����logo���

Overview

Highlights

• I am co-PI of the research project V&A Design Generator which was funded by the AHRC in 2022:
  

Office hours

Please contact me on Birbeck's MS Teams channel.

Web profiles

ORCID

Research

Research interests

• Computational Social Science.
• Web Data Extraction.
• Network Science: centrality.
• Artificial Intelligence in Finance.
• Artificial Intelligence in Sports.

Research Centres and Institutes

• ,

Supervision and teaching

Supervision

Supervisions external to ����logo���:

3

Supervisions within ����logo���:

1

I mentor MSc/PhD students working on AI in Finance, Computational Social Science, Sentiment Analysis, Web algorithmics and general Artificial Intelligence and Data Science topics.

These 10-words summaries align well with my current research:

a. 'AI in Finance: dynamic risk assessment with real-time data streams'
b. 'Architectures for cognitive agents in LLM workflows: adaptation, collaboration & safety.'
c. 'Sentiment Analysis/NLP architectures to analyse streaming news: applications to Finance.'
d. 'Sentiment Analysis/NLP architectures to monitor online discussion threads: online extremism.'
e. 'Network science methods for enhanced social web discovery and navigation.'

Doctoral alumni since 2013-14

• YLLI PRIFTI

  Nature of supervision:

  Principal Supervisor, Jan 2018

  Title of thesis:

  The Emergence of Interpersonal and Social Trust in Online Interactions

  Description:

  Abstract

  
  My PhD work is in the area of extracting and modelling user-created data on the web.
  In particular, I focussed on locating and extracting user data that ’signals’ the evolution of
  human, 1-on-1 interactions between participants of large social networks who are forever
  stranger to each other.
  The booming of ”Online Social Networks” created an opportunity for social scientists
  to study social phenomena at a scale unseen before. The vast amount of information
  combined with computer science techniques led to significant developments in a relatively
  new field: Computational Social Science. Furthermore, recently the Gig Economy
  and mass adoption of ”business sharing” sites such as Airbnb, Uber, or JustEat drove a
  new wave of computational social science research into reviews, feedback, and recom-
  mendations. All these ingredients of the larger Social Trust have been vastly discussed
  in the literature, in both the social aspect and computational models of trust.
  However, some fundamental gaps remain, and there is often confusion about when trust is
  being expressed and how reviews (or recommendations) relate to social trust. Additionally,
  the computational trust models found in the literature tend to either be entirely theoretical
  or focused on a specific data set, thus lacking universal applicability. The latter problem,
  I believe, was due to a lack of data available to researchers in the early stages of the web.
  Today, the broader Online Social Networks have matured and consolidated mechanisms
  for allowing access to data. Access to information is rarely trivial for more specialised
  and smaller online communities. Yet smaller, focussed platforms are precisely where
  social trust and interactions could be observed (or not observed) and perhaps acquire a
  meaning that approaches the social trust social scientists see in in-person interactions.
  To address this gap, we initially propose and discuss the following research question:

  ”Is there a meeting point between online interactions and social trust so that the core
  components of trust are retained?” We addressed this general open question by working
  on a computational architecture for data retrieval in social media platforms that can be
  suitably generalised and re-applied to different platforms. Lastly, as we enjoy the luxury
  of vast amounts of data that closely represent interpersonal and social trust, we addressed
  the question of ”what models of trust emerge from data” and ”how do existing models
  of trust perform with the data available”.

  ...

  Awarded:

  2023

Teaching

I mostly teach graduate modules in Data Science.

In the past I have taught Artificial Intelligence, Algorithms, Databases and Theoretical Computer Science at BSc and MSc levels.

Teaching modules

• Demystifying Computing with Python
• Data Science Techniques and Applications

Publications

External Repositories

Business and community

Outreach

Services

• Consultancies and expert advice
  2018-03-01T00:00:00

  I am academic advisor to , a start-up company by former ����logo��� doctoral students.