Ivan Alvarez Email & Phone Number

London, Gb

London, England, United Kingdom

London, England, United Kingdom

London, England, United Kingdom

• Led a team of 4 data scientists (2 mid, 2 senior) as direct reports, building machine learning (ML) automation tools for BBC News content authoring, used daily by approximately 3,000 journalists and editors.• Oversaw the development of automated metadata suggestion tools, integrated into existing content production pipelines, and empowering editorial decisions on where and how individual News stories are published.• Managed cross-functional stakeholder relationships, including engineering, product, delivery, editorial and analytics functions, aligning data science work with product priorities.• Championed data science across the business, emphasising the value added by data science applications to automating content generation, curation, and experience personalisation for end users.

London, England, United Kingdom

• Built recommendation engines to enable personalisation at scale for millions of users across news, sport and video products.• Designed and productionised a hybrid algorithmic recommender for the BBC iPlayer video-on-demand product, showing 30% uplift over manually-curated recommendations in A/B test.• New recommender provided improved personalisation for 11m weekly users and reduced editorial burden by 20h per week.• In a survey of 500 representative users, 94% rated the quality of recommendations as "good" or "very good".• Developed cross-product engine to providing real-time personalised recommendations between BBC News and BBC iPlayer products.

• Led a team of 8 researchers (1 graduate student, 2 staff scientists, 5 research assistants) developing supervised machine learning models for decoding physiological timeseries data with high prediction accuracy (>80%).• Developed advanced computer vision models including spatio-temporal Gabor wavelet pyramids and convolutional neural networks in TensorFlow and sciki-learn for feature engineering across thousands of naturalistic images.• Designed a regularised linear regression and permutation testing framework to fit encoding models that predict future brain responses in an extremely large data regime, with thousands of features and millions of elements.• Optimised timeseries analysis software for deployment in a high performance computing (HPC) environment, leveraging GPU capacity for x20 speed-up over conventional implementations.• Applied dimensionality reduction techniques (PCA, ICA, clustering) and multivariate analysis to explain how visual information is encoded in the brain.

Oxford, United Kingdom

• Spearheaded a 5‐year project using biologically‐plausible neural networks and brain imaging to understand the neural mechanisms of 3D vision & inform the design of VR kits.• Studied neural mechanisms of binocular stereopsis in humans, using computational modelling, ultra-high field MRI, and behavioural psychophysics.• Created a neural network model of 3D depth detection, which can accurately predict brain responses.• Leveraged Gaussian mixture models, hierarchical clustering, and multi-class classification to predict the depth perception abilities of individuals based on their brain scan data, leading to successful outcomes for a $7.5m research grant.

London, England, United Kingdom

* Designed and validated a novel forward model for timeseries analysis of physiological data, now used by 7 teams worldwide and leading to 16 peer‑reviewed publications.* Developed algorithms for method fusion in MRI brain imaging.* Experience with clinical and pre-clinical data collection, analysis and interpretation.* Triaged requests for ad hoc data analysis solutions from clinical teams at an NHS site.

Doctor Of Philosophy - Phd, Computational Neuroscience

Master Of Science - Ms, Cognitive Neuroscience, Distinction

Bachelor Of Science - Bs, Psychology, 1St Class