Biography

Mohammad Akbari is Senior Research Associate in Department of Computer Science, University College London. His research focuses on the applications of machine learning and natural language processing for information retrieval, web search and mining and large scale information access in general, with an emphasis on their applications in health informatics, social informatics, information retrieval, recommendation system and samrt cities. His research has been published in several major academic venues, including SIGIR, WSDM, ICMR, etc.
He is working with Professor Jun Wang on web search and reinforecement learning. He is also closely collaborating with NYU Center for Data Sciences and Tandon Computer Science and Engineering where he is a research scientist in Professor Rumi Chunara's Lab working on wellness profiling of users and communities on social networks.
He obtained a PhD in Integrative Sciences and Engineering from the National University of Singapore, advised by Professor Tat-Seng Chua.



Latest News

  • 11/2018

    I am invited to be a program committee member in NAACL 2019 .

  • 08/2018

    Our paper on "Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets", was accepted by CIKM 2018.

  • 1/2018

    I am invited to be a program committee member in ACL 2018 , SIGIR 2018 , CHI 2018 , and ICWSM 2018 .

  • 11/2017

    One paper on "Vertical Domain Text Classification: Towards Understanding IT Tickets using Deep Neural Networks ", was accepted by AAAI 2018.

  • 7/2016

    Our paper on "On the Organization and Retrieval of Health QA Records for Community-based Health Services", was selected for Best Paper Award by IJCAI, BOOM.

Research Interests

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My area of interest spans Media Search and Retrieval. Within the range, my current research focus is on the area of Knowledge Management and Organization in Social Media, KnowledgeGraph extraction and embedding, and Multimodal information fusion and rerrieval. Specifically, I am interested in structuralizing and organizing User Generated Contents (UGCs) for healthcare and well-being domain.

Research Projects

  • Knowledge Organization in Health Domain

    Community-based services leverage the wisdom of crowd through supporting communication, information sharing, and collaboration between individual users. Unsurprisingly, the impact of social media has been extended to the health care domain, as consumers have begun to seek information, share knowledge and experiences online. Therefore it imposes a greater impact on people’s daily information seeking, knowledge construction, and decision making. Although online community-based health services (CBHS) accumulate a huge amount of knowledge and grow at a continuously increasing pace, this knowledge is not effectively accessible due to the unstructured, noisy and opaque nature of data. To enhance the aggregation, navigation, and access into knowledge of the crowd, this research explores techniques to automatically analyze, organize, and retrieve large scale user generated contents (UGCs) on CBHSs.

  • Multi-Modal Information Retrieval and Ranking

    Information reranking is to recover the true order of the inittial search results. Traditional reranking approaches, such as graph-based and pseudo-based, have achieved great success for uni-modal queries. They, however, suffer from some intrinsic limitations. (1) They only capture the pairwise relations instead of high-order relations, which may lead to information loss; (2) They also usually simply concatenate heterogeneous features into one vector that may cause the curse of dimensionality, and neglect the effects of different type of features. In this work, we investigate to find a unified multi-modal framework for multi-modality information retrieval system, where the queries can be mixture of texts, images, videos and audios.

Selected Publications
[Google Scholar] [DBLP]

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Please send e-mail to me to request (p)reprints of papers that do not have a downloadable pdf associated with them.

2016

2015

2014

  • Nie, Liqiang, Mohammad Akbari, Tao Li, and Tat-Seng Chua, A joint local-global approach for medical terminology assignment, In Medical Information Retrieval Workshop at SIGIR 2014.
  • Nie, Liqiang, Tao Li, Mohammad Akbari, Jialie Shen, and Tat-Seng Chua, Wenzher: Comprehensive vertical search for healthcare domain, Annual ACM SIGIR Conference.

Professional Experiences and Services

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Teaching

  • Teaching Assistant, National University of Singaopore, 2015
    Social Media Computing, CS4242
  • Lecturer, Azad University, 2006-2015
    Neural Network, Artificial Intelligence, Compiler Design, Programming Languages Design and Implementation
  • Invited Lecturer, Iran University of Science and Technology, 2008-2009
    Compiler Design, Artificial Intelligence, Advanced Programming

Journal and Conference Reviewer

  • IEEE Transaction of Knowledge and Data Engineering (TKDE).
  • ACM Transaction on Knowledge Discovery from Data.
  • Pattern Recognition Letters Journal.
  • World Applied Science Journal
  • ACM Multimedia Conference ACM MM 2015
  • World Wide Web and Population Search at AAAI 2015
  • IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015)
  • IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2015
  • IEEE AINL-ISMW 2015

Blog

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I I occasionally update my blog here.

Contact Information

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Contact info

The best way of cummmunicating with me is through Twitter. However, there is my contact information.

  • 13 Computing Dr, 117417
  • akbari@u.nus.edu

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