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Advances in Probabilistic Topic Modeling and Expertise Ranking in Heterogeneous Networks

This report summarizes significant research contributions in topic modeling, focusing on biased propagation techniques applied to heterogeneous information networks. Key works include "Probabilistic Topic Models with Biased Propagation," accepted at KDD'11, and "Collective Topic Modeling for Heterogeneous Networks," accepted as a poster session at SIGIR'11. Additionally, there are ongoing collaborations on enhancing bilingual event extraction and preparations for a journal paper. The author has served on program committees for KDD 2011 and AAAI 2011 and is actively engaged in research at Network Science CTA.

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Advances in Probabilistic Topic Modeling and Expertise Ranking in Heterogeneous Networks

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  1. Summary Report, Spring 2011 • Research: • Probabilistic Topic Models with Biased Propagation on Heterogeneous Information Networks (with Jiawei, Bo, Yintao, Cindy) [Accepted by KDD’11] • Collective Topic Modeling for Heterogeneous Networks (with Bo, Jiawei) [Accepted by SIGIR’11 poster session] • Modeling and Exploiting Heterogeneous Sources for Expertise Ranking [Rejected by SIGIR, revising for CIKM] • Collaboration with Heng Ji@CUNY: • Enhance Bilingual Event Extraction Through Topic Modeling (with Hao Li, Heng Ji, Jiawei) [Preparing for CIKM/IJCNLP] • Preparing a Journal paper (special issue) with Heng Ji and Jiawei • Services: • PC : KDD 2011, AAAI 2011 AIW Track • Reviewer: ACM TIST, IJCNN 2011 • Others • Residing at Network Science CTA for the whole semester • Working with Prithwish Basu@BBN and Ziyu Guan@UCSB

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