).

Reliable eye-tracking data is a prerequisite for studying reading in both natural language and source code, yet recordings are often distorted by drift in combination with regressions and progressions especially in reading code. This project develops and evaluates correction methods at multiple levels: comparing manual and automated drift correction, learning eye-movement patterns to drive adaptive correction, and testing semi-automated workflows (including the open-source fix8 tool) that combine automation with human expertise.

Key papers relevant to this project:

Eye movement in programming and AI-assisted software development

Students working with laptops in a software engineering course

Empirical studies of how developers read GitHub Copilot output in the IDE (ASE 2022).

This project studies where developers look, how expertise develops, and how new tools change comprehension—using eye tracking, controlled experiments, and cognitive models of reading applied to code. Representative work includes guidance for running credible eye-tracking studies in software engineering (including a community cookbook for ECSEE), empirical studies of readability and visual inspection of GitHub Copilot output in the IDE, and tools and studies on identifier naming—such as Namesake and works on the costs of lexical similarity in names.

Key papers relevant to this project:

AI-based assistive reading interfaces

Person reading

GP-TSM (CHI 2024 LBW) and dictation readability / engagement (CHI 2026).

Assistive reading interfaces can reshape how people allocate attention, skim efficiently, or stay engaged with text—and those effects show up clearly in eye movements. One thread examines grammar-preserving text saliency modulation (GP-TSM), asking why skimmers perform better when saliency cues preserve grammatical structure, using eye tracking to connect rendering choices to comprehension and strategy. A second thread studies dictation-based reading interfaces, using eye movements to probe readability and engagement—including when a degree of unfamiliarity in the experience may be beneficial—so interface design can better support real reading behavior rather than only surface-level preferences.

Key papers relevant to this project:

Eye tracking in clinical diagnostics and rehabilitation

Road and driving scene

Naturalistic driving / intersections (IOVS, ARVO); preliminary eye-tracking work on visual search in cerebral visual impairment (ETRA 2026 LBW).

This collaboration-focused work connects gaze and head behavior to real-world mobility and safety for people with visual field loss, with an emphasis on naturalistic driving at intersections and turns. We develop data extraction and processing methods suited to messy, naturalistic recordings so researchers can study how drivers behave when roads, traffic, and head movement matter—not only fixation on a screen. Related work also examines cerebral visual impairment, including preliminary eye-tracking evidence on how visual search may differ in CVI. The studies include pilot investigations of head scanning before left turns for drivers with homonymous visual field loss, broader characterization of scanning in naturalistic driving, and exploratory work on training approaches (such as reminder-cue scanning) to support safer blind-side scanning—linking measurable behavior to rehabilitation and clinical questions.

Key papers relevant to this project:

Collaborating institutions

I have collaborated with colleagues and co-authors from the following institutions.

Harvard Medical School — Schepens Eye Research Institute Harvard Medical School Boston, MA, USA
Harvard John A. Paulson School of Engineering and Applied Sciences Harvard SEAS Cambridge, MA, USA
University of Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing, China
City University of Hong Kong City University of Hong Kong Hong Kong, China
Brown University Brown University Providence, RI, USA
Polytechnique Montréal Polytechnique Montréal Montréal, Canada
Abertay University Abertay University Dundee, United Kingdom
University of Nebraska–Lincoln University of Nebraska–Lincoln Lincoln, NE, USA
Universiti Malaya Universiti Malaya Kuala Lumpur, Malaysia
Kent State University Kent State University Kent, OH, USA
HTW Berlin HTW Berlin / BHT Berlin, Germany
University of Eastern Finland University of Eastern Finland Joensuu, Finland
OTH Regensburg OTH Regensburg Regensburg, Germany