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2003-2007, B.S. in Symbolic Systems (Honors and With Distinction), Stanford University
2007-2012, Ph.D. in Cognitive Science (Minor: Computational Linguistics), Indiana University at Bloomington


2022, Open Philanthropy, for a project training and otherwise conditioning AI models to answer difficult factual questions with the aim of sharing research findings and advancing safe and beneficial AI, $445,605
2019, Cancer Research UK, “Risk communication with patients undertaking testing for BRCA genes,” (named postdoc; PI: Alexandra Freeman), £46,467
2012, University of Memphis, Computationally Estimating Geographical Information from User-Contributed Data (named fellow; PI: Max Louwerse), $221,728
2009 National Science Foundation Graduate Research Fellowship, Honorable Mention
2008 National Science Foundation Graduate Research Fellowship, Honorable Mention
2008 Society for Computers in Psychology’s Castellan Award for Best Student Paper
2007 Firestone Medal for Excellence in Undergraduate Research
2007 Accepted to Phi Beta Kappa
2005 Recipient of research grant from Undergraduate Research Programs (‘The Role of High-Level Knowledge in Event Perception’), Stanford University ($1,200)
2003 Siemens Westinghouse Competition in Math, Science and Technology Regional Finalist (awarded 2003, funds received 2010; $1,000)


Selected Preprints

Recchia, G. (2021). Teaching Autoregressive Language Models Complex Tasks By Demonstration. arXiv.

Selected Publications

This is probably out of date – please see my Google Scholar profile for an up-to-date list

Recchia, G, Lawrence A. C. E., Capacchione, L., & Freeman, A.L.J. (2022). Making BRCA1 genetic test reports easier to understand through user-centered design: A randomized trial. Genetics in Medicine. epub ahead of print,

Recchia, G, Lawrence A. C. E., & Freeman, A.L.J. (2021). Investigating the presentation of uncertainty in an icon array: A randomized trial. PEC Innovation, 1, 1-11,

Sutherland, H., Recchia, G., Dryhurst, S., Freeman, A.L.J. (2021). How people understand risk matrices, and how matrix design can improve their use: Findings from randomized controlled studies. Risk Analysis. epub ahead of print,

Kerr, J. R., Schneider, C. R., Recchia, G., Dryhurst, S., Sahlin, U., Dufouil, C., … & Van Der Linden, S. (2021). Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys. BMJ Open, 11(8), e048025.

Recchia, G., Freeman, A. L., & Spiegelhalter, D. (2021). How well did experts and laypeople forecast the size of the COVID-19 pandemic? PloS ONE, 16(5), e0250935.

Recchia, G., Schneider, C. R., & Freeman, A. L. (2021). How do the UK public interpret COVID-19 test results? Comparing the impact of official information about results and reliability used in the UK, USA and New Zealand: a randomised controlled trial. BMJ Open, 11(5), e047731.

Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., Spiegelhalter, D., & van der Linden, S. (2021). COVID-19 risk perception: a longitudinal analysis of its predictors and associations with health protective behaviours in the United Kingdom. Journal of Risk Research, 24(3-4), 294-313.

Freeman, A. L., Kerr, J., Recchia, G., Schneider, C. R., Lawrence, A. C., Finikarides, L., … & Spiegelhalter, D. (2021). Communicating personalized risks from COVID-19: guidelines from an empirical study. Royal Society Open Science, 8(4), 201721.

Thurtle, D., Jenkins, V., Freeman, A., Pearson, M., Recchia, G., Tamer, P.,… & Gnanapragasam, V. Clinical impact of the Predict Prostate risk communication tool in men newly diagnosed with nonmetastatic prostate cancer: a multicentre randomised controlled trial. (2021). European Urology. epub ahead of print,

Recchia, G. & Freeman, A.L.J. (2020). Communicating risks and benefits to cardiology patients. Heart, 106(23).

Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., van der Bles, A.M., & Van Der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10), 201199.

Dryhurst, S., Schneider, C.R., Kerr, J. Freeman, A.L.J., Recchia, G., van Der Bles, A.M., Spiegelhalter, D., and van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 1-13.

Recchia, G. L., Bles, A. M. V., & Freeman, A. L. (2020). PREDICT: the potential pitfalls of visualisations of risk. Abstract published in Breast Cancer Research And Treatment (Vol. 180, No. 2, pp. 577-578).

