Gholamreza (Reza) Haffari

Senior Lecturer (= North American Associate Prof.)          
Director of the Graduate Diploma in Data Science
Faculty of Information Technology
Monash University
Clayton, VIC 3800, Australia

Email: gholamreza.haffariATmonash.edu
Office: 131A, Bulding 63
Tel: +61 3 9905-8106
Fax: +61 3 9905-5159

My research spans two broad areas: Natural Language Processing and Machine Learning. Understanding human language by computers has been a central goal of AI. Human language is an intricate system; each sentence has its own grammatical structure, inter-connected references, and set of possible meanings. The field of Natural Language Processing (NLP) aims to build computational models of language in order to make predictions based on real-world textual data. Example applications of NLP include machine translation, information extraction, and question answering. Tools developed for these problems are increasingly becoming part of daily life, from speech and dialogue systems on mobile devices to structured search on the web to real-time translation. NLP is a rich intersection of formal modeling, applied algorithms and scalable data systems, and has served as an important application domain for related fields such as Machine Learning (ML).

My research aligns with the Machine Learning flagship in our faculty. In the past, I have organised the following reading groups in our faculty: Deep Learning Reading Group, The Machine Learning Book Reading Group, and Natural Language Processing Reading Group.

Prospective students: Please read this before you contact me.

Research Areas

  • Deep learning methods, particularly why they work and how to use them well.
  • Structured prediction, particularly predicting complex linguistic structure.
  • Discourse, semantics, syntax, and morphology; particularly for machine translation.
  • Advanced data structures (eg succinct suffix trees) for large-scale NLP problem, such as language modelling.
  • Learning with limited amounts of supervision and large amount of un-annotated data; learning across different domains of data.
  • Learning and inference in probabilistic graphical models, particularly for NLP problems.
  • Non-parametric Bayesian models, particularly for NLP.
  • Reinforcement learning, Markov decision processes, and multi-armed bandit.
  • Dialogue systems, particularly with the deep learning approach.
  • Learning programs from data, particularly with deep learning.


  • Local Co-Chair of the Australian Language Technology Association (ALTA) workshop, which will be held in Monash (Dec 2016). Please consider submitting a paper.

  • Attended NAACL 2016 (San Diego) and ICML2016 (New York), 2016.

  • Together with Trevor Cohn, we will have a postdoc position available beginning in the first half of 2016. The duration will be three years, and the topic is on deep learning, semantics, and machine translation. Please feel free to contact Trevor/me if you are interested and have a strong background in machine learning and statistical NLP.

  • Dec 2015: Attended NIPS 2015 in Montreal, lots of interesting papers to read and follow up.

  • Nov 2015: Our grant proposal is funded: Learning Deep Semantics for Automatic Translation between Human Languages. 2016 – 2019. ARC Discovery Project with Trevor Cohn, $450K.

  • Sep 2015: Congratulations to my PhD student, Ajay Ganesh, for a great PhD thesis and graduation. Ajay has joined Andrew Mccallum's group in UMass-Amherst as a postdoc.

  • July-Aug 2015: Excited to be part of the team for Continuous Wide-band Machine Translation workshop held in University of Washington, Seattle.