Prasanta Bhattacharya

Prasanta Bhattacharya

Innovation Lead & Senior Research Scientist  ·  Behavioral Science, AI & Network Science  ·  Executive Education, Strategy & Decision-making

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I am an applied research scientist and innovation lead at A*STAR Singapore, working at the intersection of AI, behavioral science, and network analytics. I design and lead interdisciplinary studies with a strong focus on translating research insights into measurable business impact. I have led multi-year collaborations with internal and external partners, and managed a seven-figure portfolio of funded research initiatives across domains.

In addition to my research leadership, I have served as business school faculty, teaching postgraduate students and senior executives across Asia and Europe. I am a Prosci® Certified Change Practitioner (ADKAR), and also trained in R&D value-sizing frameworks, tech transfer, and contract law.

My research bridges topics and methods from behavioural science, network science, and machine learning to address challenging problems in emerging markets. The following are examples of topics that I am particularly interested in:

  1. Analyzing user behavior on large and complex social networks using digital trace data, focusing particularly on the estimation of network effects. (Skim through this and this for examples of work in this area).
  2. Building computational models of cognition (e.g., attitudes or motivations) and behavior (e.g., stances) using fine-grained data from online platforms, and studying the effectiveness of emerging applications that can benefit from this e.g., designing human-centric AI models.
  3. Answering causal inference problems in emerging business contexts using observational time-series data. This is particularly important in the context of impact/intervention evaluation problems where randomized experiments (e.g., A/B tests) are not feasible. See this for a good example.

I bring over 13 years of experience across academia, industry, and research consulting. I have collaborated with partners from AI, social media, education, and finance sectors. Across these engagements, I have:

  • Generated actionable business insights on consumer behavior at scale
  • Built analytics frameworks that support strategic decision-making
  • Helped organizations extract measurable value from their data investments
  • Designed and delivered executive training programs in analytics and AI across APAC and Europe

If you see alignment or would like to explore a potential collaboration, feel free to reach out: e‑mail  ·  LinkedIn.

AI-Enabled Qualitative Research

LLM-assisted interviewing and theme generation

AI-Enabled Psychometrics

Measuring attitudes and stances from text using LLMs.

Policy Effects On Social Networks

Unbiased estimation of policy impact under network interference.

Online Polarization & Misinformation

Analyzing how polarization and misinformation manifest on digital platforms

Personalized Sustainable Finance

AI-enabled personalization for sustainable finance advisory

Psychometrics & Financial Behavior

Analyzing the link between psychological factors, financial literacy, and financial behaviour

Selected list of past research projects with industry partners

User engagement with music recommendations [Music streaming app]

Estimating causal impact of new track releases on music recommendation platforms

User engagement with on-demand online tutoring [On-demand learning app]

Characterising learner and tutor engagement in an on-demand online tutoring platform in Asia.

Credit risk assessment [Digital finance]

Estimating creditworthiness in an Asian microlending platform using alternative datasets.

User task behavior in online search [Web search]

Identifying user search tasks using session logs from an online search engine

  1. Ang, E.T.Y.; Bhattacharya, P.; Lim, A.E.B. (2025). Estimating Policy Effects in a Social Network with Independent Set Sampling. Social Networks. Link
  2. Santy, S.; Bhattacharya, P.; Ribeiro, M.H.; Allen, K.R.; Oh, S. (2025). Position: When Incentives Backfire, Data Stops Being Human. ICML 2025. Link
  3. Belik, I.; Bhattacharya, P.; Knudsen, E.S. (2024). A Case for Simulated Data and Simulation-based Models in Organizational Network Research. Research Policy. Link
  4. Bhattacharya, P.; Gupta, R.K.; Yang, Y. (2021). Exploring the Contextual Factors Affecting Multimodal Emotion Recognition in Videos. IEEE Transactions on Affective Computing. Link
  5. Bhattacharya, P.; Phan, T.Q.; Bai, X.; Airoldi, E. (2019). Analyzing the Coevolution of Network Structure and Content Generation in Online Social Networks. Information Systems Research. Link

Recent Conferences

  • 42nd International Conference on Machine Learning (ICML 2025, Vancouver) — Position paper on sustaining human-generated data for ML. Presented by co-author (Sebastin Santy)
  • International School and Conference on Network Science (NetSci 2025, Maastricht) — Fragility in Food Supply Chain. Presented by co-author (Eugene Ang)
  • 11th International Conference on Computational Social Science (IC2S2 2025, Norrköping) — Agent-based model of affective polarization. Presented by co-author (Subhayan Mukerjee)
  • 16th Asian Conference on the Social Sciences (ACSS 2025, Tokyo) — Are LLMs effective at conducting thematic analysis?. Presented by co-author (Aimee E. Pink)

Selected Past Talks

  • July 2025 — Evolving Considerations around LLM Use. Summer Institute in Computational Social Science (SICSS), Singapore
  • June 2024 — Analysing Social Networks: Concepts, Methods and Applications. SICSS, Singapore — Link
  • Nov 2023 — Masterclass: Innovating with Network Science: Concepts, Methods and Applications. International Conference on Management Research (ICMR 2023), IIT Madras
  • Aug 2022 — Estimating Social Influence in Online Networks: Challenges, Methods, and Applications. Socioeconomic Networks and Network Science Workshop, Waseda University
  • Feb 2021 — Emerging Applications of AI & Behavioral Analytics in Financial Services. Monetary Authority of Singapore (MAS) & National Supercomputing Centre (NSCC) — Link
  • Aug 2017 — Working with Social Network Data: Promises and Pitfalls. PyData SG, Google — Link