2020 applicants

Dr Shoaib Jameel

School of Computer Science and Electronic Engineering (CSEE)
Dr Shoaib Jameel
  • Email

  • Location

    5A.529, Colchester Campus

  • Academic support hours

    Tuesday: 12:00 PM until 1:00 PM (Zoom ID: 97561813599)



My work revolves around proposing novel computational methods for machine learning with applications to text mining. Specifically, my work has centred around learning low-dimensional representations of natural language text on a large-scale. Among others, I have developed a variety of probabilistic topic models, which have seen applications in text mining and information retrieval, as well as vector space embeddings, which have shown promising results in tasks such as knowledge base completion and commonsense reasoning. You can find more about me here: https://bashthebuilder.github.io/


  • PhD The Chinese University of Hong Kong, (2014)

Research and professional activities

Research interests

Information Retrieval (IR)

The goal is to propose novel computational methods for ad-hoc IR using one of these machine learning methods.

Key words: Document Content Analysis
Open to supervise

Natural Language Processing (NLP)

The goal is to propose novel methods in NLP.

Key words: Deep Learning for NLP
Open to supervise

Teaching and supervision

Current teaching responsibilities

  • Team Project Challenge (EE) (CE293)

  • Team Project Challenge (WBL) (CE299)

  • Information Retrieval (CE306)

  • Information Retrieval (CE706)

  • Text Analytics (CE807)

  • Team Project Challenge (CS) (CE291)


Journal articles (3)

Jameel, S., Lam, W. and Bing, L., (2015). Supervised topic models with word order structure for document classification and retrieval learning. Information Retrieval Journal. 18 (4), 283-330

Bing, L., Jiang, S., Lam, W., Zhang, Y. and Jameel, S., (2015). Adaptive Concept Resolution for document representation and its applications in text mining. Knowledge-Based Systems. 74, 1-13

Bing, L., Lam, W., Wong, T-L. and Jameel, S., (2015). Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context. ACM Transactions on Information Systems. 33 (2), 1-38

Conferences (29)

Jameel, S. and Schockaert, S., (2020). Word and document embedding with VMF-mixture priors on context word vectors

Correia, A., Fonseca, B., Paredes, H., Schneider, D. and Jameel, S., (2019). Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains

Correia, A., Paredes, H., Schneider, D., Jameel, S. and Fonseca, B., (2019). Towards Hybrid Crowd-AI Centered Systems: Developing an Integrated Framework from an Empirical Perspective

Camacho-Collados, J., Espinosa-Anke, L., Jameel, S. and Schockaert, S., (2019). A Latent Variable Model for Learning Distributional Relation Vectors

Correia, A., Jameel, S., Schneider, D., Fonseca, B. and Paredes, H., (2019). The Effect of Scientific Collaboration on CSCW Research: A Scientometric Study

Jameel, S., Fu, Z., Shi, B., Lam, W. and Schockaert, S., (2019). Word embedding as maximum a posteriori estimation

Jameel, S., Bouraoui, Z. and Schockaert, S., (2018). Unsupervised Learning of Distributional Relation Vectors

JAMEEL, S. and SCHOCKAERT, S., (2017). Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding

Shi, B., Lam, W., Jameel, S., Schockaert, S. and Lai, KP., (2017). Jointly Learning Word Embeddings and Latent Topics

Jameel, S., Bouraoui, Z. and Schockaert, S., (2017). MEmbER: Max-Margin Based Embeddings for Entity Retrieval

Jameel, S. and Schockaert, S., (2016). D-GloVe: A feasible least squares model for estimating word embedding densities

Jameel, S. and Schockaert, S., (2016). Entity embeddings with conceptual subspaces as a basis for plausible reasoning

Schockaert, S. and Jameel, S., (2016). Plausible reasoning based on qualitative entity embeddings

Liao, Y., Lam, W., Jameel, S., Schockaert, S. and Xie, X., (2016). Who Wants to Join Me?

Jameel, S., Liao, Y., Lam, W., Schockaert, S. and Xie, X., (2016). Exploring Urban Lifestyles Using a Nonparametric Temporal Graphical Model

Liu, P., Jameel, S., Wu, KK. and Meng, H., (2016). Learning Track Representation and Trends for Conference Analytics

Jameel, S., Lam, W., Schockaert, S. and Bing, L., (2015). A Unified Posterior Regularized Topic Model with Maximum Margin for Learning-to-Rank

Liao, Y., Jameel, S., Lam, W. and Xie, X., (2015). Abstract Venue Concept Detection from Location-Based Social Networks

Jameel, S., Lam, W. and Bing, L., (2015). Nonparametric Topic Modeling Using Chinese Restaurant Franchise with Buddy Customers

Liu, P., Jameel, S., Lam, W., Ma, B. and Meng, H., (2015). Topic modeling for conference analytics

Bing, L., Lam, W., Jameel, S. and Lu, C., (2014). Website Community Mining from Query Logs with Two-Phase Clustering

Jameel, S. and Lam, W., (2013). A Nonparametric N-Gram Topic Model with Interpretable Latent Topics

Jameel, S. and Lam, W., (2013). An unsupervised topic segmentation model incorporating word order

Jameel, S. and Lam, W., (2013). An N-Gram Topic Model for Time-Stamped Documents

Jameel, S., Lam, W. and Qian, X., (2012). Ranking Text Documents Based on Conceptual Difficulty Using Term Embedding and Sequential Discourse Cohesion

Jameel, S., Qian, X. and Lam, W., (2012). N-gram fragment sequence based unsupervised domain-specific document readability

Jameel, S. and Qian, X., (2012). An Unsupervised Technical Readability Ranking Model by Building a Conceptual Terrain in LSI

Jameel, S., Lam, W., Qian, X. and Au Yeung, C-M., (2012). An unsupervised technical difficulty ranking model based on conceptual terrain in the latent space

Jameel, S., Lam, W., Au Yeung, C-M. and Chyan, S., (2011). An unsupervised ranking method based on a technical difficulty terrain




5A.529, Colchester Campus

Academic support hours:

Tuesday: 12:00 PM until 1:00 PM (Zoom ID: 97561813599)

More about me

Follow me on social media