This very infrequent blog is by me, Nicola Branchini.
I am a graduate researcher in Statistics in the School of Mathematics at the University of Edinburgh, advised by Prof. Víctor Elvira.

I am interested broadly in statistical methodology in Bayesian computation, efficient uncertainty quantification, decision making, probabilistic reasoning, broadly speaking computational statistics and machine learning.

I like collaborating with people. If you do research in very related topics, feel free to drop me an email. Some specific topics I am working on now directly and/or want to use in my work in the future are:

• (Adaptive/annealed) importance sampling and quasi Monte Carlo methodology for joint estimation of multiple related quantities, and related diagnostics.
• Applications in rare event estimation.
• Measure transport and optimal transport methodology

News

• Our Generalized self-normalized importance sampling featured in Xian’s blog !!! ✰ ✰
• Excited (and a bit stressed) to give a talk at the 6th workshop on Sequential Monte Carlo (SMC 2024). ✰
• Adaptive importance sampling for heavy-tailed distributions via alpha-divergence minimization to appear in AISTATS 2024. ✰
• Variational resampling to appear in AISTATS 2024. ✰
• Causal Optimal Transport of Abstractions to appear in CLEaR (Conference on Causal Learning and Reasoning) 2024. ✰
• Contributed talk accepted in a minisymposium at the 14th international conference on Monte Carlo methods and applications (MCM) 2023, on “Generalized Self Normalized Importance Sampling”. ✰
• Causal Entropy Optimization to appear in AISTATS 2023. ✰
• Received ISBA Travel Award for BayesComp 2023.
• Accepted for attending the 2023 Probabilistic Numerics School in Tübingen
• Poster on “Generalized Self Normalized Importance Sampling” accepted at BayesComp 2023.
• I received the Turing Enrichment Scheme (Placement Award, for 6 months) offer.

Reviewing

Journals

Statistics and Computing, Statistics and Probability Letters

Conferences

AISTATS 2023, AABI (workshop) 2023, NeurIPS 2023, ICLR 2024, AISTATS 2024

Talks & Posters

• Contributed talk on “Generalized Self Normalized Importance Sampling” at the 14th international conference on Monte Carlo methods and applications (MCM) 2023
• Poster on “Generalized Self Normalized Importance Sampling” at BayesComp 2023.
• Poster on “Causal Entropy Optimisation” at Greek Stochastics.
• Poster: Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization, at 5th Workshop on Sequential Monte Carlo methods, May 2022.
• Poster: Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization, at “Bayes at CIRM” Winter School, Centre International de Rencontres Mathématiques, Marseille, October 2021
• Poster: Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization at 37th Conference on Uncertainty in Artificial Intelligence (UAI), online, 2021.

Awards

• Feuer International Scholarship in Artificial Intelligence (full funding) (declined)
• School of Mathematics Studentship, University of Edinburgh (full funding)
• Dissertation Prize for the Artificial Intelligence MSc, University of Edinburgh
• Oustanding dissertation award (BSc Computer Science), University of Warwick

Some background

Previously, I was a Research Assistant at the Alan Turing Institute, working within the Warwick Machine Learning Group and supervised by Prof. Theo Damoulas. Previous to that, I was a Master’s student in the School of Informatics at the University of Edinburgh where I was supervised by Prof. Víctor Elvira working on auxiliary particle filters. As undergrad, I studied Computer Science at the University of Warwick, where I did my BSc dissertation on reproducing AlphaZero supervised by Dr. Paolo Turrini.

Random selection of nice reads

Worth having the physical version.

• Noise: A Flaw in Human Judgment, Daniel Kahneman, Olivier Sibony, Cass R. Sunstein
• The book of why, Judea Pearl & Dana McKenzie
• The Meaning of It All: Thoughts of a Citizen Scientist, Richard Feynman.
• Sustainable energy - without the hot air, David McKay
• The Art of Statistics: Learning from Data, David Spiegelhalter
• Knock on Wood: Luck, Chance, and the Meaning of Everything, Jeffrey S. Rosenthal

Blogroll

• Christian Robert’s blog
• Pierre Jacob’s blog
• Andrew Gelman’s blog
• Ferenc Huszár’s blog
• Michael Betancourt’s blog
• Sam Power’s blog
• Deniz Akyıldız’s blog
• Francis Bach’s blog
• Dennis Prangle’s blog
• Gregory Gundersen’s blog
• Yuling Yao’s blog
• Frank Nielsen’s blog
• CMU blog on ML
• Fabian Pedregosa’s blog
• Cambridge MLG blog on ML
• Terence Tao’s blog