Dr Angelica Pachon

• CSC005 Computational Probability

  This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions. The module material will consist of 'bite' sized presentations, and self-paced programming python notebooks.

• MA-009 Computational Probability

  This module gives students an understanding of Monte Carlo methods, which use random simulation to computationally solve physical problems which may be difficult if not impossible to solve analytically. The delivery style of the module is very much tutorial in nature: only basic programming and probability theory will be taught and required, with the module concentrating on practical weekly lab-based problem-solving sessions. The module material will consist of 'bite' sized presentations, and self-paced programming python notebooks.

• MA-M07 Further Mathematics for Data Science

  The module develops mathematical foundations for data science and machine learning. The module covers basic problems of Vector Calculus, Linear Algebra and Optimization including concepts of partial derivatives, gradient and Hessian, eigenvalues and eigenvectors of matrices and their use in optimisation problems, method of gradient descent and its application in Machine Learning. It also highlights the use of Python in the numerical solution of these problems, as well as in symbolic computation.

• Heilbronn Institute (HIMR) Small Grant scheme 2023 - 2024

  , with Dr Robin Stephenson, Prifysgol Sheffield.

• Complex Networks and Data Science 2019 - 2022

  CES RIS, Prifysgol De Cymru

• Problemi attuali della matematica 3 2016 - 2017

  Research Program, Prifysgol Turin

1. U. De Ambroggio and A. Pachon. Upper bounds for the largest components in critical inhomogeneous random graphs. Accepted for publication. ALEA, Lat.Am. J. Probab. Math. Stat. https://arxiv.org/abs/2012.0900
2. A. Pachon, F. Polito and C. Ricciuti. On discrete-time semi-Markov processes. Discrete & Continuous Dynamical Systems - B (DCDS-B) Vol. 26, no. 3, 2021 . doi: 10.3934/dcdsb.2020170 (*)
3. A. Pachon, F. Polito and L. Sacerdote, On the continuous-time limit of the Barab´asi-Albert random graph. Applied Mathematics and Computation, Vol. 378, art. 125177, 2020. (*)
4. A. Pachon, L. Sacerdote and S. Yang, Scale-free behaviour of networks with the copresence of preferential and uniform attachment rules. Physica D: Nonlinear Phenomena, Volume 371, 2018. (*)
5. M. Kang, A. Pachon and P. Rodriguez, Evolution of a modified binomial random graph. Journal of Statistical Physics, 170(3), (2018), 509-535. (*)
6. E. Agliari, A. Pachon, P. Rodriguez and F. Tavani, Phase transition for the Maki-Thompson rumour model on a small-world network. Journal of Statistical Physics, 169(4), (2017), 846-875. (*)
7. H. Matzinger, A. Pachon and S. Popov, Reconstruction of multidimensional scenery with a branching random walk. Ann. Appl. Probab, 27(2), (2017), 651-685. (*)
8. A. Pachon, F. Polito and L. Sacerdote, Random Graphs Associated to Some Discrete and Continuous Time Preferential Attachment Models. Journal of Statistical Physics, 162(6), (2016), 1608-1638. (*)
9. M. Kang, C. Koch and A. Pachon, The phase transition in the multi-type binomial random graph G(n,P). SIAM J. Discrete Math. 29, (2015), 1042-1064. (*)
10. H. Matzinger and A. Pachon, Recognition of Times a Walker is close to the Origin. ALEA, Lat.Am. J. Probab. Math. Stat, 9, (2012), 85-99. (*)
11. A. Oghlan and A. Pachon, The Decimation Process in Random k-SAT. SIAM, J. Discrete Math, 26(4), (2012), 1471-1509. (*)
12. S. Popov and A. Pachon, Scenery reconstruction with branching random walk. STOCHASTICS, Stoch. Inter. J. Prob. Stoch. Proc, 83(2), (2011), 107-116. (*)
13. H. Matzinger and A. Pachon, DNA-Approach to Scenery Reconstruction. Stoch. Proc. Appl, 121(11), (2011), 2455-2473. (*)
14. M. Kang, A. Pachon and P. Rodriguez, Connectivity for a modified binomial random graph by agglomeration. DMTCS proceedings, the 25th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms AofA, (2014).
15. A. Oghlan and A. Pachon, The Decimation Process in Random k-SAT. ICALP proceedings, the 38th International Colloquium on Automata, Languages and Programming, (2011). 8
16. C. A. Arango and A. Pachon, Minimum Wages in Colombia: Holding the Middle with a Bite. Ideas (Journal of Colombian Central Bank), (2004). https://ideas.repec.org/p/col/000094/003224.html#download http://www.banrep.gov.co/docum/ftp/borra280.pdf

(*) publications in refereed journals.