Hamed Zamani

[email protected]

[email protected]

CIIR, UMass Amherst

I am a Computer Science PhD candidate (with distinction) at the University of Massachusetts Amherst (UMass) working with W. Bruce Croft. I am currently a research assistant at the Center for Intelligent Information Retrieval (CIIR). My research interests include various aspects of information retrieval (IR), recommender systems, and text mining. I am currently focusing on modeling the core IR problems, e.g., query representation, document representation, and ranking, via (deep) neural networks when large-scale training data is not available. I regularly publish at and serve as PC member in major IR venues. I am an ACM SIGIR Student Liaison, representing North and South Americas.

In Fall 2018, I teach CS646, the Information Retrieval course (PhD-level), at UMass Amhest. The course webpage is available here.

During my PhD, I completed two research internships at Google (summer 2016) and Microsoft (summer 2017). Prior to UMass, I was fortunate to be advised by Azadeh Shakery as a Master student at the University of Tehran, Iran.

dblp - Google Scholar

Academic Services

• ACM SIGIR Student Liaison (representing North and South Americas) 2017 – now
• Organizer:
  • SIGIR 2018 Workshop: Learning from Limited or Noisy Data for Information Retrieval
  • RecSys 2018 Workshop: ACM RecSys Challenge 2018
• Program Committee Member:
  • SIGIR 2018, 2019
  • WWW 2018, 2019
  • WSDM 2018, 2019
  • RecSys 2018
  • CIKM 2017, 2018
  • NAACL 2019
  • ECIR 2019 (short papers)
  • Workshops: EARS @ SIGIR 2018 – KG4IR @ SIGIR 2017, 2018 – NeuIR @ SIGIR 2017 – LEARNER @ ICTIR 2017 – RecSysChallenge @ RecSys 2017
• Journal Reviewer:
  • TOIS
  • IRJ
  • TKDE
  • TIIS
  • JASIST
  • Inf. Syst.

Selected Publications:

• H. Zamani, M. Dehghani, W. B. Croft, E. Learned-Miller, J. Kamps “From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing”, CIKM 2018.
• H. Zamani, W. B. Croft. “On the Theory of Weak Supervision for Information Retrieval”, ICTIR 2018.
• H. Zamani, W. B. Croft, J. S. Culpepper. “Neural Query Performance Prediction using Weak Supervision from Multiple Signals”, SIGIR 2018.
• M. Aliannejadi, H. Zamani, F. Crestani, W. B. Croft. “Target Apps Selection: Towards a Unified Search Framework for Mobile Devices”, SIGIR 2018.
• H. Zamani, B. Mitra, X. Song, N. Craswell, S. Tiwary. “Neural Ranking Models with Multiple Document Fields”, WSDM 2018.
• H. Zamani, W. B. Croft. “Relevance-based Word Embedding”, SIGIR 2017.
• M. Dehghani, H. Zamani, A. Severyn, J. Kamps, W. B. Croft. “Neural Ranking Models with Weak Supervision”, SIGIR 2017.
• H. Zamani, M. Bendersky, X. Wang, M. Zhang. “Situational Context for Ranking in Personal Search”, WWW 2017.
• H. Zamani, J. Dadashkarimi, A. Shakery, W. B. Croft. “Pseudo-Relevance Feedback Based on Matrix Factorization”, CIKM 2016.
• H. Zamani, W. B. Croft. “Embedding-based Query Language Models”, ICTIR 2016.
• H. Zamani, W. B. Croft. “Estimating Embedding Vectors for Queries”, ICTIR 2016.
• More on the publications page.

Invited Talks:

• Purdue University, IN, March 27-28, 2019
• University of Texas at Austin, March 12-13, 2019
• Yale University, CT, February 28, 2019 Link
• University of Maryland, MD, February 20-21, 2019 Link
• University of Pittsburgh, PA, February 4-5, 2019 Link
• UMass Data Science Talk, Amherst, MA, October 24, 2018 Link
• Microsoft Research, Redmond, WA, September 18, 2018
• Google Research, Mountain View CA, March 21, 2018
• Amazon’s A9, Palo Alto CA, March 19, 2018 (with Bruce Croft and Qingyao Ai)
• Yale University, New Haven CT, March 2, 2018 Link