Recchia, G., Chiappi, A., Chandratillake, G., Raymond, L., Freeman, A. L. J. (2019). Creating genetic reports that are understood by nonspecialists: a case study. Genetics in Medicine. doi:10.1038/s41436-019-0649-0

Recchia, G. (2020). The fall and rise of AI: Investigating AI narratives with computational methods. In S. Dillon, S. Cave, & K. Dihal (Eds)., AI Narratives: A History of Imaginative Thinking About Intelligent Machines. Oxford University Press.

Recchia, G., Chiappi, A., Chandratillake, G., Raymond, L., Freeman, A. L. J. (2019). Creating genetic reports that are understood by nonspecialists: a case study. Genetics in Medicine. doi:10.1038/s41436-019-0649-0

Recchia, G. & Nulty, P. (2017). Improving a fundamental measure of lexical association. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 2963-2968). Austin, TX: Cognitive Science Society.

Recchia, G., Jones, E., Nulty, P., Regan, J., & de Bolla, P. (2016). Tracing shifting conceptual vocabularies through time. In Ciancarini, P. et al. (Eds.): Knowledge Engineering and Knowledge Management: EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19–23, 2016, Revised Selected Papers (pp. 19-28). Cham, Switzerland: Springer International AG.

Gruenenfelder, T. M., Recchia, G., Rubin, T., & Jones, M. N. (2016). Graph-theoretic properties of networks based on word association norms: Implications for models of lexical semantic memory. Cognitive Science, 40(6), 1460-95. doi: 10.1111/cogs.12299

Hladish, T.J., Pearson, C.A.B, Chao, D.L., Rojas, D.P., Recchia, G.L., Gómez-Dantés, H., Halloran, M.E., Pulliam, J.R.C., & Longini, I.M. (2016). Projected impact of dengue vaccination in Yucatán, Mexico. PLoS Neglected Tropical Diseases, 10(5). doi: 10.1371/journal.pntd.0004661

Recchia, G. & Louwerse, M. (2016). Archaeology through computational linguistics: Inscription statistics predict excavation sites of Indus Valley artifacts. Cognitive Science, 40(8), 2065-2080. doi: 10.1111/cogs.12311

Recchia, G., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Computational Intelligence and Neuroscience. doi: 10.1155/2015/986574

Recchia, G., & Louwerse, M. M. (2015). Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. The Quarterly Journal of Experimental Psychology, 68(8), 1584-1598. doi: 10.1080/17470218.2014.941296

Louwerse, M. M., Hutchinson, S., Tillman, R., & Recchia, G. (2015). Effect size matters: the role of language statistics and perceptual simulation in conceptual processing. Language, Cognition and Neuroscience, 30(4), 430-447. doi: 10.1080/23273798.2014.981552

Recchia, G., Slater, A. L., & Louwerse, M. (2014). Predicting the good guy and the bad guy: Attitudes are encoded in language statistics. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1264-1269). Austin, TX: Cognitive Science Society.

Recchia, G., & Louwerse, M. (2014). Grounding the ungrounded: Estimating locations of unknown place names from linguistic associations and grounded representations. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1270-1275). Austin, TX: Cognitive Science Society.

Recchia, G. L., & Louwerse, M. M. (2013). A comparison of string similarity measures for toponym matching. In Scheider, S., Adams, B., & Janowicz, K. (Eds.), Proceedings of 2013 ACM SIGSPATIAL International Workshop on Computational Models of Place (pp. 54-61). Orlando, FL: ACM.

Recchia, G. L., & Jones, M. N. (2012). The semantic richness of abstract concepts. Frontiers in Human Neuroscience, 6(315). doi: 10.3389/fnhum.2012.00315

Jones, M. N., Johns, B. T., Recchia, G. L. (2012). The role of semantic diversity in lexical organization.  Canadian Journal of Experimental Psychology, 66(2), 115-124. doi: 10.1037/a0026727

Hard, B., Recchia, G., & Tversky, B. (2011). The shape of action. Journal of Experimental Psychology: General, 140(4), 586-604. doi: 10.1037/a0024310

Cox, G., Kachergis, G., Recchia, G., & Jones, M. N. (2011). Towards a scalable holographic word-form representation. Behavior Research Methods, 43(3), 602-615.

Kachergis, G., Recchia, G., & Shiffrin, R. M. (2011). Adaptive magnitude and valence biases in a dynamic memory task. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 819-824. Austin, TX: Cognitive Science Society.

Jones, M. N., & Recchia, G. L. (2010). You can’t wear a coat rack: A binding framework to avoid illusory feature migrations in perceptually grounded semantic models. In S. Ohlsson and R. Catrambone (Eds.), Proceedings of the 32nd Annual Cognitive Science Society, 877-882. Austin, TX: Cognitive Science Society.

