Francisco Vargas

Microsoft Research New England

Google - X

Internship in Alexa Knowledge.

First author in 2 papers at top peer reviewed conferences: - Model Comparison for Semantic Grouping, ICML 2019 http://proceedings.mlr.press/v97/vargas19a.html - Multilingual Factor Analysis, ACL 2019 (Presented as an Oral) http://arxiv.org/abs/1905.05547

Developing numerical methods and combinatorial optimisation algorithms for research in production networks.

Sensorimotor Learning Group, Biological and Computational Learning Lab , Information Engineering Division, Department of Engineering http://www.wolpertlab.com "In the group we use theoretical studies, computer simulations and human experiments to examine the basis of skilled motor behaviour."

Providing senior student support for first year undergraduates joining the university of edinburgh. This involves: -Organizing and managing events for students; -Managing large scale accomodation lead events; -Asisting students with integration problems at the university. All of the above done in a team with usually 4 other resident asistants.

Designing protype lecutre notes and an API for a new course on practical statistics and data analysis aimed at first year undergraduate students. The course notes are developed in an interactive enviroment (ipython notebook) which allow the students to learn theoretical concepts by playing with real data from City of Edinburgh Council's open data resources. Some of the big challenges in this project were the visualizations and infographics used as part of the course material and finding the appropiate data sets to demonstrate them.

During my time at iceRobotics worked on the development of statistical algorithms for the fertility and health monitoring system developed by IceRobotics ltd. I had a great opportunity working on speeding up algorithms plus gained practical expertise in the field of data science.

Focus on stochastic dynamics (Ito Calculus, Stochastic Control).

Dissertation Grade : 91% Overall: 89.6% Courses: - Category Theory - Signal Processing - Probabilistic ML - Advance Topics in ML (Focus on GP driven Differential Equations) - ML for Language Processing - Thesis: Numerical Approaches to the Empirical Schrodinger Bridge Problem Published one of my coursework's (ML for Language Processing) at EMNLP 2020 (Main conference track). Published thesis at Entropy 2021 where it was selected as cover of the september issue. Maria Luisa Sanchez Scholarship Recipient £15000 . Graduate Research Award Recipient £6465.

* 3rd and 4th Year: 1. Probabilistic Modelling and Reasoning; 2. Differential Geometry; 3. Methods of Mathematical Physics; 4. Fourier Analysis and Statistics; 5. Quantum Computing; 6. Machine Learning and Pattern Recognition; 7. Algorithms and Data Structures; 8. Network Analysis; 9. Machine Learning Practical (Deep Learning); 10. Introduction to Theoretical Computer Science; 11. Introduction to Vision and Robotics; 12. System Design Project (Robot Soccer Competition); 13. Compiling Techniques; 14. Software Engineering Large Practical. 15. Data Base Systems. 16. Dissertation in Fourier Analysis and Machine Learning for Time Series; * 2nd Year: 1. Linear Algebra and Several Variable Calculus; 2. Dynamics and Vector Calculus; 3. Discrete Mathematics and Mathematical reasoning; 4. Probability Theory; 5. inf2B: Algorithms and Data Structures / Machine Learning; 6. inf2C: Computer Systems / Software Engineering; 7. Physics of Fields and Matter; 8. Modern Physics;