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This is a Berkman Klein alum page. The information below may be out of date.

Momin M. Malik was the Data Science Postdoctoral Fellow at the Berkman Klein Center from 2018-2020, where he worked on building a comprehensive theoretical view of machine learning that integrates critical, constructivist, and statistical perspectives, in addition to supporting Media Cloud and community initiatives. 

He is a multidisciplinary researcher who brings statistical modeling to bear on critical, reflexive questions with and about large-scale digital trace data. He is broadly concerned with issues of algorithmic power and control, and of validity and rigor in computational social science and applications of machine learning to areas of social science and policy. He has an undergraduate degree in history of science from Harvard, a master's from the Oxford Internet Institute, and a PhD from Carnegie Mellon University's School of Computer Science. He currently works as Director of Data Science at Avant-garde Health. 


Oct 18, 2013

The Challenges of Defining 'News Literacy'

This brief seeks to stimulate a discussion among the grantees about different approaches to defining, framing, and understanding core concepts such as 'news' and 'news literacy',…

Feb 23, 2012

Youth and Digital Media: From Credibility to Information Quality

Building upon a process- and context-oriented information quality framework, this paper seeks to map and explore what we know about the ways in which young users of age 18 and…


Dec 14, 2018

Get to Know Berkman Klein Fellow Momin Malik

a spotlight on one of our 2018-2019 BKC Fellows

Momin is bringing network analysis techniques, various statistical models, topic models, and natural language processing to reduce data in ways that are comprehensive,…


Harvard Medical School

Anti-Racist Epidemiology

Research suggests reparations for slavery could have reduced COVID-19 infections and deaths in U.S.

Feb 10, 2021
Misinformation Review

Disinformation creep: ADOS and the strategic weaponization of breaking news

Mutale Nkonde and colleagues publish in HKS Misinformation Review

Jan 18, 2021

Can algorithms themselves be biased?

The short answer is, “yes, but it doesn’t really matter when compared to the choice to use machine learning.”

Apr 24, 2019