Michael Ibrahim

photoshoot from nasa's surp program (2023)

Hi! I’m a current undergraduate at the Paul G. Allen School of Computer Science and Engineering and Information School of the University of Washington studying computer science and informatics. I’m also a research assistant at the Iyer & AIR Labs, advised by Vikram Iyer and Sawyer Fuller, where I focus on creating machine learning algorithms and embedded system software for micro-scale robots. I also have previous industry experience, with being a software engineer intern at Amazon and Boeing.

I also focus on outreach of computer science and other STEM topics to underprivileged and underserved communities. I serve as a scholar lead at Washington State Opportunity Scholarship, a Allen School Student Recruitment Representative, and also a teacher/mentor at AVELA and Rawan.

Outside of school I love playing basketball, powerlifting, going on the occasional hike, and practicing on getting a higher wpm on monkeytype.

news

Apr 18, 2025	Our poster got accepted to ICRA’s Late Breaking Results 🎉
Aug 16, 2024	Presented at the Washington NASA Space Grant Consortium Symposium (pt. 2)
Jan 29, 2024	Thank you Qualcomm for the nice trip to San Diego! 🏖️

selected publications

Ethics

Probing the Ethical Boundaries of Personalization: a Case Study of Twitter’s Recommendation Algorithm

K. Feng , M. Ibrahim , and J. Yoon

Mar 2024

Abs PDF

Most online content platforms today are designed to maximize the time users spend engaging with their content. This engagement allows platforms to both serve advertisements and collect data from usage patterns to incorporate into their recommendation and per- sonalization algorithms. However, personalization algorithms are often opaque; surfacing relevant and interesting content to users at best, and constructing echo chambers in which users are not exposed to a diversity of opinions or beliefs at worst. This issue is exacerbated by the fact that many content platforms enable users to fine-tune personalization algorithms without much in the way of ethical guardrails, such as “liking” a post or selecting “see less/- more”. In this paper, we ask the question of whether there are ethical limits to personalization in content platforms. Twitter is a content platform with a wealth of publicly-available information surrounding its personalization algorithms, which we use as a case study for our investigation. We conduct a literature review of prior research in content personalization on social media and analyze published algorithmic personalization source code by Twitter along with related technical blog posts. We then identify ethically nu- anced components of Twitter’s content personalization pipeline and analyze them from the perspective of 6 ethical theories. We con- clude with a discussion of how developers can more deeply engage with ethical considerations when building personalized systems.