Recchia, G., & Jones, M. N. (2009). More data trumps smarter algorithms: Comparing pointwise mutual information with latent semantic analysis. Behavior Research Methods, 41(3), 647-656. Version presented at the Society for Computers in Psychology won Castellan Award for Best Student Paper, 2008.

Recchia, G., Johns, B. T., & Jones, M. N. (2008). Context repetition benefits are dependent on context redundancy. In V. Sloutsky, K. McRae, & B. Love (Eds.), Proceedings of the 30th Cognitive Science Society, 267-272. Austin, TX: Cognitive Science Society.

Hard, B. and Recchia, G. (2006). Reading the language of action. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Cognitive Science Society, 1434-1439.


Recchia, G. L. (2012). Investigating the semantics of abstract concepts: Evidence from a property generation game. Doctoral dissertation, Indiana University. Raw data, tagged data, and codebooks available here.

Selected Presentations

(See the above “Publications” section for presentations with published conference or workshop proceedings.)

Communicating risks and evidence in a public health emergency (with co-presenters Alex Freeman, John Kerr, Christin Ellermann). (2021). World Health Organization EPI-WIN Webinar.

Risk communication and results reports. (2019). Informing Policy on Secondary Findings from Genome Sequencing in Clinical Practice: Learning from the 100,000 Genomes Project. Workshop funded by UCL Public Policy. The Francis Crick Institute.

Communicating risk: Lessons from the literature and the PREDICT prognostic model. (2018). Public Health England National Cancer Registration and Analysis Service.

Fall and rise of AI: Computational methods for investigating cultural narratives. (2017). AI Narratives: Workshop 1, Leverhulme Centre for the Future of Intelligence and the Royal Society, 16 May 2017, Hughes Hall, Cambridge.

Recchia, G. (2016). The utility of count-based models for the digital humanities. Digital Humanities Congress. Sheffield, 2016.

Tracing concepts through time. (2016). Natural Language and Information Processing Seminar Series, University of Cambridge.

Recchia, G. (2015). Considerations for evaluating models of language understanding and reasoning. Neural Information Processing Systems RAM (Reasoning, Attention, Memory) Workshop, Montreal, Canada.

Recchia, G. (2015). Making sense of language: It’s okay to count. Invited talk at Microsoft Research Cambridge, UK.

The unreasonable effectiveness of co-occurrence-based models. (2015). Big Data Methods for Social Sciences and Policy, University of Cambridge, UK.


Supervised two masters’ students (Lee Freegard, Lauren Capacchione) and provided partial supervision/guidance to a third (Antonia Chiappi) completing projects to create genetic reports that communicate risks and evidence more effectively, as part of a partnership between the Winton Centre and a faculty member of the Institute for Continuing Studies Genomic Medicine Programme (Dr Gemma Chandratillake).

Co-designed & taught Machine Reading the Archive, a digital methods development programme organised by the Cambridge Digital Humanities Network, Cambridge Big Data and the Cambridge Digital History Programme, University of Cambridge. For 2017/18 this involved small group teaching and individual mentoring of projects in the Machine Reading the Archive programme (14 contact hours), teaching of introductory sessions in the Digital Methods Training Programme (8 contact hours), development of an advanced workshop for the Methods programme (4 contact hours), with 32 hours allotted to preparation, admin and course development. Workshops organized included Digital Research Project Design for Beginners, Curating Your Own Digital Archive, Introduction to Webscraping, Introduction to Optical Character Recognition. Additionally served as a mentor for two students in the 2020 Machine Reading the Archive programme.

Designed & taught Literary Critical Coding, a graduate training series in Python programming and digital methods for the humanities. Faculty of English, University of Cambridge. 8-week pilot in Easter Term 2016; expanded to 16-week program for Lent & Easter terms of 2017 & 2018.

During my graduate program at Indiana University, served as associate instructor for Experiments and Models in Cognition and Statistical Techniques. Mentored one undergraduate student (Ryan Fitzpatrick) through the semester-long Undergraduate Research Opportunities in Computing programme.


Held nonstipendiary honorary contract with Public Health England; coauthored NHSE/I service audit report “Understanding demand for COVID-19 rehabilitation in the East of England,” (authors: Gabriel Recchia*, Tara Berger Gillam*, Ebubechukwu Uzoechina, Rachel Wakefield, Helen Watson, Sushant Jeurkar, Anees Pari). *joint first authors

~6 meetings with Policy Fellows through University of Cambridge’s Centre for Science and Policy

Current member of Cognitive Science Society, American Society of Human Genetics (2021); past member of Congenica Artificial Intelligence Advisory Group, Cognitive Science Society, Society for Computers in Psychology.


