Bamshad Mobasher (PER-027)
Bamshad Mobasher is a faculty member and director at DePaul University in Jarvis CDM / School of Computing / DePaul AI Institute. Documented areas of work include recommender systems, machine learning, information retrieval, local news, contrastive learning, and news media.
Summary ✓
- Primary affiliation: Jarvis CDM / School of Computing / DePaul AI Institute
- Role types: faculty, director
- Last reviewed: 2026-05-31
Access (availability): 🟢 Green — open or routine · 🟡 Yellow — appointment or affiliation · 🔴 Red — restricted · ⚪/⚫ White/Black — not yet classified
Verification: ✓ confirmed · ~ likely · ? needs review · ✕ archived
Profile & contact
- Faculty profile: www.depaul.edu/faculty/bamshad-mobasher
- ORCID: 0000-0001-9701-9178 (probable)
Description
Bamshad Mobasher is a faculty member and director at DePaul University in Jarvis CDM / School of Computing / DePaul AI Institute. Documented areas of work include recommender systems, machine learning, information retrieval, local news, contrastive learning, and news media.
Linked resources
- High-Performance Compute Cluster (Affiliated faculty)
- Center for Web Intelligence (Director)
- SPARK Center (Collaborator)
- AI Institute (Director)
Linked grants
- ReDDDoT Phase 1: Planning Grant: AI-Enabled Support Services for Transplanted Populations (PI)
- Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems (PI)
- Using Multi-Armed Bandits to Model User Dynamics in Personalized Recommendation (PI)
- III: Small:Using Data Mining and Recommender Systems to Facilitate Large-Scale Requirements Processes (Co-PI)
- Secure Personalization: Building Trustworthy Recommender Systems (Co-PI)
- Collaborative Research: A Computational Framework for Agent-based Contracting (PI)
- Student Support for the 2015 User Modeling, Adaptation and Personalization Conference (PI)
- III: Small: RUI: Multi-dimensional Recommendation in Complex Heterogeneous Networks (Co-PI)
- Bias Amplification in AI-Based Algorithms for Personalized Recommendation (PI)
- Down the Rabbit Hole: Analyzing the Influence of AI-Based Personalized Recommendation Algorithms on the Trajectory of User Preferences (PI)
- Using Machine Learning to Battle the Infodemic of Misinformation in the Age of COVID-19 (PI)
- Supporting Students Attending the User Modeling, Adaptation and Personalization 2012 Conference (PI)
- Student Support for User Modeling, Adaptation and Personalization Conference (PI)
- ReDDDoT Phase 1: Planning Grant: AI-Enabled Support Services for Transplanted Populations: A Community-Centered Design and Development Approach (PI)
Linked courses
None documented.
Linked publications
- Controlling Popularity Bias in Learning-to-Rank Recommendation (Coauthor) — DOI
- Using Recommender Systems to Help Revitalize Local News (Coauthor) — DOI
- PerSpect: Perspective-aware Contrastive Learning for Hybrid News Recommendation (Coauthor) — DOI
- Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation (Author) — DOI
- PUB-651 (Author) — DOI
- PUB-662 (Author) — DOI
- PUB-663 (Author) — DOI
- PUB-664 (Author) — DOI
- PUB-665 (Author) — DOI
- PUB-666 (Author) — DOI
- PUB-667 (Author) — DOI
- PUB-668 (Author) — DOI
- PUB-669 (Author) — DOI
- PUB-671 (Author) — DOI
- PUB-672 (Author) — DOI
- PUB-673 (Author) — DOI
- PUB-674 (Author) — DOI
- PUB-675 (Author) — DOI
- PUB-753 (Author) — DOI
- PUB-754 (Author) — DOI
- PUB-756 (Author) — DOI
- PUB-757 (Author) — DOI
- PUB-759 (Author) — DOI
- PUB-760 (Author) — DOI
- PUB-761 (Author) — DOI
- PUB-763 (Author) — DOI
- PUB-767 (Author) — DOI
- PUB-768 (Author) — DOI
- PUB-779 (Author) — DOI
- PUB-780 (Author) — DOI
- PUB-802 (Author) — DOI
- PUB-815 (Author) — DOI
- PUB-820 (Author) — DOI
- PUB-832 (Author) — DOI
- PUB-833 (Author) — DOI
- PUB-839 (Author) — DOI
- PUB-850 (Author) — DOI
- PUB-919 (Author) — DOI
Linked outputs
None documented.
Verification
- Status: ✓ Listed in the map and on a DePaul page
- Last reviewed: 2026-05-31
- ORCID verification: probable (source: orcid_api)
Source URLs
Notes
Person listing on the Resource Map. Open linked places, grants, courses, and publications from this page.