Lena Armstrong


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Lena Armstrong is a NSF Graduate Research Fellow and Computer Science PhD Student at Harvard University, working with Fernanda Viegas and Martin Wattenberg at the Insight & Interaction Lab.

Her research has focused on human computer interaction, algorithmic justice, and algorithm auditing. She is interested in bridging humans and technology for social impact, understanding bias and opacity in automated systems, and encouraging more inclusive experiences in computer science.


Lena Armstrong, Jayne Everson, Amy J. Ko. “Navigating a Blackbox: Students’ Experiences and Perceptions of Automated Hiring.” ICER 2023.

Jean Salac, Alannah Oleson, Lena Armstrong, Audrey Le Meur, Amy J. Ko. “Funds of Knowledge used by Adolescents of Color in Scaffolded Sensemaking around Algorithmic Fairness.” ICER 2023. [BEST PAPER]

Lena Armstrong, Abby Liu, Steve MacNeil, and Danaë Metaxa. “The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in Hiring.” (submitted)

Lena Armstrong and Danaë Metaxa. “Navigating Automated Hiring: Fairness Perceptions, Strategy Use, and Outcomes Among Young Job Seekers.” (submitted)

Jean Salac, Lena Armstrong, Megumi Kivuva, Jayne Everson, Alannah Oleson, Amy J. Ko. “Supporting Adolescents in Developing Critical Computing Consciousness.” (submitted)

Andrew Revell, Alexander Silva, Dhanya Mahesh, Lena Armstrong, Thomas Arnold, John Bernabei, Brain Litt, Ezquiel Gleichgerrcht, Leonardo Bonilha, Joel Stein, Sandhitsu Das, Russell Shinohara, Danielle Bassett, Kathryn Davis. “White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures.(submitted)

Research Projects

Senior Honors Thesis & Continued Research (August 2022 - Present)

Design Use Build (DUB) REU Program (June - August 2022)

  1. How Adolescents Make Sense of Algorithmic Fairness: interviewed adolescents on their perceptions of algorithmic fairness and analyzed their responses to different technological scenarios

  2. Critical Concious Computing: taught an algorithmic fairness and computer science as part of the Upward Bound program for first-generation, low-income high school students in Seattle, and studied impact of critically conscious computing education

  3. Perceptions of Automated Hiring: created a project to determine first-time job seekers’ perceptions and experiences with automated hiring algorithms to determine bias and opacity [Poster]


HCII Summer Undergraduate Research Program (June - August 2021)


Penn Center for Neuroengineering and Therapeutics (May 2020 - December 2021)

  1. White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures: helped investigate differences between white matter and gray matter through functional recordings acquired by implantable devices and neuroimaging to better understand brain function and pathophysiology

  2. Machine learning of EEG to help diagnose epilepsy: created a pipeline to predict brain functional connectivity from structural connectivity with Python using brain network analysis and machine learning techniques [Poster for Penn Fall Research Expo]


Work and Leadership Experiences

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