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 Dr. Víctor Elvira. The kind of research I (mostly) enjoy doing is the so called “fundamental” or “basic” research. To be clear, it does not mean it is “better” or “more important” (nor less, for that matter) than other kinds of research.

I am interested broadly in statistical methodology surrounding efficient uncertainty quantification, decision making, probabilistic reasoning, computational statistics, and machine learning.
More specifically, I am interested in methods for (possibly adaptive) Importance Sampling, experimental design, and causal inference.

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:

• Importance sampling and quasi Monte Carlo methodology for joint estimation of multiple related quantities.
• Measure transport and optimal transport methodology
• Applications in rare event estimation.
• Applications in causal inference, in particular methods to combine interventional and observational data to make sample-efficient inferences in causal models.

News

• 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.
• Poster on “Causal Entropy Optimization” accepted at Greek Stochastics.
• I received the Turing Enrichment Scheme (Placement Award, for 6 months) offer.

Reviewing

Journals

Statistics and Probability Letters

Conferences

AISTATS 2023, AABI (workshop) 2023, NeurIPS 2023

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 Dr. 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