Bhargav Desikan
AI And Tech Lead at University of Cambridge
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolBhargav Desikan is an AI and tech lead and doctoral researcher whose work sits at the intersection of AI, society, and economic policy, with 11 years of experience spanning academic labs, think tanks, and open-source projects. He builds production-ready ML tooling and contributes to libraries like metric-learn, gensim and PyMC, while also authoring a practical NLP book and mentoring community projects as a two-time Google Summer of Code participant. As AI & Tech Lead at the Autonomy Institute and a research affiliate at Stanford, Cambridge and Chicago, he translates deep technical skills in deep learning, causal inference and Bayesian modeling into policy analyses and tools for democratic accountability. His research has appeared in venues from Nature to NeurIPS, and he has led high-impact policy work at IPPR on managing AI’s economic effects. Unusually, Bhargav combines hands-on metric-learning and probabilistic programming contributions with a social-science-driven PhD trajectory studying human labour, value, and AI supply chains. Based in London, he’s equally comfortable shipping code, advising legal and civil-society stakeholders, and theorizing the societal consequences of algorithms.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Information, Communication and Social Sciences, Doctor of Philosophy - PhD, Information, Communication and Social Sciences at University of OxfordMasters of Arts in Computational Social Science, Social Sciences, 3.82 / 4.0, Masters of Arts in Computational Social Science, Social Sciences, 3.82 / 4.0 at University of ChicagoBachelor’s Degree, Computer Science, 8.27 / 10, Bachelor’s Degree, Computer Science, 8.27 / 10 at Birla Institute of Technology and Science, Pilani - Goa Campus
English, Tamil, Hindi, French
Github Skills (29)
probabilistic-programming10
python10
scikit10
metric-learning10
statistics10
machine-learning10
statistic10
numpy10
mcmc10
bayesian10
topic-models10
natural-language-processing10
scikit-learn10
jupyter-notebook10
topic-modeling10
Programming languages (6)
Github contributions (5)
piskvorky/gensim
Apr 2016 - Mar 2017
Topic Modelling for Humans
Role in this project:
Data Scientist
Data ScientistContributions:30 commits, 49 PRs, 276 comments in 11 months
Contributions summary:Bhargav primarily contributed to the documentation and tutorial aspects of the `gensim` repository, specifically focusing on the Dynamic Topic Models (DTM) implementation. Their work included adding and updating content within the DTM tutorial notebook, and demonstrating the usage of new methods such as `get_term_topics` and `get_document_topics`. Furthermore, they included illustrative examples and discussions about distance metrics used in the context of topic modelling, and corrected errors in earlier commits.
pythonword-similarityword-embeddingsdata-miningfor-humans
scikit-learn-contrib/metric-learn
Sep 2016 - Nov 2016
Metric learning algorithms in Python
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 5 PRs, 44 comments in 1 month
Contributions summary:Bhargav's contributions primarily focused on enhancing and refining metric learning algorithms within the `metric-learn` library. They fixed a typo, introduced random state functionality across several supervised learning models (LSML, ITML, SDML, RCA), and added a `fit_transform` method to base class. The user's work also included adding new examples and a tutorial notebook demonstrating the usage of the available algorithms, showcasing their understanding of metric learning concepts.
pythonmetric-learningmachine-learninglearning-algorithmsscikit-learn
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