Bio

Aadesh Salecha is a computer scientist who works on integrating Artificial Intelligence and Psychology. He graduated from the University of Minnesota, where he worked with Prof. Jaideep Srivastava on misinformation spread and its mitigation mechanisms. At Stanford, he works with Prof. Johannes Eichsteadt on using cognitive psychology and psychometrics to understand bias development in Large Language Models. He is also collaborating the effort on the creation of new-age psychological interventions using AI to democratize access and improve personalization and retention. His work is focused on using computational methods for societal good by facilitating the measurement and causal analysis of population health metrics like drug abuse, subjective-wellbeing, etc.

Contact

Additional Info

  • Mail Code: 9025

All Publications

  • A computational model of reward learning and habits on social media. Nature communications Turner, G., Gunschera, L. J., Subrahmanya, S., Salecha, A., Eichstaedt, J. C., Palminteri, S., Orben, A. 2026

    Abstract

    Social media have fundamentally transformed how we live and communicate. However, the methods to study how our cognitive systems interact with technology platforms are very limited. Computational modelling represents a new avenue to uncover the finegrained cognitive processes driving social media behaviour. Here, we develop a computational model of real-world social media posting data, adapted from the animal reward learning literature. Using a Twitter (currently X) dataset (n = 2696 users), including a preregistered replication, we show that a hybrid reinforcement learning and habitual cognitive process underlies social media posting behaviour. More frequent posters show more signs of habitual behaviour. Further, younger people and women are more driven by reinforcement learning - updating their strategy more adaptively to maximise social media rewards - while older users and men are more habitual.

    View details for DOI 10.1038/s41467-026-73547-6

    View details for PubMedID 42243108

  • Media hype regarding psychedelic treatments for depression and PTSD from 2017 to 2024. Scientific reports Evers, A., Stade, E. C., Salecha, A., Tait, Z., Khazanov, G. 2026

    View details for DOI 10.1038/s41598-026-50186-x

    View details for PubMedID 42034826

  • No Good Time to Negotiate? Effective Track Two Dialogue in Protracted Israel-Palestine Conflict Escalation CONFLICT RESOLUTION QUARTERLY Fink, O., Graf, W., Subrahmanya, S., Salecha, A., Eichstaedt, J. C. 2026

    View details for DOI 10.1002/crq.70032

    View details for Web of Science ID 001700862100001

  • Monitoring the opioid epidemic via social media discussions. NPJ digital medicine Smith, D. A., Lavertu, A., Salecha, A., Hamamsy, T., Humphreys, K., Lembke, A., Kiang, M. V., Altman, R. B., Eichstaedt, J. C. 2025; 8 (1): 284

    Abstract

    The opioid epidemic persists in the U.S., with over 80,000 deaths annually since 2021, primarily driven by synthetic opioids. Responding to this evolving epidemic requires reliable and timely information. One source of data is social media platforms. We assessed the utility of Reddit data for surveillance, covering heroin, prescription, and synthetic drugs. We built a natural language processing pipeline to identify opioid-related content and created a cohort of 1,689,039 Reddit users, each assigned to a state based on their previous Reddit activity. We measured their opioid-related posts over time and compared rates against CDC overdose and NFLIS report rates. To simulate the real-world prediction of synthetic opioid overdose rates, we added near real-time Reddit data to a model relying on CDC mortality data with a typical 6-month reporting lag. Reddit data significantly improved the prediction accuracy of overdose rates. This work suggests that social media can help monitor drug epidemics.

    View details for DOI 10.1038/s41746-025-01642-x

    View details for PubMedID 40374984

    View details for PubMedCentralID 11800014

  • Large language models display human-like social desirability biases in Big Five personality surveys. PNAS nexus Salecha, A., Ireland, M. E., Subrahmanya, S., Sedoc, J., Ungar, L. H., Eichstaedt, J. C. 2024; 3 (12): pgae533

    Abstract

    Large language models (LLMs) are becoming more widely used to simulate human participants and so understanding their biases is important. We developed an experimental framework using Big Five personality surveys and uncovered a previously undetected social desirability bias in a wide range of LLMs. By systematically varying the number of questions LLMs were exposed to, we demonstrate their ability to infer when they are being evaluated. When personality evaluation is inferred, LLMs skew their scores towards the desirable ends of trait dimensions (i.e. increased extraversion, decreased neuroticism, etc.). This bias exists in all tested models, including GPT-4/3.5, Claude 3, Llama 3, and PaLM-2. Bias levels appear to increase in more recent models, with GPT-4's survey responses changing by 1.20 (human) SD and Llama 3's by 0.98 SD, which are very large effects. This bias remains after question order randomization and paraphrasing. Reverse coding the questions decreases bias levels but does not eliminate them, suggesting that this effect cannot be attributed to acquiescence bias. Our findings reveal an emergent social desirability bias and suggest constraints on profiling LLMs with psychometric tests and on this use of LLMs as proxies for human participants.

    View details for DOI 10.1093/pnasnexus/pgae533

    View details for PubMedID 39691446

  • Influence of emotions on coping behaviors in crisis: a computational analysis of the COVID-19 outbreak JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE Xu, H., Sudheendra, S., Huh, J., Salecha, A., Srivastava, J. 2024

    View details for DOI 10.1007/s42001-024-00282-7

    View details for Web of Science ID 001215061500001

  • The Language of Conflict Transformation: Assessing Psychological Change Patterns in Israeli-Palestinian Track Two Interactive Problem Solving NEGOTIATION AND CONFLICT MANAGEMENT RESEARCH Fink, O., Graf, W., Subrahmanya, S., Salecha, A., Eichstaedt, J. C. 2024; 17 (2): 130-152

    View details for DOI 10.34891/svxv-s665

    View details for Web of Science ID 001247374700002