Behavioral Data Science lab

The Behavioral Data Science lab is a research group in the UTS Data Science Institute at the University of Technology Sydney, Australia.

Our research aims to make sense of large amounts of behavioral data, to understand online behavior and its offline effects. We develop core methods in machine learning and optimization in order to distill heaps of raw data into meaningful insights in areas including

  • social and information network analysis,
  • information diffusion across social networks,
  • mis- and dis-information spreading,
  • tomorrow's labour markets and labour transitions,
  • machine learning,
  • computational social science,
  • data mining, and
  • natural language processing

Two 1-Year Postdoc and multiple funded PhD positions available in detecting and modelling mis-/dis-information spread. If you are interested in working with us, please get in touch.

Recent News

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  • May 2025
    Andrei successfully led the volunteer program for The Web Conference 2025 in Sydney, coordinating a team of 55 students who supported this premier international event. As Volunteer Chair, Andrei managed everything from audio-visual systems to registration support, contributing to the success of WWW'25 which attracted ~1,300 delegates.
  • May 2025
    🏆 Our paper Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study by Frankie, Philipp and Andrei was awarded the prestigious Best Paper Award at The Web Conference 2025 (WWW'25)! This groundbreaking work uses inverse reinforcement learning to uncover connection patterns that traditional topic analysis overlooks, demonstrating how user behaviors reveal deeper social structures. The award was presented during the closing ceremony in Sydney, where Andrei also served on the local organizing committee. So well done, Frankie and Philipp!
  • Apr 2025
    🎓 Congratulations to Pio Calderon who successfully defended his PhD thesis on ‘Modeling the Online Spread of Ideas in a Finite Attention Environment’ in the Behavioral Data Science lab! During his time with us, Pio published in top venues including ECML-PKDD, ICWSM, IEEE TCSS, and JMLR. His innovative work on the Opinion Market Model and Bayesian Mixture Hawkes processes has significantly advanced our understanding of online information dynamics. We're proud of your achievements, Pio!
  • Mar 2025
    Huixiang Fu started her PhD, focusing on detecting and tackling misinformation using advanced machine learning techniques. Welcome, Huixiang!
  • Jan 2025
    📄 The paper Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data by Pio, Alex and Andrei was published in the prestigious IEEE Transactions on Computational Social Systems. This work advances how we can model data with varying time granularity!

Recent Posts

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Jun 20, 2025 Research, blogpost

Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement

We present IC-Mamba, a state space model that that forecasts social media engagement by modeling intervalcensored data with integrated temporal embeddings.

May 9, 2025 Research, blogpost

Some Reddit users just love to disagree, new AI-powered troll-spotting algorithm finds

Our article from the The Conversation from our paper presented at WWW'25.

Oct 13, 2023 Research, blogpost

Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions

We introduce the Opinion Market Model (OMM) as a testbed to measure the impact of positive interventions on online opinion dynamics.