Have served as an external reviewer for the (U.S.) National Science Foundation, as well as a reviewer for Psychonomic Bulletin & Review; Cognition; Topics in Cognitive Science; Cognitive Science; Behavior Research Methods; Cognitive Processing; Frontiers in Psychology – Cognition; Canadian Journal of Experimental Psychology (co-reviewer); Behaviour & Information Technology; Data & Knowledge Engineering, Brain Sciences.


Participated in PHG Foundation expert roundtables on Black Box Medicine and Transparency in June & July 2019, Cambridge, UK.


Volunteered for Africa’s Voices Foundation (AVF) on a project using interactive radio and SMS to gather opinions of Ugandan audiences related to causes of complications during pregnancy; designed a strategy for analysing SMS data in mixed languages (English, Luganda and Swahili) and carried out a supervised thematic analysis of the data. The aim was to explore innovative ways for SMS analysis in health-related projects in Africa.


Book proposals and book chapters reviewed:

Quantitative Semantics, ed. Sverker Sikström (proposal). Springer.

Big Data in Cognitive Science: From Methods to Insights, ed. Michael Jones. Taylor & Francis.


University of Cambridge, 2014-present

Winton Centre for Risk and Evidence Communication, 2018-present
Research Associate

  • Academic research on risk communication, esp. effective communication of quantified information
    (e.g., societal risks, health risks, risks and benefits of medical treatments)
  • Lead on user testing research and evaluation of tools for communicating risks and benefits
    (e.g. NHS Predict: Breast Cancer, NHS Predict: Prostate, genetic reports)

Cambridge Centre for Digital Knowledge, Concept Lab, 2014-2018
Research Associate

  • Developing computational methods for investigating similarities among concepts in historical language corpora, tracking how they change over time, and characterizing their properties
  • Developing computational tools to allow humanities researchers to understand and visualize statistical properties of word occurrences in large historical textual datasets

University of Memphis, 2013-2014

Institute for Intelligent Systems, Multimodal Aspects of Discourse (MAD) Lab
IC postdoctoral fellow

  • Developing co-occurrence based algorithms to estimate geographical information (e.g., longitude, latitude, and population size of a city) from text (e.g., newspapers that do not explicitly describe such geographical information)
  • Investigated the role that semantic representations play in a variety of cognitive tasks (place estimation, conceptual processing, valence estimation)

Indiana University, 2007-2012

Cognitive Computing Lab
(advisor: Professor Michael N. Jones)
Doctoral candidate, associate instructor, graduate research assistant

  • Developed and investigated computational models of semantic representation based on large-scale language statistics, with special attention to investigating how simple, neurally plausible mechanisms extract meaning from noisy, unsupervised data
  • Served as associate instructor for Experiments and Models in Cognition (Q270) and Statistical Techniques (K300)

Stanford University, 2003-2007

Spoken Syntax Lab (advisors: Professor Joan Bresnan, Professor Tom Wasow)

  • Developed search and analysis tools for repositories of temporal, phonological, syntactic and semantic annotations of spontaneous speech
  • Added temporal alignments and other information to a database of 2,350 English datives from the Switchboard corpus; investigated syntactic priming of the dative alternation with the R statistical package

Space, Time, and Action Research Lab (director: Professor Barbara Tversky; supervisor: Dr. Bridgette Hard)

  • Awarded URP grant of $1,200 for research on hierarchical encoding of events in action perception
  • Developed computational metric for quantifying low-level cues in perceived action; assisted in data analysis and conducting experiments

Social Cognitive Development Lab (supervisor: Bridgette Hard)

  • Created experimental stimuli for eye-tracking study and assisted in conducting experiments

SemLab (director: Stanley Peters; supervisor: Elizabeth Bratt)

  • Contributed to development of speech interface of DC-Train, a Navy damage control simulator
  • Developed coding scheme for speech acts and coded videos of tutor-student interactions in Transana


Substantial experience with Python, C#, JavaScript/Typescript, Node.js, HTML/CSS, and R.

Substantial experience with algorithms, packages and resources for computational language analysis including NLTK, word2vec, scikit-learn, WordNet, Latent Semantic Analysis, random indexing, etc.

Coursera certifications: Deep Learning Specialization (5 courses, ~17 wks),
Data Science Specialization (9 courses, ~9 months)