Md Zia Ullah
md zia ullah

Dr Md Zia Ullah

Lecturer

 0131 455 4761

  M.Ullah@napier.ac.uk

   

Biography

Md Zia Ullah is a Lecturer in the School of Computing, Engineering & Built Environments at Edinburgh Napier University. He received his PhD degrees in the Department of Computer Science and Engineering from the Toyohashi University of Technology, Japan in 2016. His PhD thesis focused on Bipartite Graph based Ranking Models and its Application to Information Retrieval and Bioinformatics.

After his PhD, he was appointed as a Post-doctoral researcher at Universite Toulouse - Paul Sabatier, CNRS, and Universite de Toulouse - Jean Jaures, Toulouse, France from 2017 to 2022 where he worked in Information retrieval, Natural language processing, and Applied machine learning (Deep learning). During his Post-doc, he also worked on the EU H2020 Project PREVISION on Machine learning applications and lectured Data analytics at the Université Toulouse I Capitole, France.

He also received his B.Sc. (Hons.) degree from the University of Chittagong, Bangladesh in 2010 and his M.Eng. degree from the Toyohashi University of Technology in 2013.


RESEARCH INTERESTS

His research interests lie primarily in
- Information retrieval (IR),
- Natural language Processing (NLP),
- Applied machine learning (ML), and
- Deep learning

His research has focused on
- Adaptive information retrieval,
- Query performance prediction (QPP),
- Search Intent mining and diversification,
- Check-worthiness prediction,
- Statistical analysis of IR parameters, and
- Deep learning-based histopathological image recognition.


PUBLICATIONS

He has co-patented an adaptive IR technique and published his research outcomes in the international journals and conferences, including

* ACM Transactions on Information Systems (TOIS),
* ACM Transactions on Intelligent Systems and Technology (TIST),
* ACM Special Interest Group on Information Retrieval (SIGIR),
* ACM Conference on Information and Knowledge Management (CIKM),
* European Conference on Information Retrieval (ECIR), and
* IEEE Engineering in Medicine and Biology Society (EMBS).

He actively participated in evaluation forums such as TREC, CLEF, and NTCIR for obtaining the benchmark datasets, evaluating his research ideas, and comparing with the related methods.


PROFESSIONAL SERVICE

He is actively involved as a Program committee (PC) member for the following international conferences:

* ACM Special Interest Group on Information Retrieval (SIGIR), (2018~),
* ACM Conference on Information and Knowledge Management (CIKM), (2019~),
* ACM International Conference on Web Search and Data Mining (WSDM), (2020~),
* ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD), (2020~),
* European Conference on Information Retrieval (ECIR), (2019~),
* European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), (2022~),
* Conference and Labs of the Evaluation Forum (CLEF), (2017~).

CURRENT PROJECTS

* False webs: Network to address the misinformation pandemic (2023 -- 2025), Royal Society Edinburgh, Co-PI


PAST PROJECTS (Involved as a researcher)

* PREVISION (EU Horizon 2020), 2019 -- 2021, 28 Partners, 9.4M)
* FabSpace 2.0 (EU Horizon 2020), 2015 --2018, 7 Partners, 3.4M)

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21 results

Can we predict QPP? An approach based on multivariate outliers

Conference Proceeding
Chifu, A., Déjean, S., Garouani, M., Mothe, J., Ortiz, D., & Ullah, M. Z. (2024)
Can we predict QPP? An approach based on multivariate outliers. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part III. https://doi.org/10.1007/978-3-031-56063-7_38
Query performance prediction (QPP) aims to predict the success and failure of a search engine on a collection of queries and documents. State of the art predictors can enable ...

Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction

Journal Article
Aziz, A., Hossain, M. A., Chy, A. N., Ullah, M. Z., & Aono, M. (2023)
Leveraging contextual representations with BiLSTM-based regressor for lexical complexity prediction. Natural Language Processing Journal, 5, Article 100039. https://doi.org/10.1016/j.nlp.2023.100039
Lexical complexity prediction (LCP) determines the complexity level of words or phrases in a sentence. LCP has a significant impact on the enhancement of language translations...

Comparison of machine learning models for early depression detection from users’ posts

Book Chapter
Mothe, J., Ramiandrisoa, F., & Ullah, M. Z. (2022)
Comparison of machine learning models for early depression detection from users’ posts. In F. Crestani, D. E. Losada, & J. Parapar (Eds.), Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project (111-139). Springer. https://doi.org/10.1007/978-3-031-04431-1_5
With around 300 millions people worldwide suffering from depression, the detection of this disorder is crucial and a challenge for individual and public health. As with many d...

Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach

Presentation / Conference Contribution
Mothe, J., & Ullah, M. Z. (2021, November)
Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach. Presented at 30th ACM International Conference on Information & Knowledge Management, Queensland, Australia
A search engine generally applies a single search strategy to any user query. The search combines many component processes (e.g., indexing, query expansion, search-weighting m...

Exploiting various word embedding models for query expansion in microblog

Presentation / Conference Contribution
Ahmed, S., Chy, A. N., & Ullah, M. Z. (2020, December)
Exploiting various word embedding models for query expansion in microblog. Presented at 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), Kuching, Malaysia
Microblogs, especially Twitter, make it easier to communicate with others in a real-time manner and is treated as a valuable information source. With the increasing amount of ...

Forward and backward feature selection for query performance prediction

Presentation / Conference Contribution
Déjean, S., Ionescu, R. T., Mothe, J., & Ullah, M. Z. (2020, March)
Forward and backward feature selection for query performance prediction. Presented at 35th Annual ACM Symposium on Applied Computing, Brno, Czech Republic
The goal of query performance prediction (QPP) is to automatically estimate the effectiveness of a search result for any given query, without relevance judgements. Post-retrie...

Query expansion for microblog retrieval focusing on an ensemble of features

Journal Article
Chy, A. N., Ullah, M. Z., & Aono, M. (2019)
Query expansion for microblog retrieval focusing on an ensemble of features. Journal of Information Processing, 27, 61-76. https://doi.org/10.2197/ipsjjip.27.61
In microblog search, vocabulary mismatch is a persisting problem due to the brevity of tweets and frequent use of unconventional abbreviations. One way of alleviating this pro...

Studying the variability of system setting effectiveness by data analytics and visualization

Presentation / Conference Contribution
Déjean, S., Mothe, J., & Ullah, M. Z. (2019, September)
Studying the variability of system setting effectiveness by data analytics and visualization. Presented at Experimental IR Meets Multilinguality, Multimodality, and Interaction: 10th International Conference of the CLEF Association (CLEF 2019), Lugano, Switzerland
Search engines differ from their modules and parameters; defining the optimal system setting is challenging the more because of the complexity of a retrieval stream. The main ...

Information nutritional label and word embedding to estimate information check-worthiness

Presentation / Conference Contribution
Lespagnol, C., Mothe, J., & Ullah, M. Z. (2019, July)
Information nutritional label and word embedding to estimate information check-worthiness. Presented at 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris
Automatic fact-checking is an important challenge nowadays since anyone can write about anything and spread it in social media, no matter the information quality. In this pape...

Learning to adaptively rank document retrieval system configurations

Journal Article
Deveaud, R., Mothe, J., Ullah, M. Z., & Nie, J.-Y. (2019)
Learning to adaptively rank document retrieval system configurations. ACM transactions on information systems, 37(1), Article 3. https://doi.org/10.1145/3231937
Modern Information Retrieval (IR) systems have become more and more complex, involving a large number of parameters. For example, a system may choose from a set of possible re...

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