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Dr Bikesh Raj Upreti

Lecturer in Business Information Sy

School of Business
Faculty of Business, Economics and Law
b.upreti@business.uq.edu.au

Overview

Bikesh Raj Upreti is a Lecturer in the Department of Business Information Systems at the University of Queensland (UQ) in Brisbane, Australia. He completed his doctoral degree from Aalto University School of Business, Helsinki. His doctoral dissertation, " Untangling the Application of Text-mining Methods in Information Systems Domain", focused on developing applications to uncover insights from the large-scale text data. After graduating, he continued as a postdoctoral researcher and visiting scholar at the Department of Information Service Management, Aalto Business school, before joining the University of Queensland.

Bikesh's research interests are in the areas of applied computational methods and quantitative inquiry of inter-disciplinary phenomena. He has applied advanced machine learning, deep learning and other analytical tools for large-scale behavioural and predictive analytics set in Information systems, marketing, finance, and political discourses. His work has been published in several journals (European Journal of Information systems, Industrial Marketing Management, Journal of Travel Research, Electronic Markets) and peer-reviewed conference proceedings (ICIS, HICSS, and Bled).

His work has won the inaugural edition of the Paper-at-hon competition at ICIS 2017, the Best paper award at the Bled conference 2019, and the nomination for the best paper award at HICSS 2020. He also actively serves as a reviewer for the journals such as (European Journal of Information systems, Decision Sciences, Internet research and Information & Management, and International Journal of Information Management) and conferences (ICIS, ECIS, HICSS, AMCIS, PACIS).

Publications

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Publications

Book Chapter

  • Knowledge-driven approaches for financial news analytics

    Upreti, Bikesh Raj, Back, Philipp Martin, Malo, Pekka, Ahlgren, Oskar and Sinha, Ankur (2019). Knowledge-driven approaches for financial news analytics. Network Theory and Agent-Based Modeling in Economics and Finance. (pp. 375-404) edited by Anindya S. Chakrabarti, Lukáลก Pichl and Taisei Kaizoji. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-13-8319-9_19

Journal Article

Conference Publication

  • Detecting political bots on twitter during the 2019 Finnish parliamentary election

    Rossi, Sippo, Rossi, Matti, Upreti, Bikesh Raj and Liu, Yong (2020). Detecting political bots on twitter during the 2019 Finnish parliamentary election. 53rd Hawaii International Conference on System Sciences 2020, Wailea, HI, United States, 7 - 10 January 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.24251/HICSS.2020.298

  • Understanding the creation of trust in cryptocurrencies: bitcoin

    Marella, Venkata, Uperti, Bikesh and Merikivi, Jani (2020). Understanding the creation of trust in cryptocurrencies: bitcoin. 32nd Bled eConference Humanizing Technology for a Sustainable Society, Bled, Slovenia, 16 - 19 June 2019. Maribor, Slovenia: University of Maribor Press. doi: 10.18690/978-961-286-280-0.44

  • Analyzing the ways IT has changed our TV consumption: binge watching and marathon watching

    Upreti, Bikesh Raj, Merikivi, Jani, Bragge, Johanna and Malo, Pekka (2018). Analyzing the ways IT has changed our TV consumption: binge watching and marathon watching. ICIS 2017: Transforming Society with Digital Innovation, Seoul, South Korea, 10 - 13 December 2017. Atlanta, GA, United States: Association for Information Systems.

  • To reach the clouds: application of topic models to the meta-review on cloud computing literature

    Upreti, Bikesh Raj, Asatiani, Aleksandre and Malo, Pekka (2016). To reach the clouds: application of topic models to the meta-review on cloud computing literature. 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, United States, 5 - 8 January 2016. Washington, DC, United States: I E E E Computer Society. doi: 10.1109/hicss.2016.493

This staff member is a UQ Expert for media in the following fields:

Artificail Inteligence, Social Media analytics, Big data, Information Systems

They are happy to lend their expertise to your articles or broadcasts and share their research discoveries and insights with the community via media channels.

For additional assistance with story ideas, general advice and information or help with seeking further experts, please email the UQ Media Team or telephone (07) 3365 1120.

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ORCID: 0000-0002-5632-4720

Personal Profile: Link

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