Scholar
Martin Saveski
Google Scholar ID: M3D870YAAAAJ
Assistant Professor, University of Washington
Citations & Impact
Citations
1,053
H-index
13
i10-index
18
Publications
20
Co-authors
7
list available
Publications
Resume (English only)
Academic Achievements
- - Community Notes Reduce Engagement With and Diffusion of False Information Online (PNAS'25)
- - Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking (WWW'25)
- - Social Media Algorithms Can Shape Affective Polarization via Exposure to Antidemocratic Attitudes and Partisan Animosity (Arxiv'24)
- - Reranking Social Media Feeds: A Practical Guide for Field Experiments (Arxiv'24)
- - Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness (ICML'24)
- - Counterfactual Evaluation of Peer-Review Assignment Policies (NeurIPS'23)
- - Embedding Societal Values into Social Media Algorithms (JOTS'23)
- - Engaging Politically Diverse Audiences on Social Media (ICWSM'22)
- - Perspective-taking to Reduce Affective Polarization on Social Media (ICWSM'22)
- - The Structure of Toxic Conversations on Twitter (WWW'21)
- - Social Catalysts: Characterizing People Who Spark Conversations Among Others (CSCW'21)
Research Experience
- - Assistant Professor at the Information School, University of Washington
- - Postdoc at Stanford University
- - Internships at Yahoo! Labs, Amazon, LinkedIn, and Facebook during his graduate studies
Education
- - Assistant Professor at the Information School, University of Washington
- - Postdoc at Stanford University, mentored by Johan Ugander
- - Ph.D. from MIT in 2020, supervised by Deb Roy
- - M.Sc. in Data Mining and Knowledge Management, one year in Paris and one year in Barcelona
- - B.Sc. in Computer Science from Staffordshire University with First Class honors
Background
- His research develops tools for analyzing large-scale social data, aiming to provide a better understanding of social structure and behaviors online while also impacting the design of digital social systems. His work often intersects with Social Networks, Machine Learning, and Causal Inference.
