Posts by Collection

Regularised B-splines projected Gaussian Process priors to estimate time-trends of age-specific COVID-19 deaths related to vaccine roll-out

Appeared in Bayesian Analysis, 2021

In this paper we fit beta splines to COVID-19 mortality data for each US state and use the fitted curves to estimate future deaths.

Recommended citation: Monod, M., Blenkinsop, A., Brizzi, A., Chen, Y., Perello, C. C. C., Jogarah, V., Wang, Y., Flaxman, S., Bhatt, S., & Ratmann, O. (2021). Regularised B-splines projected Gaussian Process priors to estimate time-trends of age-specific COVID-19 deaths related to vaccine roll-out. https://arxiv.org/abs/2106.12360

Adaptively Optimised Adaptive Importance Samplers

Preprint (submitted for publication), 2023

This paper, which stemmed out of my BSc project, introduces a new adaptive importance sampling algorithm that used adaptive optimisation to adapt the proposal distribution.

Recommended citation: C. C. Perello, C. A., Akyildiz, Ö. D. (2023). Adaptively Optimised Adaptive Importance Samplers. https://arxiv.org/abs/2307.09341

Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems

Appeared in Stat, 2023

This paper is an extension of my 2nd year group project, which consisted on fitting Hawkes processes to the London Santander Cycle bike-sharing system. In this paper we employ a spatial component in the model to take in account distances between docking stations when predicting bike usage.

Recommended citation: F. Sanna Passino, Y. Che, C. C. Perello, C. A. (2023). Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems. https://arxiv.org/abs/2311.00595

Non-existence of the 2D quadratic Poisson optimal matching

MSc Thesis, 2024

This is my MSc thesis, where I analysed a recent proof of the fact that a stationary, non-ergodic, quadratic and locally-optimal 2D Poisson matching does not exist.

Recommended citation: C. C. Perello, C. A. (2024), Non-existence of the 2D quadratic Poisson optimal matching.

Guest lectures for APMA1650: Statistical Inference I

Published: February 01, 2025

I gave four guest lectures for APMA1650: Statistical Inference I at Brown University, on Feb 10th, 12th ,14th and on Apr 14th. In the February lectures, I covered discrete random variables, probability mass functions as well as expectation and variance for this class of random variables. In my upcoming lecture, I will cover more advanced topics such as hypothesis testing and confidence intervals.

On the non-existence of 2D, locally optimal ergodic and stationary matchings

Published: April 01, 2025

I gave a short talk at Brown’s Graduate Lecture Series in Analysis and PDEs (GLESPA) on the work I conducted when writing my MSc thesis, which is available here.

Peer Tutoring

Peer Tutoring, Imperial College London, Department of Mathematics, 2022

I was a peer tutor for the first year mathematics course at Imperial College London. I was responsible for leading weekly tutorials for a group of 5-6 students, helping them develop their problem solving skills and understanding of the course material. I also prepared challenging problems for the students to work on in their own time.

UTA for MATH50003 Numerical Analysis

Undergraduate Teaching Assistant, Imperial College London, Department of Mathematics, 2023

I was an Undergraduate Teaching Assistant (UTA) for MATH50003 Numerical Analysis at Imperial College London, a 2nd year undergraduate course led by Prof Sheehan Olver. I attended the weekly problems classes and helped students with problems, especially with the Julia programming exercises. I also marked the mock exam for said course. More information can be found on the course’s GitHub page.

Guest lectures

Lecturing, Brown University, 2025

I gave four guest lectures for APMA1650: Statistical Inference I at Brown University.

TA for STAT 5264: Stochastic Processes with Applications (Section 002)

TAing, Columbia University, Department of Statistics, 2025

I’m currently a TA for STAT 5264: Stochastic Processes with Applications (Section 002), taught by Prof Graeme Baker. This is a graduate-level course that covers the theory and applications of stochastic processes, including Markov chains, Poisson processes, Brownian motion, and stochastic calculus. We are using the book Stochastic Calculus for Finance II: Continuous-Time Models by Steven Shreve as the main textbook, which can be accessed here. I am holding office hours on Tuesdays from 3-4PM and Wednesdays from 9-10AM at Uris Hall 321/322.